TYBSC-CS-Wireless-sensor-networks-and-mobile-communication-munotes

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INTRODUCTION
Unit Structure
1.0 Objectives
1.1 Introduction
1.1.1 Introduction to Sensor Networks
1.1.2 Overview of Wireless Sensors Networks
1.2 Unique constraints and challenges.
1.3 Advantage of Sensor Networks
1.3.1 Disadvantages of Senso rs Networks
1.4 Applications of Sensor Networks
1.4.1 Industrial Control and Monitoring
1.4.2 Home Applications
1.4.3 Environmental and Agricultural Monitoring
1.4.4 Military and Security Applications
1.4.5 Asset Tr acking
1.4.6 Heath Monitoring
1.4.7 Application Categories
1.4.8 Major Applications of Sensors networks
1.5 Mobile Ad hoc Networks’ (MANETs) and Wireless Sensor
Networks
1.5.1 Application of MANETs
1.5.2 Characterist ics of MANETs
1.5.3 Difference between MANETs & Wireless Sensors Networks
1.6 Enabling technologies for Wireless Sensor Networks
1.7 List of References
1.8 Conclusion

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2 ABSTRACT
Wireless sensor network is a type of wireless network consist a collection
of tiny device called sensor node. Sensor node has a resource constraint
means battery power, storage and communication capability. These sensor
nodes are set with radio interface with which they communicated with one
another to form a network. Wireless sensor network has very necessary
application like remote has remote environmental monitoring and target
tracking. The goal of our survey is to present a comprehensive review of
the recent literature on various aspects of wirel ess sensor networks and
also discuss how wireless sensor network works and advantages and
disadvantages over the traditional network. Wireless sensor networks are
networks composed of a number of sensor nodes that communicate
wirelessly. It’s utilized over a wide range of applications. This paper looks
at the wireless sensor networks from the applications point of view and
surveyed different application areas where the use of such sensor networks
and their specifications, capabilities.
keywords: wireless sensor networks .
1.0 OBJECTIVES
 In this lesson, you will be introduced for the types of applications for
which wireless sensor networks are intended and a first intuition about
the types of technical solutions that are required, both in hardware and
in netw orking technologies. Also, able to understand the capabilities
and limitations of the nodes in a sensor network and principles options
on how individual sensor nodes can be connected into a wireless
sensor network.
 The objective of this chapter is to provi de an up -to-date treatment of
the fundamental techniques, applications, taxonomy, and challenges of
wireless sensor networks.
 Wireless sensor networks aim to gather environmental data and the
node devices placement may be known or unknown a priori. Network
nodes can have actual or logical communication with all devices; such
a communication defines a topology according to the application.
 communication technologies continue to undergo rapid advancement.
In recent years, there has been a steep growth in research in the area of
wireless sensor networks (WSNs). In WSNs, communication takes
place with the help of spatially distributed, autonomous sensor nodes
equipped to sense specific information. WSNs can be found in a
variety of both military and civilian app lications worldwide. Examples
include detecting enemy intrusion on the battlefield, object tracking,
habitat monitoring, patient monitoring and fire detection. Sensor
networks are emerging as an attractive technology with great promise
for the future. Howe ver, challenges remain to be addressed in issues
relating to coverage and deployment, scalability, quality -of-service,
size, computational power, energy efficiency and security. This paper
presents an overview of the different applications of the wireless
sensor networks and various security related issues in WSNs. in the munotes.in

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3 last two to three years a number of theoretical and/or simulation
studies were done on the topic of object -tracking. while these studies
are useful, they are too general and provide little guidance for the
actual deployment of sensor networks for real - life location -tracking of
an enemy. this thesis focuses on developing an object -tracking
application and prescribes sensor network configurations that work
well with our algorithms. we implem ent our software using crossbow
hardware technology. the major issues addressed in this project are the
evaluation and efficient use of a wireless sensor network product with
no changes, in a real-world application, and efficient ways to
algorithmically analyze the collected raw data from the specific
wireless sensor networks product. although the focus is the
development of a real -world application using wireless sensor
networks, it also provides be a great opportunity to explore the new
area of wireless communication overall.
1.1 INTRODUCTION
1.1.1 Introduction to Wireless Sensors Networks
 wireless sensor network is a wireless network consisting of spatially
distributed autonomous devices that use sensors to monitor physical or
environmental conditions. These a utonomous devices, or nodes,
combine with routers and a gateway to create a typical WSN system.
The distributed measurement nodes communicate wirelessly to a
central gateway, which provides a connection to the wired world
where you can collect, process, an alyze, and present your
measurement data. To extend distance and reliability in a wireless
sensor network, you can use routers to gain an additional
communication link between end nodes and the gateway. Currently,
wireless sensor networks are beginning to be deployed at an
accelerated pace. It is not unreasonable to expect that in 10 -15 years
that the world will be covered with wireless sensor networks with
access to them via the Internet (Figure -1). This can be considered as the
Internet becoming a physica l network. This new technology is exciting
with unlimited potential for numerous application areas including
environmental, medical, military, transportation, entertainment, crisis
management, homeland defense, and smart spaces.
Figure -1 Accessing WSNs through Internet.
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4  The major challenges to be addressed in WSNs are coverage and
deployment, scalability, quality - of- service, size, computational
power, energy efficiency and security. Among these challenges,
security is a major issue in wireless sensor netw orks. Most of the
threats and attacks against security in wireless networks are almost
similar to their wired counterparts while some are exacerbated with the
inclusion of wireless connectivity. In fact, wireless networks are
usually more vulnerable to var ious security threats as the unguided
transmission medium is more susceptible to security attacks than those
of the guided transmission medium. The broadcast nature of the
wireless communication is a simple candidate for eavesdropping. In
this paper we pre sent an overview of the applications and security
issues relating to Wireless Sensor Networks(WSNs).
o Vision of Ambient Intelligence
 The most common form of information processing has happened on
large, general -purpose computational devices, ranging from old-
fashioned mainframes to modern laptops or palmtops. In many
applications, like office applications, these computational devices are
mostly used to process information that is at its core centered around a
human user of a system, but is at best indirectly related to the
physical environment.
 In another class of applications, the physical environment is at the
focus of attention. Computation is used to exert control over physical
processes, for example, when controlling chemical processes in a
factory for c orrect temperature and pressure. Here, the computation is
integrated with the control; it is embedded into a physical system.
Unlike the former class of systems, such embedded systems are
usually not based on human interaction but are rather required to wo rk
without it; they are intimately tied to their control task in the context
of a larger system. Such embedded systems are a well-known and
long-used concept in the engineering sciences (in fact, estimates say
that up to 98% of all computing devices are us ed in an embedded
context. Their impact on everyday life is also continuing to grow at a
quick pace. Rare is the household where embedded computation is not
present to control a washing machine, a video player, or a cell phone.
In such applications, embedd ed systems meet
 human -interaction -based systems. Technological progress is about to
take this spreading of embedded control in our daily lives a step
further. There is a tendency not only to equip larger objects like a
washing machine with embedded computa tion and control, but also
smaller, even dispensable goods like groceries; in addition, living and
working spaces themselves can be endowed with such capabilities.
Eventually, computation will surround us in our daily lives, realizing
a vision of “Ambient Intelligence” where many different devices will
gather and process information from many different sources to both
control physical processes and to interact with human users.
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5  To realize this vision, a crucial aspect is needed in addition to
computation an d control: communication. All these sources of
information have to be able to transfer the information to the place
where it is needed – an actuator or a user – and they should
collaborate in providing as precise a picture of the real world as is
required. For some application scenarios, such networks of sensors and
actuators are easily built using existing, wired networking
technologies. For many other application types, however, the need to
wire together all these entities constitutes a considerable obsta cle to
success: wires constitute a maintenance problem; wires prevent
entities from being mobile; and wires can prevent sensors or actuators
from being close to the phenomenon that they are supposed to control.
Hence, wireless communication between such devices is, in many
application scenarios, an inevitable requirement. Therefore, a new
class of networks has appeared in the last few years: the so - called
Wireless Sensor Network (WSN). These networks consist of
individual nodes that areable to interact wit h their environment by
sensing or controlling physical parameters; these nodes have to
collaborate to fulfill their tasks as, usually, a single node is incapable
of doing so; and they use wireless communication to enable this
collaboration. In essence, the nodes without such a network contain at
least some computation, wireless communication, and sensing or
control functionalities. Despite the fact that these networks also often
include actuators, the term wireless sensor network has become the
commonly accepted name. Sometimes, other names like “wireless
sensor and actuator networks” are also found.
 These WSNs are powerful in that they are amenable to support a lot of
very different real - world applications; they are also a challenging
research and engineer ing problem because of this very flexibility.
Accordingly, there is no single set of requirements that clearly
classifies all WSNs, and there is also not a single technical solution
that encompasses the entire design space. For example, in many WSN
applica tions, individual nodes in the network cannot easily be
connected to a wired power supply but rather have to rely on onboard
batteries. In such an application, the energy efficiency of any
proposed solution is hence a very important
 figure of merit as a long operation time is usually desirable. In other
applications, power supply might not be an issue and hence other
metrics, for example, the accuracy of the delivered results, can
become more important. Also, the acceptable size and costs of an
individual n ode can be relevant in many applications. Closely tied to
the size is often the capacity of an onboard battery; the price often has
a direct bearing on the quality of the node’s sensors, influencing the
accuracy of the result that can be obtained from a si ngle node.
Moreover, the number, price, and potentially low accuracy of
individual nodes is relevant when comparing a distributed system of
many sensor nodes to a more centralized version with fewer, more
expensive nodes of higher accuracy. Simpler but num erous sensors
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6 architecture of a system both simpler and more energy efficient as they
facilitate distributed sampling – detecting objects, for example,
requires a distributed system.
 Realizing such wireless sensor networks is a crucial step toward a
deeply penetrating Ambient Intelligence concept as they provide,
figuratively, the “last 100 meters” of pervasive control. To realize
them, a better understanding of their potential applications and the
ensuing requirements is necessary, as is an idea of the enabling
technologies. These questions are answered in the following sections;
a juxtaposition of wireless sensor networks and related networking
concepts such as fieldbuses or mobile ad hoc network is provided as
well.
1.1.2 Overview of Wireless Sensor Networks
 The progress in wireless communications, digital electronics, and
micro systems has enabled the development of small -size, low-cost,
power -efficient multifunctional sensors. Moore’s law predicts a gre at
future for this technological field. In the future the typical sensor nodes
the size “of a 35 mm film canister” (Wikipedia, Wireless Sensor
Network Webpage, 2005), and their development cost will be
drastically reduced, generating an explosion in the wireless sensor
network usage.
 Wireless sensor networks (WSN) is a rich domain that involves
both hardware and system design. It consists of sensor devices that are
“small in size and able to sense, process data, and communicate with
each other, typically over an RF (radio frequency)
 Channel” (Haenggi, 2005). Their purpose is to collect and process data
from the environment, produce a detection event and then forward the
information to a specific destination.
 Wireless sensor networks are a specialization of the wireless ad-hoc
mesh networks. They inherit all the ad -hoc and mesh characteristics
described above. They are wireless self-organizing, self-healing, and
adaptive networks. They contain a large number of small, inexpensive,
low-power nodes and use speci alized communication techniques and
routing, like “an asymmetric many -to-one data flow” (Carle &
Simplot - Ryl, 2004) to communicate. Nodes’ characteristics (size,
lifetime, computational power), system’s architecture, and protocols
enable WSN to be deeply embedded into the environment. If these
capabilities will be combined with the Internet, an “embedded
Internet” (Culler & Hong, 2004) will be produced. Zhao and Guibas
(2004) accent that “sensor networks extend the existing Internet deep
into the physical environment. The resulting network is orders of
magnitude more expansive and dynamic than the current TCP/IP
network.” Figure 3 provides an illustration of a sensor network and
Internet integration. A complete WSN implementation is a
“macroscopic view” (Ca rle & Simplot - Ryl, 2004) of the
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7 observe and interact with physical phenomena in real time at a fidelity
that was previously unobtainable.” (Carle, & Simplot -Ryl, [2004]).
WSN is a new, intere sting, and active research area; it introduces
various challenges and concerns; the following section highlights some
of them.
 Figure 3. Integration of a Wireless Sensor Network and the
Internet (Zhao & Guibas, 2004).
1.2 UNIQUE CONSTRAINTS AND CHALLENGES
 Self-organizing capabilities
 Short -range broadcast communication and multi -hop routing Dense
deployment and cooperative effort of sensor nodes Frequently
changing topology due to fading and node failures
 Limitations in energy, transmitted power, memory, and computing
power.
 They also highlights that the WSN differ from the wireless ad -hoc
mesh networks in the latter three characteristics. Zhao and Guibas
(2004) identify “limited hardware,” “limited support for networking,”
and “limited support for software deve lopment” as general WSN
design and implementation challenges. Wang, Hassanein, and Xu
(2005) add “data redundancy,” the diversity of the possible
application, and security and privacy concerns. Before some of the
above concerns are analyzed further, we wil l discuss in the following
section the important WSN applications that set the requirements and
drove a WSN development.
 Unlike a centralized system, a sensor network is subject to a unique
 Set of resource constraints such as finite on-board battery power
 And limited network communication bandwidth. In a typical sensor
 Network, each sensor node operates untethered and has a micropro -
 Cessor and a small amount of memory for signal processing and task
 Scheduling. Each node is also equipped with one or more sensing
 Devices such as acoustic microphone arrays, video or still cameras,
 Infrared (IR), seismic, or magnetic sensors. Each sensor node com-
 Municates wirelessly with a few other local nodes within its radio
 Communication range.
 Sensor networks extend the existing Internet deep into the physi -
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8  More expansive and dynamic than the current TCP/IP network and
 Is creating entirely new types of traffic that are quite different from
 What one finds on the Internet now. Information collected by and
 Transmitted on a sensor network describes conditions of physical
 Environments —for example, temperature, humidity, or vibration —
 And requires advanced query interfaces and search engines to effec -
 Tively support user-level functions. Sensor networks may inter-
 Network with an IP core network via a number of gateways, as in
 Figure 1.1. A gateway routes user queries or commands to appropriate
 Nodes in a sensor network. It also routes sensor data, at times aggre -
 Gated and summarized, to users who have requested it or are expected
 To utilize the information. A data repository or storage service may
 Be present at the gateway, in addition to data logging at each sensor.
 Advantages of Sensor Networks
 Networked sensing offers unique advantages over traditional cen-
 tralized approaches. Dense networks of distributed communicating
 sensors can improve signal -to-noise ratio (SNR) by reducing average
distances from sensor to
 source of signal, or target. Increased
 energy efficiency in communications is enabled by the multihop
 topology of the network [184]. Moreover, additional relevant
information from other sensors can be aggregated during this multihop
transmission through in-network processing [104]. But perhaps the
 greatest advantages of networked sensing are in improved robustness
1.3 ADVANTAGES OF SENSOR NETWORKS
1. It is scalable and hence can accommodate any ne w nodes or devices at
any time.
2. It is flexible and hen ce open to physical partitions.
3. All the WSN nodes can b e accessed throug h centralized montoring
system.
4. As it is wireless in nature, it does not require wires or cables. Refer
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9 5. Wireless can be applied on large scale and in various domains such as
mines, healthcare, surveillance, agriculture etc.
6. It uses different security algorithms as per underlying wireless
technologies and hence provide reliable network for c onsumers or
users.
1.3.1 Disadvantages of WSN (wireless sensors networks)
Following are the drawbacks or disadvantages of WSN :
1. As it is wireless in nature, it is prone to hacking by hackers.
2. It cannot be used for high -speed communication as it is desig ned for
low-speed applications.
3. It is expensive to build such network and hence cannot be affordable
by all.
4. There are various challenges to be considered in WSN such as energy
efficiency, limited bandwidth, node costs, deployment model,
Software/hardwar e design constraints and so on.
5. In star topology based WSN, failure of central node leads to whole
network shutdown.
1.4 APPLICATIONS OF SENSOR NETWORKS
1.4.1 Industrial Control and Monitoring
The deployment of wireless network sensors in the industrial control -and-
monitoring field seems very prominent. Normally, a factory has a control
room to monitor and control the state of the plant and the condition of the
equipment. Specific critical values, like temperature or pressure, are
collected from the plant or the equipment. The values describe the plant’s
or the equipment’s condition, which is then forwarded to the control room
where it is evaluated. Traditionally, industrial control and monitoring
requires the deployment of a complex, expensive wired netwo rk. Sensor
networks can replace the wired network, providing reliable data transfer
and reducing the initial deployment and maintenance cost.
Lighting, ventilation and air -conditioning are other possible areas for
wireless sensors. WSN provide the flexibil ity to support dynamic
changes in the environment. This is also enhanced by the WSN
programming feature, which offers secure and balanced services (e.g.,
balanced heating and air conditioning). When used to control and monitor
complex equipment like robots , or other rotating and moving equipment,
WSN provide the necessary flexibility. Thus, the system’s reliability
is increased, because damage caused by the machinery’s movement is
avoided. In addition, small -size sensing nodes can be used where wired
implem entations are impossible.
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10 1.4.2 Home Applications
Home automation is another large application area for wireless sensor
networks. The uses in the industrial applications field described above
also apply to home implmentations. Centralized control of home
appliances has already been implemented by using wired solutions or
other wireless technology solutions. Their replacement by a wireless
sensor network provides a development and maintenance cost reduction,
system flexibility, and stretch ability. WS N also provides total, and secure
control of the home devices. Another area for the use of WSN that is
relevant to home application is the toy industry, a large market. The nature
of wireless networks enable toys to behave in complex and logical ways at
a reasonable cost..
1.4.3 Environmental and Agricultural Monitoring
environmental monitoring of WSN implementations as pioneers in this
technology. Wireless networks can be used for habitat monitoring and
ecosystem measurements. Haenggi (2004) finds that seismic activity,
forest fire, floods and water quality also can be detected and localized by
the use of WSNs. Culler and Hong (2004) claim that the outdoor
deployment, low power operation, fault tolerance, data quality, and
networking characteristics o f WSNs are ideal for environmental
applications. Moreover, given those characteristics, WSNs can be used for
agricultural purposes. Better knowledge of the agricultural environment
enables the more precise control of fertilizers, water management cost
reduction, quality maximization and environment protection.
1.4.4 Military and Security Applications
As with almost any new technology military and security application are
recommended uses for wireless sensor networks. WSNs can assist or
replace quards around a building or camp perimeter. Target localization
and identification is another potential use, whereby friendly troops use
WSNs to identify themselves (Callaway, 2004). Haenggi (2005) finds that
such implementation can improve “military command, control,
communication and computing (C4)” schema. Additionally, he describes
an application for “surveillance and battle -space monitoring” in which the
proper sensors are deployed in the ground or are carried by unmanned
vehicles to monitor opposing forces. Haengg i (2005) mentions other
potential uses in an “urban warfare” field: “to prevent reoccupation” of
buildings that have already been cleared; and for “self -healing
minefields,” where, instead of a “static complex obstacle,” the WSNs
provide “an intelligent, d ynamic obstacle that senses related positions and
responds to an enemy breaching attempt by physical reorganization.”
1.4.5 Asset Tracking
Among the potential uses of wireless sensor networks, asset tracking
is also a large area of interest for military an d commercial application.
Calllaway (2004) describes a possible use: for tracking “shipping
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11 container, it and its content become recognizable from a distance. An
exact knowledge of the container’s type and position can save handlers
a great amount of time by preventing unnecessary errors. The WSNs
provide a cost- effective way to increase the “shipper’s productivity.”
1.4.6 Heath Monitoring
identifies two different wireless sensor network medical applications that
are expected to rapidly increase. First, he mentions “medical sensing” in
which data such as “body temperature, blood pressure, and pulse,”
collected from the system, can be transmitted to a local or remote
computer for heal th monitoring uses. Additionally, WSNs can be used in
the “micro -surgery” field, where tiny medical instruments are used to
perform “ microscopic and minimal invasive surgery.”
Application Categories
The above applications show that, among the WSN applicat ions, there are
some common features. Holger and Willig (2005) identify the existence of
data “sources” and “sinks” in most of the WSN applications in which the
“sources” are the nodes that sense the data from the environment and the
“sinks” are the nodes where the data arrived, like gateways. The “sinks”
can be WSN components or they can sit outside the system. Holger and
Willig (2005) place the applications based on the sources -and-sinks
interaction in four categories. The first category is “event detecti on,” is
which the sources, when they detect an event send messages to the sinks.
An event could be a single value, for example, an above threshold
humidity, or a complicated type. Holger and Willig’s second category is
“periodic measurements,” in which the sources periodically send messages
to the sinks. The third category comprises “function approximation and
edge detection” in which the WSN system, based on specific finite values,
approximates an “unknown function.” The final category is “tracking” in
which the event producer is mobile, and thus a WSN is used to detect the
object’s position and possibly its speed and direction.
The preceding section included categories and possible implementations
of wireless sensor networks. According to Haenggi (2005), t he
opportunities for the WSNs are “ubiquitous.” Zhao and Guibas (2004) find
that “the main long -term will be the increase in the number of sensors per
application and the increase in the decentralization of sensor control and
processing.” However, the relevant constraints and challenges, that
are mentioned above will be further analyzed in the next sections. They
must be addressed for easier and faster deployment of the wireless sensor
network applications.
 The major applications of WSNs
 Application of WSNs

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12 1. Logistics
Logistics is a multi -player business which has changed significantly in the
last decade. E.g. transport of food. Figure 9 shows one application
scenario, where wireless sensor net work nodes are connected to goods
(mostly food because of their perishable nature). The goods are loaded
from a storehouse or warehouse to a good carrier vehicle, in which their
nodes need to be self -organize and form a network of nodes, which can
forward information of the goods’ from one state to the outside world
using a gateway (e.g. a telematics unit).
Logistics benefits clearly from Wireless Sensor Networks. However, the
requirements of logistics for applicable WSNs are challenging.
2. Environmental moni toring
Simple computations and to send/receive data performance done by the
sensor nodes. These nodes are small in size and are embedded into
devices. Data collection is the typical usage where data collected from the
surrounding environment via sensors. E nvironment monitoring has
become an important field of control and protection, providing real -time
systems and control communication with the physical world. During data
collection sensor nodes
Monitor and manage air quality,
Monitor and manage conditions of traffic,
Monitor and manage weather situations.
Characteristics of an environmental monitoring system
Autonomy. Batteries must be able to power the weather stations during the
whole deployment.
Reliability. The network has to perform simple and predicta ble operations,
to prevent unexpected crashes.
Robustness. The network must account for a lot of problems such as poor
radio connectivity (e.g., in case of snow fall) or hardware failures.
Flexibility. One must be able to quickly add, move, or remove stati ons at
any time depending on the needs of the applications.
3. Industrial supervision
The advances in wireless communication, microelectronics, digital
electronics, and highly integrated electronics and the increasing need for
more efficient controlled ele ctric systems make the development of
monitoring and supervisory control tools the object of study of many
researchers.
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13 4. Intelligent buildings
Wireless Sensor Networks (WSN) has become cardinal towards the
implementation of smart homes, and they are pro ved to be a permitting
technology for assisted living. WSNs are deemed appropriate for
placement in home environments for diverse applications.
Military applications
WSNs consist of a large number of small sensor nodes. Costing of small
nodes is also less expensive. In military operations, there is always a threat
or security challenges of being attacked by enemies. So if regular use of
small nodes which is less expensive help to reduce the loss.

Figure 9.
Wireless sensor network for logistics.
Figure 10 shows wireless sensor networks for military application. This
application provides suitable sensors which can be used in top secret
missions. These sensors can detect, identify and classify threads based on
the count, number, whether it is armored vehicles or men in foot, type and
amount of weapons they carry, etc., can be detected in advance. This
application provides reliable real time war pictures and better situational
awareness.

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14 Wireless sensor network for military application.
TYPES OF APPLICATIONS (Cont. - Types of wireless sensor networks
through)
Many of these applications share some basic characteristics. In most of
them, there is a clear difference between sources of data – the actual
nodes that sense data – and sinks – nodes where the data should be
delivered to. These sinks sometimes are part of the sensor network itself;
sometimes they are clearly systems “outside” the network (e.g. the
firefighter’s PDA communicating with a WSN). Also, there are usually,
but not always, more sources than sinks and the sink is oblivious or not
interested in the identity of the sources; the data itself is much more
important.
The interaction patterns between sources and sinks show some typical
patterns. The most relevant ones are:
Event detection Sensor nodes should report to the sink(s) once they have
detected the occurrence of a specified event. The simplest events can be
detected locally by a single sensor node in isolation (e.g. a te mperature
threshold is exceeded); more complicated types of events require the
collaboration of nearby or even remote sensors to decide whether a
(composite) event has occurred (e.g. a temperature gradient becomes too
steep). If several different events can occur, event classification might be
an additional issue.
Periodic measurements Sensors can be tasked with periodically reporting
measured values. Often, these reports can be triggered by a detected event;
the reporting period is application dependent.
Function approximation and edge detection The way a physical value like
temperature changes from one place to another can be regarded as a
function of location. A WSN can be used to approximate this unknown
function (to extract its spatial characteristics), using a limited number of
samples taken at each individual sensor node. This approximate
mapping should be made available at the sink. How and when to update
this mapping depends on the application’s needs, as do the approximation
accuracy and the inherent trade -off against energy consumption.
Similarly, a relevant problem can be to find areas or points of the same
given value. An example is to find the isothermal points in a forest fire
application to detect the border of the actual fire. This can be generalized
to finding “edges” in such functions or to sending messages along the
boundaries of patterns in both space and/or time.
Tracking The source of an event can be mobile (e.g. an intruder in
surveillance scenarios). The WSN can be used to report updates on the
event source’s position to the sink(s), potentially with estimates about
speed and direction as well. To do so, typically sensor nodes have to
cooperate before updates can be reported to the sink. These interactions
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15 given time span, only from certain areas, and so on). These requirements
can also change dynamically overtime; sinks have to have a means to
inform the sensors of their requirements at runtime. Moreover, these
interactions can take place only for one specific request of a sink (so-
called “one -shot queries”), or they could be long-lasting relationships
between many sensors and many sinks.
The examples also have shown a wide diversity in deployment options.
They r ange from well planned, fixed deployment of sensor nodes (e.g. in
machinery maintenance applications) to random deployment by dropping
a large number of nodes from an aircraft over a forest fire. In addition,
sensor nodes can be mobile themselves and compe nsate for shortcomings
in the deployment process by moving, in a post deployment phase, to
positions such that their sensing tasks can be better fulfilled. They could
also be mobile because they are attached to other objects (in the logistics
applications, for example) and the network has to adapt itself to the
location of nodes.
Closely related to the maintenance options are the options for energy
supply. In some applications, wired power supply is possible and the
question is mute. For self-sustained sensor nodes, depending on the
required mission time, energy supply can be trivial (applications with a
few days of usage only) or a challenging research problem, especially
when no maintenance is possible but nodes have to work for years.
Obviously, acceptabl e price and size per node play a crucial role in
designing energy supply.
1.5 MOBILE ADHOC NETWORKS (MANETS) AND
WIRELESS SENSOR NETWORKS
Mobile Adhoc Network (MANETs)
o A MANET consists of a number of mobile devices that come together
to form a netw ork as needed, without any support from any existing
internet infrastructure or any other kind of fixed stations.
o A MANET can be defined as an autonomous system of nodes or
MSs(also serving as routers) connected by wireless links, the union of
which forms a communication network modeled in the form of an
arbitrary communication graph.
o This is in contrast to the well -known single hop cellular network
model that supports the needs of wireless communication between two
mobile nodes relies on the wired backbone and fixed base stations.
o In a MANET, no such infrastructure exists and network topology may
be changed dynamically in an unpredictable manner since nodes are
free to move and each node has limiting transmitting power, restricting
access to the node only i n the neighboring range.
o MANETs are basically peer -to-peer, multi -hop wireless networks in
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16 manner from a source to an arbitrary destination, via intermediate
nodes as given in the figure:

o As nodes move, the connectivity may change based on relative
locations of other nodes. The resulting change in the network topology
known at the local level must be passed on to other nodes so that old
topology information can be updated.
o For example, as MS 2 in the figure changes its point of attachment
from MS3 to MS4, other nodes that are part of the network should use
this new route to forward packets to MS2. In the figure, we assume
that it is not possible to have all nodes within each other's radio rang e.
In case all nodes are closed by within each other's radio range, there
are no routing issues to be addressed.
o In figures raise another issue, that of symmetric and asymmetric
(bidirectional) and asymmetric (unidirectional) links. Consider
symmetric link s with associative radio range; for example, if MS1 is
within radio range of MS3, then MS3 is also within radio range of
MS1. The communication links are symmetric. This assumption is not
always valid because of differences in transmitting power levels and
the terrain. Routing in asymmetric networks is relatively hard task. In
certain cases, it is possible to find routes that exclude asymmetric
links, since it is cumbersome to find the return path. The issue of
efficient is one of the several challenges enc ountered in a MANET.
o The other issue is varying the mobility patterns of different nodes.
Some other nodes are highly mobile, while others are primarily
stationary. It is difficult to predict a node's movement and direction of
movement and numerous studies have been performed to evaluate
their performance using different simulators.
Characteristics of MANET
Some characteristics of adhoc network are as follows:
o Dynamic topologies: nodes are free to move arbitrarily; thus the
network topology may be changed r andomly and unpredictably and
primarily consists of bidirectional links. In some cases where the
transmission power of two nodes is different, a unidirectional link may
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17 o Bandwidth -constrained and variable capacity links: wireless links
continue to ha ve significantly lower capacity than infrastructure
networks.
o Energy -constrained operation: some or all of the MSs in a MANET
may rely on batteries or other exhaustible means for their energy. For
these nodes or devices, the most important system design op timization
criteria may be energy conservation.
o Limited physical security: MANETs are generally more prone to
physical security threats than wire line networks. The increased
possibility of eavesdropping, spoofing, and denial of services (DoS)
attacks shou ld be considered carefully. To reduce security threats,
many existing link security techniques are often applied within
wireless networks.
Applications of MANET
Some specific applications of ad hoc networks include industrial and
commercial applications in volving cooperative mobile data exchange.
There are many existing and future military networking requirements for
robust, IP -compliant data services within mobile wireless communication
networks, with many of these networks consist of highly dynamic
autono mous topology segments. Advanced features of Mobile ad hoc
networks, including data rates compatible with multimedia applications
global roaming capability, and coordination with other network structures
are enabling new applications.
o Defense applications: Many defense applications require on the fly
communications set -up, and ad hoc/sensor networks are excellent
candidates for use in battlefield management.
o Crisis management applications: These arise, for example, as a
result of natural disasters in which the entire communication
infrastructure is in disarray. Restoring communications quickly is
essential.
o Telemedicine: The paramedic assisting the victim of a traffic accident
in a remote location must access medical records (e.g. X -rays) and
may need video conference assistance from a surgeon for an
emergency intervention. In fact, the paramedic may need to
instantaneously relay back to the hospital the victim's X -rays and other
diagnostic tests from the site of the accident.
o Tele-geoprocessing application: The combination of GPS, GIS
(Geographical Information Systems), and high -capacity wireless
mobile systems enables a new type of application referred to as tele -
geo processing.
o Virtual Navigation: A remote database contains the graphical
representation of building, streets, and physical characteristics of a
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18 buildings, including an emergency rescue plan, or find possible points
of interest.
o Education via the internet: educational opportun ities available on the
internet or remote areas because of the economic infeasibility of
providing expensive last -mile wire line internet access in these areas to
all subscribers.
o Vehicular area network: This a growing and very useful application
of adhoc network in providing emergency services and other
information. This is equally effective in both urban and rural setup.
The basic and exchange necessary data that is beneficial in a given
situation.
wireless sensor networks
Wireless sensor networks (WSNs) have the power of distributed
communication, computing, and sensing features. They are characterized
as infrastructure less, fault tolerant and self -organizing networks which
provide opportunities for low -cost, easy -to-apply, rapid and flexible
installatio ns in an environment for various applications
The wireless sensor and the sensor node architecture are given in the
diagram below -

Characteristics of WSN
The characteristics of WSN are as follows −
 Resource constraints − Nodes of WSN are smaller in size and get
power from the batteries. It justifies that service provided by the nodes
like communication and computation amount of memory is very
limited.
 Commu nication paradigm − The data centric feature of WSN
explains its data centric nature and justifies that the communication is
restricted to nodes.
 Application specific design − WSN is application specific i.e. the
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Introduction
19  Node failure and unreliable communication − Various factors like
harsh operating conditions leading to instability, unpredictability, nodal
mobility, environmental interferences makes typical WSN nodes to be
error -prone.
 Scalability and density − The num ber of nodes in WSNs may be large
and densely deployed to a higher degree in various applications.
 Dynamic Topologies − Nodes are free to travel randomly at different
speeds in few applications and sometimes may fail to operate, to add or
to replace. So th ere can be different network topology.
 Communication models − WSNs use different communication models
− Flat/ hierarchical /distributed WSNs; or homogeneous/ heterogeneous
WSNs.
Operating Environment
The WSNs are mostly deployed in remote and hazardous loc ations for
unattended operations because of their ability to withstand harsh
environmental conditions.
Requirements of WSN
The requirements of WSN are explained below:
 Flexibility − The architecture of WSN is not fixed. Rather it varies
from application to application which justifies that the protocols and
algorithms have the characteristics of self -organization.
 Fault tolerance − The nodes in WSNs have the capability to sustain
the functions carried out in the network even in situations like limited
batter y power, interference from external sources, failure rate of nodes,
harsh environmental conditions.
 Lifetime − The two major factors that should be taken into
consideration are load balancing and energy saving. These two factors
can enhance the lifetime of the WSN architecture as long as possible.
 Scalability − The number of nodes in a WSN network can be large.
Accordingly WSN architecture and protocols should be designed.
 Real -time − The Various capabilities like sensing, processing and
communication of WS N are used in various real -world problems so
should follow stringent time.
 Security − For example in health care data and military data, the data
offered by WSN network are private which are sensitive in nature. So
security is evident in such architectures .
 Production cost − The cost of nodes in WSN network has to be low as
once the nodes run out of the energy it has to be replaced by newer
nodes.
 Deployment − In large -scale WSNs, there is random deployment of
nodes whose maintenance and replacements are no t practically
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20  Dependability − One can rely on WSN as the architectural design is
robust that leads to secure collection of data and reliable delivery with
no loss.
A MANET is a mobile ad -hoc network that contains wireless links and
nodes. It is an infrastructure -less network, and it can change its topology
and configure itself on the fly, it can communicate via multiple hops.
Whereas a Wireless Sensor Network (WSN) is a set of spatially
distributed and dedicated sensors that are interlinked via the wireless
medium for monitoring and recording the physical conditions of the
environment and organizing the collected data at a central location.
Let’s look at the similarities between MANET and WSN
1. Both are infrastructure -less, distributed wireless networks
2. Routing Techniques are more or less the same
3. Both are Ad -hoc networks
4. Topology can change over a period
5. Nodes can be operated on a battery
6. Both wireless channels use unlicensed spec trum (cause of interference)
What makes them different?
1. The data rate of MANETs is more than WSN
2. The number of nodes in the WSN is more than MANETs
3. Mobility is very high in MANETs(since nodes are less) than WSN
4. Sensor nodes of WSN are generally static and cooperate together to
transfer the sensed data
5. Sensor nodes usually consume less energy than MANET’s nodes
6. MANETs are usually close to civilization
7. Public -key cryptography is used in MANETs whereas symmetric key
cryptography used in WSNs for security purpo ses
8. Compared to MANETs, WSNs are smaller, more powerful, and more
memory -constrained
9. Mostly, MANETs are used for distributed computing whereas WSNs
are used for information gathering from the environment
10. WSNs are more prone to failures than MANETs.
1.6 ENABLING TECHNOLOGIES FOR WIRELESS
SENSOR NETWORKS
Building such wireless sensor networks has only become possible with
some fundamental advances in enabling technologies.
First technology is the miniaturization of hardware. Smaller feature sizes
in chips h ave driven down the power consumption of the basic
components of a sensor node to a level that the constructions of WSNs
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Introduction
21 memory chips and the radio modems which are responsible for wirel ess
communication have become much more energy efficient. Reduced chip
size and improved energy efficiency is accompanied by reduced cost.

Figure 1.2: Enabling Technologies
Second one is processing and communication and the actual sensing
equipment is th e third relevant technology. Here, however, it is difficult to
generalize because of the vast range of possible sensors.
These three basic parts of a sensor node have to accompanied by power
supply. This requires, depending on application, high -capacity ba tteries
that last for long times, that is, have only a negligible self - discharge rate,
and that can efficiently provide small amounts of current. Ideally, a sensor
node also has a device for energy scavenging, recharging the battery with
energy gathered f rom the environment – solar cells or vibration -based
power generation are conceivable options. Such a concept requires the
battery to be efficiently chargeable with smallamounts of current, which is
not a standard ability. Both batteries and energy scaveng ing are still
objects of ongoing research.
The counterpart to the basic hardware technologies is software. This
software architecture on a single node has to be extended to a network
architecture, where the division of tasks between nodes, not only on a
single node, becomes the relevant question -for example, how to structure
interfaces for application programmers. The third part to solve then is the
question of how to design appropriate communication protocols.
Building such wireless sensor networks has onl y become possible with
some fundamental advances in enabling technologies. First and foremost
among these technologies is the miniaturization of hardware. Smaller
feature sizes in chips have driven down the power consumption of the
basic components of a se nsor node to a level that the constructions of
WSNs can be contemplated. This is particularly relevant to
microcontrollers and memory chips as such, but also, the radio modems,
responsible for wireless communication, have become much more energy
efficient. Reduced chip size and improved energy efficiency is
accompanied by reduced cost, which is necessary to make redundant
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22 Next to processing and communication, the actual sensing equipment is
the third relevant technology.
Thes e three basic parts of a sensor node have to accompanied by power
supply. This requires, depending on application, high -capacity batteries
that last for long times, that is, have only a negligible self-discharge rate,
and that can efficiently provide small amounts of current. Ideally, a
sensor node also has a device for energy scavenging, recharging the
battery with energy gathered from the environment – solar cells or
vibration -based power generation are conceivable options. Such a concept
requires the battery to be efficiently chargeable with small amounts of
current, which is not a standard ability. Both batteries and energy
scavenging are still objects of ongoing research.
The counterpart to the basic hardware technologies is software. The first
question to answer here is the principal division of tasks and
functionalities in a single node – the architecture of the operating system
or runtime environment. This environment has to support simple
retasking, cross -layer information exchange, and modularity to allow for
simple maintenance. This software architecture on a single node has to be
extended to a network architecture, where the division of tasks between
nodes, not only on a single node, becomes the relevant question – for
example, how to structure interfaces for application programmers. The
third part to solve then is the question of how to design appropriate
communication protocols.
1.7 LIST AND REFERENCES
 Wireless Sensor Networks Technology, Protocols, and Applications ,
Kazem Sohraby, Daniel Minoli and TaiebZnati, John Wiley & Sons,
2007.
 Fundamentals of Wireless Sensor Networks, Theory and Practice,
Waltenegus Dargie, Christian Poellabauer , Wiley Series on wireless
Communication and Mobile Computing, 2011
 Internet Resources
1.8 CONCLUSION
 WSN follows different topologies such as star, tree, mesh, hybrid etc.
Hence one can understand pros and cons of these topologies to derive
advantages of WSN and disadvantages of WSN. Moreover WSN uses
different underlying wireless t echnologies. Hence one can also refer
advantages and disadvantages of Zigbee , Z-wave , WiFi , and WiFi6 et.
 Each such sensor network node typically has many parts: a radio
transceiver with an internal antenna or connection to an external
antenna, a microcontroller, an electronic circuit for interfacing with the
sensors and an energy source, usually a b attery or an embedded form
of energy harvesting.
 A sensor node might vary in size and size can be a size of a grain of
dust. munotes.in

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23  Sensor collects the analog data from the physical world and an ADC
converts this data to digital data.
 When a large number of senso r nodes are deployed in a large area to
cooperatively monitor a physical environment, the networking of these
sensor node is equally important
 In flat architecture, the base station sends commands to all the sensor
nodes.
 In hierarchical architecture, a gr oup of sensor nodes are formed as a
cluster and the sensor nodes transmit data to corresponding cluster
heads.
 Wireless sensor network mainly consists of sensor nodes. A wireless
sensor network consists of many different components.
 The static parts would be connected to the constant power supply, so
that wireless parts can use low power to communicate to them and also
nodes can go in the standby mode from time to time.
 A dynamic maintenance approach works as an ‘on -the-fly’-based
triggering technique that creates a new topology when the current one
is no longer optimal.
 MANET stands for Mobile ad -hoc Network also called as wireless ad
hoc network or ad hoc wireless network that usually has a routable
networking environment on top of a Link Layer ad hoc netw ork.
 The key to achieving a longer lifetime for WSN is to design wireless
sensor networks that minimize power consumption of wireless sensor
devices, hence the name “low power”.
 Challenges in wireless sensor node in various ways for an application.
 The maj or applications of WSNs
 The goods are loaded from a warehouse to a freight vehicle.
 Their typical usage is to gather information about their environment
via sensors, to potentially pre -process these data, and to finally
transmit them.
 Characteristics of an environmental monitoring system

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24 2
SENSOR NODE HARDWARE AND
NETWORK ARCHITECTURE
Unit Structure
2.0 Objectives
2.1 Sensor Node Hardware and Network Architecture
2.1.1 Key Definitions of Sensor Networks
2.2 Single -node Architecture
2.2.1 Hardware Components & design Constraints
2.2.2 Hardware Components
2.2.3 Controller
2.2.4 Memory
2.2.5 Communication Devices
2.2.6 Sensors & Actuators
2.2.7 Power Supply
2.3 Operating Systems and Execution Environment
2.4 Introduction to TinyOS and nesC.
2.5 Network architecture
2.5.1 Sensor Netw orks Scenario
2.5.2 Types of sources and sinks
2.5.3 Single -hop versus multi -hop networks
2.5.4 Multiple sinks and sources
2.6 Optimization goals and figures of merit
2.6.1 Quality of service
2.6.2 Energy efficiency
2.7 Design principles for WSNs
2.7.1 Distributed Organization
2.7.2 In Network Processing Techniques
2.7.3 Adaptive Fidelity & Accuracy
2.7.4 Data Eccentricity
2.7.5 Exploit Local Information
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25 2.7.7 Exploit Heterogeneity
2.7.8 Component Based Protocol Stacks
2.7.9 Service interfaces of WSNs
2.7.10 Gateway concepts.
2.8 List of References
2.9 Summary
2.10 Unit End Exercises

2.0 OBJECTIVES
In WSN, the main task of a sensor node is to sense data and sends it to the
base station in multi hop environment for which routing path is essential.
For computing the routing path from the source node to the base station
there is huge numbers of proposed routing protocols exist (Sharma et al.,
2011).
Currently, WSN (Wireless Sensor Network) is the most standard services
employed in commercial and industrial applications, because of its
technical development in a processor, communication, and low -power
usage of embedded computing devices. The wireless sensor network
architecture is built with nodes that are used to observe the surroundings
like temperature, humidity, pressure, position, vibration, sound, etc. These
nodes can be used in various real -time applications to perform various
tasks like smart detecting, a discovery of neighbor nodes, data processing
and storage, data collection, target tracking, monitor and controlling,
synchronization, node localization, and effective routing between the base
station and nodes. Presently, WSNs are beginning to be organized in an
enhanced step. It is not awkward to expect that in 10 to 15 years that the
world will be protected with WSNs with entree to them via the Internet.
This can be measured as the Internet becoming a physical n/w. This
technolog y is thrilling with infinite potential for many application areas
like medical, environmental, transportation, military, entertainment,
homeland defense, crisis management, and also smart spaces.
2.1 SENSOR NODE HARDWARE AND NETWORK
ARCHITECTURE
2.1.1 K ey Definitions Of Sensor Networks :
Definition: A Sensor Network is composed of a large number of sensor
nodes, which are tightly positioned either inside the phenomenon or very
close to it.
Sensor networks have the contribution from signal processing, net working
and protocols, databases and information management, distributed
algorithms, and embedded systems and architecture.
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26 A wireless sensor network (WSN) can be defined as a network of low -size
and low -complex devices denoted as nodes that can sense the environment
and communicate the information gathered from the monitored field
through wireless links.
The following are the Key terms and concepts that will be used in sensor
network development techniques.
• Sensor: A transducer that converts a physical ph enomenon such as
heat, light, sound, or motion into electrical or other signals that may be
further operated by other apparatus.
• Sensor node: A basic unit in a sensor network, with on -board sensors,
processor, memory, wireless modem, and power supply. It i s often
abbreviated as node. When a node has only a single sensor on board,
the node is sometimes referred as a sensor.
• Network topology: A connectivity graph where nodes are sensor nodes
and edges are communication links. In a wireless network, the link
represents a one -hop connection, and the neighbors of a node are those
within the radio range of the node.
• Routing: The process of determining a network path from a packet
source node to its destination.
• Date -centric: Approaches that name, route, or access a piece of
data via properties, such as physical location, that are external to a
communication network. This is to be contrasted with addresscentric
approaches which use logical properties of nodes related to the network
structure.
• Geographic routing: Routing of data based on geographical features
such as locations or regions. This is an example of datecentric
networking.
• In-network: A style of processing in which the data is processed and
combined near where the data is generated.
• Collaborative processing : Sensors cooperatively processing data from
multiple sources in order to serve a high-level task. This typically
requires communication among a set of nodes.
• State: A snapshot about a physical environment (e.g., the number of
signal sources, their locatio ns or spatial extent, speed of movement), or
a snapshot of the system itself (e.g.,the network state).
• Uncertainty: A condition of the information caused by noise in sensor
measurements, or lack of knowledge in models. The uncertainty affects
the system‘s ability to estimate the state accurately and must be
carefully modeled. Because of the ubiquity of uncertainty in the data,
many sensor network estimation problems are cast in a statistical
framework. For example, one may use a covariance matrix to
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27 probability distributions for non-Gaussian processes.Task: Either high -
level system tasks which may include sensing, communication,
processing, and resource allocation, or application tasks which may
include detection, classification, localization, or tracking.
• Detection: The process of discovering the existence of a physical
phenomenon. A threshold - based detector may flag a detection
whenever the signature of a physical phenomenon is determi ned to be
significant enough compared with the threshold.
• Classification: The assignment of class labels to a set of physical
phenomena being observed.
• Localization and tracking: The estimation of the state of a physical
entity such as a physical phenomeno n or a sensor node from a set of
measurements. Tracking produces a series of estimates over time.
• Value of information or information utility: A mapping of data to a
scalar number, in the context of the overall system task and knowledge.
For example, infor mation utility of a piece of sensor data may be
characterized by its relevance to an estimation task at hand and
computed by a mutual information function.
• Resource: Resources include sensors, communication links,
processors, on -board memory, and node ener gy reserves. Resource
allocation assigns resources to tasks, typically optimizing some
performance objective.
• Sensor tasking: The assignment of sensors to a particular task and the
control of sensor state (e.g., on/off, pan/tilt) for accomplishing the task.
• Node services: Services such as time synchronization and node
localization that enable applications to discover properties of a node
and the nodes to organize themselves into a useful network.
• Data storage: Sensor information is stored, indexed, and acce ssed by
applications. Storage may be local to the node where the data is
generated, load-balanced across a network, or anchored at a few points
(warehouses).
• Embedded operating system (OS): The run-time system support for
sensor network applications. An embedded OS typically provides an
abstraction of system resources and a set of utilities.
• System performance goal: The abstract characterization of system
properties. Examples include scalability, robustness, and network
longevity, each of which may be measu red by a set of evaluation
metrics.
• Evaluation metric: A measurable quantity that describes how well the
system is performing on some absolute scale. Examples include packet
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28 (application), location error (application), or processing latency
(application/system). An evaluation method is a process for comparing
the value of applying the metrics on an experimental system with that of
some other benchmark system.
2.2 SINGLE -NODE ARCHITECTURE
2.2.1 Hardware Components & Design Constraints
2.2.2 HARDWARE COMPONENTS:
Choosing the hardware components for a wireless sensor node, obviously
the applications has to consider si ze, costs, and energy consumption of the
nodes. A basic sensor node comprises five main components such as
Controller, Memory, Sensors and Actuators, Communication devices and
Power supply Unit.

Figure 1.3: Sensor node Hardware components
2.2.3 Controller :
A controller to process all the relevant data, capable of executing arbitrary
code. The controller is the core of a wireless sensor node. It collects data
from the sensors, processes this data, decides when and where to send it,
receives data from other sensor nodes, and decides on the actuator‘s
behavior. It has to execute various programs, ranging from time- critical
signal processing and communication protocols to application programs; it
is the Central Processing Unit (CPU) of the node.
For General -purpose processors applications microcontrollers are used.
These are highly overpowered, and their energy consumption is
excessive. These are used in embedded systems. Some of the key
characteristics of microcontrollers are particularly suited to embedded
systems are their flexibility in connecting with other devices like sensors
and they are also convenient in that they often have memory built in.
A specialized case of programmable processors are Digital Signal
Processors (DSPs). They are specifically geare d, with respect to their
architecture and their instruction set, for processing large amounts of
vectorial data, as is typically the case in signal processing applications. In
a wireless sensor node, such a DSP could be used to process data coming
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29 from a simple analog, wireless communication device to extract a digital
data stream. In broadband wireless communication, DSPs are an
appropriate and successfully used platform.
An FPGA can be reprogrammed (or rather reconfigured) ―in the field to
adapt to a changing set of requirements; however, this can take time and
energy – it is not practical to reprogram an FPGA at the same frequency as
a microcontroller could change between different programs.
An ASIC is a specialized processor, custom designed for a given
application such as, for example, high-speed routers and switches. The
typical trade -off here is loss of flexibility in return for a considerably
better energy efficiency and performance. On the other hand, where a
microcontroller requires software development, ASICs provide the same
functionality in hardware, resulting in potentially more costly hardware
development.
Examples: Intel Strong ARM, Texas Instruments MSP 430, Atmel
ATmega.
2.2.4 Memory:
Some memory to store programs and intermediate data ; usually, different
types of memory are used for programs and data. In WSN there is a need
for Random Access Memory (RAM) to store intermediate sensor
readings, packets from other nodes, and so on. While RAM is fast, its
main disadvantage is that it loses its content if power supply is interrupted.
Program code can be stored in Read -Only Memory (ROM) or, more
typically, in Electrically Erasable Programmable Read -Only Memory
(EEPROM) or flash memory (the later being similar to EEPROM but
allowing data to be erased or written in blocks instead of only a byte at a
time). Flash memory can also serve as intermediate storage of data in case
RAM is insufficient or when the power supply of RAM should be shut
down for some time.
2.2.5 Communication Device:
Turning nodes into a network requires a device for sending and receiving
information over a wireless channel.
Choice of transmission medium: The communication device is used to
exchange data between individual nodes. In some cases, wired
communication can actually be the method of choice and is frequently
applied in many sensor networks. The case of wireless communication is
considerably more interesting because it include radio frequencies. Radio
Frequency (RF) - based communication is by far the most relevant one as it
best fits the requirements of most WSN applications.
Transceivers: For Communication, both transmitter and receiver are
required in a sensor node to convert a bit stream coming from a
microcontroller and convert them to and from radio waves. For two tasks a
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30
Transceiver structure has two parts as Radio Frequency (RF) front end and
the baseband part.
1. The radio frequency front end performs analog signal processing in
the actual radio frequency Band.
Figure 1.3.4 : RF front end

2. The baseband processor performs all signal processing in the digital
domain and communicates with a
sensor node‘s processor or other digital circuitry.
a. The Power Amplifier (PA) accepts upconverted signals from the IF
or baseband part and amplifies them for transmission over the antenna.
b. The Low Noise Amplifier (LNA) amplifies incoming signals up to
levels suitable for further processing without significantly reducing
the SNR. The range of powers of the incoming signals
varies from very weak signals from nodes close to the reception
boundary to strong signals from nearby nodes; this range can be up to
100 dB.
c. Elements like local oscillators or voltage -controlled oscillators and
mixers are used for frequency conversion from the RF spectru m to
intermediate frequencies or to the baseband. The incoming signal at RF
frequencies f RF is multiplied in a mixer with a fixed - frequency signal
from the local oscillator (frequency f LO). The resulting intermediate -
frequency signal has frequency fLO − fRF. Depending on the RF front
end architecture, other elements like filters are also present.
Transceiver tasks and characteristics:
□ Service to upper layer: A receiver has to offer certain services to the
upper layers, most notably to the Medium Access Control (MAC)
layer. Sometimes, this service is packet oriented; sometimes, a
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transceiver only provides a byte interface or even only a bit interface to
the microcontroller.
□ Power consumption and energy efficiency: The simplest interpretation
of energy eff iciency is the energy required to transmit and receive a
single bit.
□ Carrier frequency and multiple channels: Transceivers are available for
different carrier frequencies; evidently, it must match application
requirements and regulatory restrictions.
□ State change times and energy: A transceiver can operate in different
modes: sending or receiving, use different channels, or be in different
power -safe states.
□ Data rates: Carrier frequency and used bandwidth together with
modulation and coding determine the gross data rate.
□ Modulations: The transceivers typically support one or several of
on/off -keying, ASK, FSK, or similar modulations.
□ Coding: Some transceivers allow various coding schemes to be
selected.
□ Transmission power control: Some transceivers can dire ctly provide
control over the transmission power to be used; some require some
external circuitry for that purpose. Usually, only a discrete number of
power levels are available from which the actual transmission power
can be chosen. Maximum output power i s usually determined by
regulations.
□ Noise figure: The noise figure NF of an element is defined as the ratio
of the Signal -to- Noise Ratio (SNR) ratio SNR I at the input of the
element to the SNR ratio SNR O at the element‘s output: NF= . It
describes the degradation of SNR due to the element‘s
operation and is typically given in dB: NF dB= SNR I dB − SNR O dB.
□ Gain: The gain is the ratio of the output signal power to the input signal
power and is typically given in dB. Amplifiers with high gain are
desirable to achieve good energy efficiency.
□ Power efficiency: The efficiency of the radio front end is given as the
ratio of the radiated power to the overall power consumed by the front
end; for a power amplifier, the efficiency describes the ratio of the
output signal‘s power to the power consumed by the overall power
amplifier.
□ Receiver sensitivity: The receiver sensitivity (given in dBm) specifies
the minimum signal power at the receiver needed to achieve a
prescribed Eb/N0 or a prescribed bit/packet error rate.
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32 absence of interference; it evidently depends on the maximum
transmission power, on the antenna characteristics.
□ Blocking performance: The blocking performance of a receiver is its
achieved bit error rate in the presence of an interferer.
□ Out of band emission: The inverse to adjacent channel suppression is
the out of band emission of a transmitter. To limit disturbance of other
systems, or of the WSN itself in a multichannel setup, th e transmitter
should produce as little as possible of transmission power outside of its
prescribed bandwidth, centered around the carrier frequency.Carrier
sense and RSSI: In many medium access control protocols, sensing
whether the wireless channel, the c arrier, is busy (another node is
transmitting) is a critical information. The receiver has to be able to
provide that information. the signal strength at which an incoming data
packet has been received can provide useful information a receiver has
to provi de this information in the Received Signal Strength Indicator
(RSSI).
□ Frequency stability: The frequency stability denotes the degree of
variation from nominal center frequencies when environmental
conditions of oscillators like temperature or pressure change.
□ Voltage range: Transceivers should operate reliably over a range of
supply voltages.
Otherwise, inefficient voltage stabilization circuitry is required.
2.2.6 Sensors and actuators:
The actual interface to the physical world: devices that can observe or
control physical parameters of the environment.
Sensors can be roughly categorized into three categories as
2.2.6.1 Passive, omnidirectional sensors:
These sensors can measure a physical quantity at the point of the sensor
node without actually manipu lating the environment by active probing – in
this sense, they are passive. Moreover, some of these sensors actually are
self-powered in the sense that they obtain the energy they need from the
environment – energy is only needed to amplify their analog signal.
2.2.6.2 Passive, narrow -beam sensors These sensors are passive as well,
but have a well - defined notion of direction of measurement.
2.2.6.3 Active sensors This last group of sensors actively probes the
environment, for example, a sonar or radar sensor or some types of
seismic sensors, which generate shock waves by small explosions.
These are quite specific – triggering an explosion is certainly not a lightly
undertaken action – and require quite special attention.
Actuators: Actuators are just about as diverse as sensors, yet for the
purposes of designing a WSN that converts electrical signals into physical
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33 2.2.7 Power supply:
As usually no tethered power supply is available, some form of batteries
are necessary to provide energy. Some times, some form of recharging by
obtaining energy from the environment is available as well (e.g. solar
cells). There are essentially two aspects: Storing energy and Energy
scavenging. Storing energy: Batteries

2.2.7.1 Traditional batteries:
The power source of a sensor node is a battery, either non- rechargeable
(―primary batteries ‖) or, if an energy scavenging device is present on
the node, also rechargeable (―secondary batteries ‖).
TABLE 1.1: Energy densities for various primary and secondary battery
types
Upon these batteries the requirements are
2.2.7.2 Capacity:
They should have high capacity at a small weight, small volume, and low
price.
The main metric is energy per volume, J/cm 3.
2.2.7.3 Capacity under load:
They should withstand various usage patterns as a sensor node can
consume quite different levels of power over time and actually draw high
current in certain operation modes.
2.2.7.4 Self-discharge:
Their self -discharge should be low. Zinc -air batteries, for example, have
only a very short lifetime (on the order of weeks).
2.2.7.5 Efficient recharging:
Recharging should be efficient even at low and intermittently available
recharge power.
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34 2.2.7.6 Relaxation:
Their relaxation effect – the seeming self -recharging of an empty or
almost empty battery when no current is drawn from it, based on chemical
diffusion processes within the cell – should be clearly understood. Battery
lifetime and usable capacity is considerably extended if this effect is
leveraged.
2.2.7.7 DC–DC Conversion:
Unfortuna tely, batteries alone are not sufficient as a direct power source
for a sensor node. One typical problem is the reduction of a battery‘s
voltage as its capacity drops. A DC – DC converter can be used to
overcome this problem by regulating the voltage deliv ered to the node‘s
circuitry. To ensure a constant voltage even though the battery‘s supply
voltage drops, the DC – DC converter has to draw increasingly higher
current from the battery when the battery is already becoming weak,
speeding up battery death. The DC – DC converter does consume energy
for its own operation, reducing overall efficiency.
Energy scavenging: Depending on application, high capacity batteries that
last for long times, that is, have only a negligible self -discharge rate, and
that can e fficiently provide small amounts of current. Ideally, a sensor
node also has a device for energy scavenging, recharging the battery with
energy gathered from the environment – solar cells or vibration -based
power generation are conceivable options.
2.2.7.8 Photovoltaics:
The well -known solar cells can be used to power sensor nodes. The
available power depends on whether nodes are used outdoors or indoors,
and on time of day and whether for outdoor usage. The resulting power is
somewhere between 10 μW/cm 2 indoors and 15 mW/cm 2 outdoors.
Single cells achieve a fairly stable output voltage of about 0.6 V (and have
therefore to be used in series) as long as the drawn current does not exceed
a critical threshold, which depends on the light intensity. Hence, solar cells
are usually used to recharge secondary batteries.
2.2.7.9 Temperature gradients:
Differences in temperature can be directly converted to electrical energy.
2.2.7.10 Vibrations:
One almost pervasive form of mechanical energy is vibrations: walls o r
windows in buildings are resonating with cars or trucks passing in the
streets, machinery often has low frequency vibrations. both amplitude and
frequency of the vibration and ranges from about 0.1 μW/cm 3 up to 10,
000 μW/cm 3 for some extreme cases. Conv erting vibrations to electrical
energy can be undertaken by various means, based on electromagnetic,
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35 2.2.7.11 Pressure variations:
Somewhat akin to vibrations, a variation of pressure can also be used as a
power source.
2.2.7.12 Flow of air/liquid:
Another often -used power source is the flow of air or liquid in wind
mills orturbines. The challenge here is again the miniaturization, but
some of the work on millimeter scale MEMS gas turbines might be
reusable.

Figure 1.5 A MEMS device for converting vibrations to electrical
energy, based on a variable
capacitor








TABLE 1.2: Comparison of energy sources
2.3 OPERATING SYSTEMS AND EXECUTION
ENVIRONMENTS

 An operating system (OS) is system software that mana ges computer
hardware and software resources and provides common services for
computer programs.
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36  For hardware functions such as input and output and memory
allocation, the operating system acts as an intermediary between
programs and the computer hardware.
 An embedded system is some combination of computer hardware and
software, either fixed in capability or programmable, that is
specifically designed for a particular function.
 Embedded operating systems are designed to be used in embedded
computer systems. They are able to operate with a limited number of
resources. They are very compact and extremely efficient by design.












2.4 INTRODUCTION TO TINYOS AND NESC
The use of an event -based programming model as the only feasible way to
support the concurrency required for sensor node software while staying
within the confined resources and running on top of the simple
hardware provided by these nodes. The open question is how to harness
the power of this programming model without getting lost in the
complexity of many individual state machines sending each other events.
In addition, modularity should be supported to easily exchange one state
machine against another. The operating system TinyOS, along with the
programming language nesC, addresses these challenges.
TinyOS supports modularity and event -based programming by the concept
of components. A component contains semantically related functionality,
for example, for handling a radio interface or for computing routes. Such
a component comprises the required state information in a frame, the
program code for normal tasks, and handlers for events and commands.
Both events and commands are exchanged between different
components. Components are arranged hierarchically, from low-level
components close to the hardware to high-level components making up
the actual application. Events originate in the hardware and pass upward
from low -level to high-level components; commands, on the other hand,
are passed from high-level to low-level components.

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37 Figure shows a timer component that provides a more abstract version of a
simple hardware time. It understands three commands (“init”, “start”,
and “stop”) and can handle one event(“fire”) from another component,
for example, a wrapper component around a hardware timer. It issues
“setRate” commands to this component and can emit a “fired” event
itself.
The important thing to note is that, in staying with the event -based
paradigm, both command and event handlers must run to conclusion; they
are only supposed to perform ver y simple triggering duties. In particular,
commands must not block or wait for an indeterminate amount of time;
they are simply a request upon which some task of the hierarchically lower
component has to act. Similarly, an event handler only leaves informa tion
in its component’s frame and arranges for a task to be executed later; it
can also send commands to other components or directly report an event
further up.
The actual computational work is done in the tasks. In TinyOS, they also
have to run to comple tion, but can be interrupted by handlers. The
advantage is twofold: there is no need for stack management and tasks are
atomic with respect to each other. Still, by virtue of being triggered by
handlers, tasks are seemingly concurrent to each other.
The a rbitration between tasks – multiple can be triggered by several events
and are ready to execute – is done by a simple, power -aware First In
First Out (FIFO) scheduler, which shuts the node down when there is no
task executing or waiting.
With handlers and tasks all required to run to completion, it is not clear
how a component could obtain feedback from another component about a
command that it has invoked there – for example, how could an
Automatic Repeat Request (ARQ) protocol learn from the MAC protocol
whether a packet had been sent successfully or not? The idea is to split
invoking such a request and the information about answers into two
phases: The first phase is the sending of the command, the second is an
explicit information about the outcome of th e operation, delivered by a
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38 separate event. This split -phase programming approach requires for each
command a matching event but enables concurrency under the constraints
of run -to- completion semantics – if no confirmation for a command is
required, no c ompletion event is necessary.
Having commands and events as the only way of interaction between
components (the frames of components are private data structures), and
especially when using split-phase programming, a large number of
commands and events add up in even a modestly large program. Hence, an
abstraction is necessary to organize them. As a matter of fact, the set of
commands that a component understands and the set of events that a
component may emit are its interface to the components of a
hierarc hically higher layer; looked at it the other way around, a component
can invoke certain commands at its lower component and receive
certain events from it. Therefore, structuring commands and events that
belong together forms an interface between two compo nents.The nesC
language formalizes thi s intuition by allowing a programmer to define
interface types that define commands and events that belong together. This
allows to easily express split-phase programming style by putting
commands and their correspond ing completion events into the same
interface. Components then provide certain interfaces to their users and
in turn use other interfaces from underlying components.
Figure shows how the Timer component of the previous example can be
reorganized into using a clock interface and providing two interfaces
StdCtrl and Timer. The corresp onding nesC code is shown in Listing 1.
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39 Note that the component TimerComponent is defined here as a module
since it is a primitive component, directly containing handlers and tasks.
Such primitive components or modules can be combined into larger
configurations by simply “wiring” appropriate interfaces together. For
this wiring to take place, only components that have the correct
interface types can be plugged together (this is checked by the
compiler). Figure shows how the TimerComponent and an additional
component HWClock can be wired together to form a new component
CompleteTimer, exposing only the StdCtrl and Timer interfaces to the
outside; Listing 2 shows the correspondin g nesC code. Note that both
modules and configurations are components.

Using these component definition, implementation, and connection
concepts, TinyOS and
nesC together form a powerful and relatively easy to use basis to
implement both coreoperating syst em functionalities as well as
communication protocol stacks and application functions. Programmers
do use these paradigms and arrive at relatively small, highly specialized
components that are then combined as needed, proving the modularity
claim. Also, code size and memory requirements are quite small.
Overall, TinyOS can currently be regar ded as the standard
implementation platform for WSNs. It is also becoming available for an
increasing number of platforms other than the original “motes” on which
it had been developed. On top of the TinyOS operating system, a vast
range of extensions, protocols, and applications have been developed. A
virtual machine concept describes on top of TinyOS that provides a high -
level interface to concisely represent programs; it is particularly beneficial
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40 for over-the-air reprogramming and retasking of an existing network.
Conceiving of the sensor network as a relational database is made
possible by the TinyDB project.
Other examples
Apart from TinyOS, there are a few other ex ecution environments or
operating systems for WSN nodes. One example is Contiki10, which has
been ported to various hardware platforms and actually implements a
TCP/IP stack on top of a platform with severely restricted resources.
Other examples are ecos and the Mantis project.
Some Examples of Sensor Nodes
There are quite a number of actual nodes available for use in wireless
sensor network research and development. Again, depending on the
intended application scenarios, they have to fulfill quite differen t
requirements regarding battery life, mechanical robustness of the node’s
housing, size, and so on.
The “Mica Mote” family
Starting in the late 1990s, an entire family of nodes has evolved out of
research projects at the University of California at Berkel ey, partially with
the collaboration of Intel, over the years. They are commonly known as
the Mica motes11, with different versions (Mica, Mica2, Mica2Dot)
having been designed. They are commercially available via the company
Crossbow12 in different versio ns and different kits. TinyOS is the usually
used operating system for these nodes.All these boards feature a
microcontroller belonging to the Atmel family, a simple radio modem
(usually a TR 1000 from RFM), and various connections to the outside. In
addit ion, it is possible to connect additional “sensor boards” with, for
example, barometric or humidity sensors, to the node as such, enabling a
wider range of applications and experiments. Also, specialized enclosures
have been built for use in rough environm ents, for example, for
monitoring bird habitats. Sensors are connected to the controller via an
I2C bus or via SPI, depending on the version.
2.5 NETWORK ARCHITECTURE
The architecture of wireless sensor networks draws upon many sources.
Historically, a lot of related work has been done in the context of self -
organizing, mobile, ad hoc networks. While these networks are intended
for different purposes, they share the need for a decentralized, distributed
form of organization. From a different perspective, se nsor networks are
related to real-time computing and even to some concepts from peer-to-
peer computing, active networks, and mobile agents/swarm intelligence.
NETWORK ARCHITECTURE:
It introduces the basic principles of turning individual sensor nodes into a
wireless sensor network. In this optimization goals of how a network
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41 2.5.1. Sensor network scenarios
2.5.2 Optimization goals and figures of merit
2.5.3 Gateway concepts
2.5.1 Sensor Network Scenarios :
2.5.2 Types of sources and sinks:
Source is any unit in the network that can provide information (sensor
node). A sink is the unit where information is required, it could belong to
the sensor network or outside this network to interact with another
network or a gateway to another larger Internet. Sinks are illustrated by
Figure 1.11, showing sources and sinks in direct communication.

Figure 1.11 Three types of sinks in a very simple, single -hop sensor
network
2.5.3 Single -hop versus multi -hop networks:
Because of limit ed distance the direct communication between source and
sink is not always possible. In WSNs, to cover a lot of environment the
data packets taking multi hops from source to the sink. To overcome such
limited distances it better to use relay stations, The data packets taking
multi hops from source to the sink as shown in Figure 1.12, Depending
on the particular application of having an intermediate sensor node at the
right place is high.

Figure 1.12 Multi -hop networks: As direct communication is impossibl e
because of distance and/or

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42 obstacles
Multi -hopping also to improves the energy efficiency of communication
as it consumes less energy to use relays instead of direct communication,
the radiated energy required for direct communication over a distance d is
cdα (c some constant, α ≥ 2 the path loss coefficient) and using a relay at
distance d/2 reduces this energy to 2c(d/2) α
This calculation considers only the radiated energy. It should be pointed
out that only multi - hop networks
operating in a store and forward fashion are considered here. In such a
network, a node has to correctly receive a packet before it can forward it
somewhere. Cooperative relaying (reconstruction in case of erroneous
packet reception) techniques are not considered here.
2.5.4 Multi ple sinks and sources:
2.5.4.1 In many cases, multiple sources and multiple sinks present.
Multiple sources should send information to multiple sinks. Either all or
some of the information has to reach all or some of the sinks. This is
illustrated in figure 1.13.

Figure 1.13 Multiple sources and/or multiple sinks.

Note how in the scenario in the lower half, both sinks and active sources
are used to forward data to the sinks at the left and right end of the
network.
Three types of mobility: In the scenar ios discussed above, all participants
were stationary. But one of the main virtues of wireless communication is
its ability to support mobile participants In wireless sensor networks,
mobility can appear in three main forms
a. Node mobility
b. Sink mobility
c. Even t mobility
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43 (a) Node Mobility: The wireless sensor nodes themselves can be mobile.
The meaning of such mobility is highly application dependent. In
examples like environmental control, node mobility should not happen; in
livestock surveillance (sensor nodes attached to cattle, for example), it is
the common rule. In the face of node mobility, the network has to
reorganize to function correctly.
(b) Sink Mobility: The information sinks can be mobile. For example, a
human user requested information via a PDA w hile walking in an
intelligent building. In a simple case, such a requester can interact with
the WSN at one point and complete its interactions before moving on,
In many cases, consecutive








interactions can be treated as separate, unrelated reques ts.
Figure 1.14
Sink mobility: A mobile sink moves through a sensor network as
information is being retrieved on its behalf(c) Event Mobility: In tracking
applications, the cause of the events or the objects to be tracked can be
mobile. In such scenarios , it is (usually) important that the observed event
is covered by a sufficient number of sensors at all time. As the event
source moves through the network, it is accompanied by an area of
activity within the network – this has been called the frisbee mode l. This
notion is described by Figure 1.15, where the
task is to detect a moving elephant and to observe it as it moves around
Figure 1.15 Area of sensor nodes detecting an event – an elephant – that
moves through the network along with the event source (d ashed line
indicate the elephant’s trajectory; shaded ellipse the activity area
following or even preceding the elephant)

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2.6 OPTIMIZATION GOALS AND FIGURES OF MERIT
For all WSN scenarios and application types have to face the challenges
such as
 How to optimize a network and How to compare these solutions?
 How to decide which approach is better?
 How to turn relatively inaccurate optimization goals into measurable
figures of merit? For all the above questions the general answer is
obtained from
 Quality of service
 Energy efficiency
 Scalability
 Robustness
2.6.1 Quality of service:
WSNs differ from other conventional communication networks in the type
of service they offer. These networks essentially only move bits from one
place to another. Some generic possi bilities are
Event detection/reporting probability - The probability that an event that
actually occurred is not detected or not reported to an information sink that
is interested in such an event For example, not reporting a fire alarm to a
surveillance station would be a severe shortcoming.
Event classification error - If events are not only to be detected but also to
be classified, the error in classification must be small
Event detection delay -It is the delay between detecting an event and
reporting it to any/all interested sinks
Missing reports -In applications that require periodic reporting, the
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45 probability of undelivered reports should be small
Approximation accuracy - For function approximation applications, the
average/maximum absolute or relative er ror with respect to the actual
function.
Tracking accuracy Tracking applications must not miss an object to be
tracked, the reported position should be as close to the real position as
possible, and the error should be small.
2.6.2 Energy efficiency:
Ener gy efficiency should be optimization goal. The most commonly
considered aspects are:
Energy per correctly received bit-How much energy is spent on
average to transport one bit of information (payload) from the transmitter
to the receiver.
Energy per report ed (unique) event -What is the average energy spent to
report one event
Delay/energy trade-offs-―urgent events increases energy investment for a
speedy reporting events. Here, the trade -off between delay and energy
overhead is interesting
Network lifetime The time for which the network is operational
Time to first node death -When does the first node in the network run out
of energy or fail and stop operating?
Network half-life-When have 50 % of the nodes run out of energy and
stopped operating
Time to partit ion-When does the first partition of the network in two (or
more) disconnected parts occur?
Time to loss of coverage the time when for the first time any spot in
the deployment region is no longer covered by any node‘s observations.
Time to failure of first event notification A network partition can be
seen as irrelevant if the unreachable part of the network does not want to
report any events in the first place.
Scalability: The ability to maintain performance characteristics
irrespective of the size of t he network is referred to as scalability. With
WSN potentially consisting of thousands of nodes, scalability is an
obviously essential requirement. The need for extreme scalability has
direct consequences for the protocol design. Often, a penalty in
perfor mance or complexity has to be paid for small networks.
Architectures and protocols should implement appropriate scalability
support rather than trying to be as scalable as possible. Applications with a
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46 Robustness: Wireless sensor networks should also exhibit an appropriate
robustness. They should not fail just because a limited number of nodes
run out of energy, or because their environment changes and severs
existing radio links between two nodes. If possible, these failures have to
be compensated by finding other routes.
Gate way concepts:
For practical deployment, a sensor network only concerned with itself is
insufficient.
The network rather has to be able to interact with other information
devices for example to read the temperature sensors in one‘s home while
traveling and accessing the Internet via a wireless.
Wireless sensor networks should also exhibit an appropriate robustness
They should not fail just because of a limited number of nodes run out of
energy or because of their environment changes and breaks existing radio
links between two nodes.
If possible, these failures have to be compensated by finding other routes.
Figure 1.16 shows this networking scenario, The WSN first of all has to
be able to exchange data with such a
mobile device or with some sort of gateway, which provides the physical
connection to the Internet. The WSN support standard wireless
communication technologies such as IEEE 802.11. T he design of
gateways becomes much more challenging when considering their logical
design. One option is to regard a gateway as a simple router between
Internet and sensor network.

Figure 1.16 A wireless sensor network with gateway node, enabling
access to remote clients via the
Internet
WSN to Internet communication: Assume that the initiator of a WSN –
Internet communication resides in the WSN.
For example, a sensor node wants to deliver an alarm message to some
Internet host.
The first problem to solve is how to find the gateway from within the
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47 Basically, a routing problem to a node that offers a specific service
has to be solved, integrating routing and service discovery
If several such gateways are available, how to choose between them?
In par ticular, if not all Internet hosts are reachable via each gateway or at
least if some gateway should be preferred for a given destination host?
How to handle several gateways, each capable of IP networking, and
the communication among them?
One option is to build an IP overlay network on top of the sensor network
How to map a semantic notion (―Alert Alice ) to a concrete IP address?
Even if the sensor node does not need to be able to process the IP
protocol, it has to include sufficient information (IP addr ess and port
number, for example) in its own packets;
the gateway then has to extract this information and translate it into IP
packets.
An ensuing question is which source address to use here – the gateway in
a sense has to perform tasks similar to that of a Network Address
Translation (NAT) device.

Figure 1.17: A wireless Sensor Network with gateway node, enabling
access to remote
clients via the WSN
Internet to WSN communication: The case of an Internet -based entity
trying to access services of a WSN is even more challenging.
This is fairly simple if this requesting terminal is able to directly
communicate with the WSN.
The more general case is, however, a terminal ―far away requesting
theservice, not immediately able to communicate with any sensor node and
thus requiring the assistance of a gateway node
 First of all, again the question is how to find out that there actually is
a sensor network in the desired location, and how to find out about the
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48  Once the requesting termin al has obtained this information, how to
access the actual services.
 The requesting terminal can instead send a properly formatted request
to this gateway, which acts as an application -level gateway
 The gateway translates this request into the proper intra sensor
network protocol interactions
 The gateway can then mask, for example, a data-centric data
exchange within the network behind an identity -centric exchange used
in the Internet
 It is by no means clear that such an application -level protocol exists
that represents an actual simplification over just extending the actual
sensor network protocols to the remote terminal
 In addition, there are some clear parallels for such an application -level
protocol with so- called Web Service Protocols, which can explic itly
describe services and the way they can be accessed

Figure 1.18: A wireless Sensor Network with gateway node, enabling
access to remote
clients via the internet
2.7 DESIGN PRINCIPLES FOR WSN
2.7.1 Distributed Organization
• Both the scalability and the robustness optimization goal are required
to organize the network in a distributed fashion.
• When organizing a network in a distributed fashion, it is necessary to
know potential shortcomings of this approach
• In many cases, a centralized approach can produ ce solutions that
perform better or require fewer resources.
• One possibility is to use centralized principles in a localized fashion by
electing, out of set of equal nodes.
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49 • Such elections result in a dynamic hierarchy.
• The election process should be repeat ed continuously until the elected
node runs out of energy
2.7.2 In Network Processing Techniques
1. Aggregation: The simplest in -network processing technique is
aggregation. The term aggregation means that information is
aggregated into a condensed form in n odes intermediate between
sources and sinks out of information provided by nodes further away
from the sink. The aggregation function must be applied in the
intermediate nodes as shown in Figure 2.5.
2. Distributed Source Coding and Distributed Compressio n:
 The objective is to encode the information provided by several sensors
by using traditional coding schemes, which may be complex for
simple sensor nodes.
 The readings of adjacent sensors are going to be quite similar and
correlated. Such correlation ca n be exploited instead of sending the
sum of the data so that the overhead can be reduced.
3.Distributed and collaborative signal processing: When complex
computations on a certain amount of
data is to be done, it can be more energy efficient to compute th ese
functions on the sensor nodes using Fast
Fourier Transform (FFT). In principle, this is similar to algorithm design
for parallel computers. However the
energy consumption of communication and computation are relevant
parameters to decide between variou s algorithms.
3. Mobile code/Agent -based networking: The idea of mobile code is to
have a small, compact representation of program code to be sent from
node to node. This code is executed locally for collecting
measurements and then decides where to be sent n ext. This idea has
been used in various environments.
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50 2.7.3 Adaptive Fidelity & Accuracy
• The idea of making fidelity of computation depends upon the amount
of energy available for that particular computation.
• This concept can be extended from a single node to an entire network.
As an example, consider a function approximation application.
• When more sensors participate in the approximation, the function is
sampled at more points and the approximation is better. But more
energy has to be invested.
• Hence, it i s up to an application to define the degree of accuracy of the
results and the task of the communication protocols to achieve this
accuracy.
2.7.4 Data Eccentricity
• In traditional communication networks, the focus will be on the pair of
communicating peers , the sender and the receiver of data.
• In a wireless sensor network, the interest of an application is actual
information reported about the physical environment. This is applicable
when a WSN is redundantly deployed such that any given event can be
report ed by multiple nodes.
• This method of concentrating on the data rather than identity of nodes
is called data-centric networking.
• For an application, this means that an interface is exposed by the
network where data only is addressed in requests.
2.7.5 Expl oit Local Information
• Another useful technique is to exploit location information in the
communication protocols when -ever such information is present.
• Since the location of an event is crucial information for many
applications, mechanisms must be availabl e to determine the location of
sensor nodes.
• It can simplify the design and operation of communication protocols
and can improve their energy efficiency.
2.7.6 Exploit Activity Patterns
• Activity patterns in a wireless sensor network are quite different from
that of traditional networks.
• The data rate averaged over a long time can be very small.
• This can be detected by a larger number of sensors, breaking
into a frenzy of activity, causing a well -known event shower effect.
• Hence, the protocol design should be able to handle such
bursts of traffic by switching between modes of quiescence and of high
activity.
2.7.7 Exploit Heterogeneity
• Sensor nodes can be heterogeneous by constructions, that is, they have
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51 processing power.
• They can also be heterogeneous by evolution, that is, they started from
an equal state, but scavenge energy from the environment due to
overloading.
• Heterogeneity in the network is both a burden and an opportunity.
• The opportu nity is an asymmetric assignment of tasks, giving nodes
with more resources or more capabilities the more demanding tasks.
• The burden is asymmetric task assignments cannot be static but have to
be reevaluated.
2.7.8 Component Based Protocol Stacks
• The conc ept is a collection of components which can form a basic
“toolbox” of protocols and algorithms to build upon.
• All wireless sensor networks will require some form of physical, MAC,
Link layer protocols, routing and transport layer functionalities.
• Moreover, “helper modules” like time synchronization, topology
control can be useful.
• On top of these basic components, more abstract functionalities can
then be built.
• The set of components active on a sensor node can be complex and will
change from application to application.
• Protocol components will also interact with each other either by
using simple exchange of data packets or by exchange of cross -layer
information.Services Interfaces of WSN:
2.8 LIST OF REFERENCES

 Protocols and Architectures for Wireless Sens or Network, Holger Kerl,
Andreas Willig, John Wiley and Sons, 2005
 Internet References
2.9 SUMMARY
 The separation of functionalities is justified from the hardware
properties as is it supported by operating systems like TinyOS. These
trade -offs form the basis for the construction of networking
functionalities, geared toward the specific requirements of wireless
sensor network applications.
 The wireless sensor networks and their networking architecture will
have many different guises and shapes. For many applications, but by
no means all, multihop communication is the crucial enabling
technology, and most of the WSN research as well as the following
part of this book are focused on this particular form of wireless
networking. Four main optimization goals – WSN -specific forms of
quality of service support, energy efficiency, scalability, and robustness
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52 arbitrated and balanced against each other. To do so, the design of
WSNs departs in cruci al aspects from that of traditional networks,
resulting in a number of design principles. Most importantly,
distributed organization of the network, the use of in -network
processing, a data-centric view of the network, and the adaptation of
result fidelity and accuracy to given circumstances are pivotal
techniques to be considered for usage.
 The large diversity of WSNs makes the design of a uniform, general -
purpose service interface difficult; consequently, no final solutions to
this problem are currently available. Similarly, the integration of WSNs
in larger network contexts, for example, to allow Internet - based hosts a
simple access to WSN services, is also still a fairly open problem. The
physical layer is mostly concerned with modulation and demodulati on
of digital data; this task is carried out by so -called transceivers. In
sensor networks, the challenge is to find modulation schemes and
transceiver architectures that are simple, low cost, but still robust
enough to provide the desired service.
2.10 UNIT END EXERCISES
1. Discuss the 4 different types of controllers.
2. State and explain any 5 characteristics of Transceiver. 10. What are the
transceiver operational states?
3. In Wireless Sensor Networks, state the three types of Mobility. 12.
Write a short note on 4 aspects of optimization goals?
4. List and explain any 5 basic principles for designing network
protocols.
5. What are the requirements for WSN service interfaces?
6. State the reasons why gateways are needed in WSN.
7. Explain Single -node Architecture in Detai l?
8. Explain network Architecture?

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53 3
MEDIUM ACCESS CONTROL
PROTOCOLS
Unit Structure
3.0 Objectives
3.1 Introduction
3.2 Fundamentals of MAC Protocols
3.2.1 Performance requirements
3.2.2 Common protocols
3.3 MAC Protocols for WSNs
3.3.1 Schedule -based protocols
3.3.2 Random acce ss-based protocols
3.4 Sensor -MAC Case Study
3.4.1 Protocol overview
3.4.2 Periodic listen and sleep operations
3.4.3 Schedule selection and coordination
3.4.4 Schedule synchronization
3.4.5 Adaptive listening
3.4.6 Access control and data exchange
3.4.7 Message passing
3.5 Summary
3.6 List of References
3.7 Unit End Exercises
3.0 OBJECTIVES
 To understand fundamental and performance requirements of MAC
protocols
 To get familiar with some of the MAC protocols along with the case
study
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54 3.1 INT RODUCTION
WSNs are often made up of numerous cheap, low -power, multifunctional
wireless devices that are randomly and hastily placed around a geographic
area. Because to resource constraints, sensing devices can only process
and communicate a finite quanti ty of data at a time. Yet, it is the combined
effort of these sensing devices that holds out hope for a large impact on a
variety of applications in many different sectors, such as science and
engineering, military scenarios, protecting key infrastructure, and
environmental monitoring.
A high degree of self -organization and coordination between the sensors is
necessary to carry out the duties necessary to support the underlying
application in order to fully utilize the potential benefits of WSNs. The
requir ement for the wireless sensor nodes to self -organize into a multi -hop
wireless network lies at the core of this cooperative endeavor to
accomplish communications. To properly complete the task for which they
are deployed, wireless sensor nodes must therefo re be equipped with
effective communications and network protocols.
Communication linkages must be established between nearby sensor
nodes in order to construct a multi -hop wireless network infrastructure for
data transfer. Nevertheless, communication in w ireless networks is
accomplished via electromagnetic signal transmission in the air, as
opposed to communication over a directed media in wired networks. So,
all sensor network nodes must fairly share this shared communication
medium. A medium access contr ol protocol must be used to accomplish
this. The primary determinant of WSN performance is the selection of the
medium access control protocol.
3.2 FUNDAMENTALS OF MAC PROTOCOLS
The spatial dispersion of the communicating nodes presents a significant
chall enge in developing efficient MAC techniques for shared access
media. The nodes must exchange a certain amount of coordinating
information in order to agree on which node can use the communication
channel at any given moment. However, the utilization of the
communication channel itself is often necessary for the sharing of this
information. The complexity of the access control protocol is increased by
the multiaccess medium problem's recursive nature, which also raises the
administrative burden needed to con trol access among the contending
nodes. Moreover, due to spatial dispersal, a given node cannot instantly
know the status of other nodes in the network.Every data collected by a
node, whether explicitly or implicitly, is at least as old as the time it took
for it to travel over the communication channel.
The aggregate behavior of a distributed multipleaccess protocol is
influenced by two key variables: the overhead involved and the
intelligence of the decision made by the access protocol. These two
elements are inextricably linked. Making an effort to raise decision quality
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55 overhead is probably going to result in a lower -quality decision. As a
result, these two aspects must be traded off.
Although challenging, figuring out the type and volume of data a
distributed multiple access protocol uses could be useful. Knowing
precisely what data is required could help one recognize its
importance.The majority of the distributed multiple -access pro tocols for
WSNs that have been proposed function somewhere along a spectrum of
information, from no information to perfect information. Moreover, the
data can be predefined, dynamic, or local.Every node involved in
communication is aware of predetermined i nformation. During protocol
execution, various nodes obtain dynamic global information. Each node is
aware of local information. Global information that is predetermined and
dynamic may lead to effective coordination among the nodes that could
even be flaw less. Yet, the cost in terms of unused channel capacity is
typically substantial. The usage of local information has the potential to
lower the overhead needed to coordinate the competing nodes, but it may
have a negative impact on the protocol's overall s peed.
The majority of access approaches for shared -medium networks are built
on the trade -off between the MAC protocol's efficiency and the overhead
necessary to achieve it. The performance measurements for the MAC
protocol are presented in the remaining p ortion of this section, along with
the most popular methods for controlling access to the medium.
3.2.1 Performance requirements
The breadth of the research has been fairly extensive in attempting to
ascertain the performance requirements of MAC protocols. Delay,
throughput, robustness, scalability, stability, and fairness have historically
dominated MAC protocol design. The description of these performance
metrics is provided below.
1. Delay: The length of time a data packet spends in the MAC layer
before it is successfully transferred is referred to as delay. In addition
to network traffic volume, the MAC protocol's design decisions also
affect delay. The MAC protocol must enable delay -bound guarantees
for time -critical applications in order for those applica tions to comply
with QoS standards. The specific QoS requirements' semantics vary
depending on the application. With proper message scheduling, both
locally within a communicating node and globally across all nodes in
the network, guaranteed delay limitati ons are typically established. It
is possible to distinguish between probabilistic and deterministic delay
guarantees.An expected value, a variance, and a confidence interval
are often used to describe probabilistic delay guarantees. A known
number of stat e changes occur between message arrival and message
transmission thanks to deterministic delay guarantees. Deterministic
MAC methods thus provide an access time upper bound. In a real -time
setting, where the accuracy of the application depends on the
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56 2. Throughput: The rate at which messages are handled by a
communication system is known as throughput. It is typically
expressed as a number of messages or bits per second. It reflects the
portion of the channel capacity that is utilized for data transmission in
wireless contexts. As the initial load on the communication system
rises, throughput rises. The throughput stops increasing and, in certain
situat ions, may even begin to decrease once the load hits a certain
threshold. To maximize channel throughput while decreasing message
delay is a key goal of a MAC protocol.
3. Robustness: In terms of reliability, availability, and dependability
requirements, robus tness measures how sensitive the protocol is to
errors and false information. Error confinement, error detection and
masking, reconfiguration, and restart are only a few of the multifaceted
concerns that robustness must concurrently solve. It is challengin g to
achieve robustness in a time -varying network like a WSN since it
heavily depends on the failure models of the links and communication
nodes.
4. Scalability: The ability of a communications system to maintain its
performance characteristics regardless of the size of the network or the
number of competing nodes is referred to as scalability. In WSNs, the
total number of sensor nodes may surpass thousands and, in some
situations, millions. Scalability in these networks becomes crucial.
Scalability is difficu lt to achieve, particularly in time -varying contexts
like wireless networks. Avoiding relying on globally consistent
network states is a typical strategy for achieving scalability. With the
creation of hierarchical structures and information aggregation
techniques, localizing interactions among the communication nodes is
another strategy.For instance, creating clusters of sensor nodes enables
the development of highly scalable shared medium access protocols.
Similar to this, combining data from various sens ors enables the
creation of traffic patterns that can be effectively used to scale the
MAC protocol to many sensor nodes.
5. Stability: The ability of a communications system to manage changes
in the traffic load over extended periods of time is referred to a s
stability. For instance, a stable MAC protocol must be capable of
handling sudden loads that are greater than the maximum sustained
load, provided that the channel's maximum capacity is not exceeded by
the promised long -term load. Usually, a MAC protocol 's scalability is
examined in terms of either delay or throughput.If the message waiting
time is constrained, a MAC protocol is regarded as stable in terms of
latency. There is a limited backlog of messages in the transmission
queue, which can be used to i dentify these systems. A MAC protocol
is stable in terms of throughput if the throughput does not decrease as
the load being delivered rises. In time -varying large -scale WSNs, it is
challenging to accommodate load changes while preserving system
stability. The careful scheduling of bursty traffic is one potential
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57 6. Fairness: If a MAC protocol distributes channel capacity among
competing communicating nodes fairly without unneces sarily slowing
down the network throughput, it is said to be fair. To ensure equitable
QoS and prevent instances where certain nodes perform better than
other nodes, it is desirable to achieve fairness across competing nodes.
No application is therefore st arved or harshly penalized. It should be
noted that the definition of fairness given above makes the assumption
that the channel capacity requirements of all communication nodes are
equal. But, it's possible that the network may need to support a variety
of traffic sources with varying traffic production patterns and a wide
range of QoS requirements.Communicating nodes are given various
weights to reflect their relative resource shares in order to
accommodate varied resource needs. Then, based on the weight s
assigned, proportional fairness is attained. If it is impossible to
increase any competitive node's allocation without lowering another
node's service rate below its proportional fair share, a MAC protocol is
said to be proportionally fair.
While global information may be needed to coordinate access to the
communication medium among all contending stations, equitable
resource allocation in wireless networks can be challenging to
implement. Even when a centralized resource allocation approach is
utilized, it is challenging to calculate each contesting node's fair share
due to the time -varying properties of wireless networks.
7. Energy efficiency: A sensor node has one or more integrated sensors,
a small embedded processor, and short -range radio communication
capabilities. These sensor nodes are powered by small -capacity
batteries. Wireless sensor nodes are frequently installed in
unsupervised locations, which makes it challenging to replace their
batteries. Additionally, it is difficult and unstable to recharge sensor
batteries using scavenged energy. The lifespan of a sensor node is
directly impacted by these harsh restrictions.In order to increase the
lifespan of sensor nodes in WSNs, energy conservation becomes of
utmost importance. One of the most crucial co nsiderations in the
design of the MAC protocol for wireless sensor nodes is energy
economy. The MAC -layer protocols' energy inefficiency is caused by
a number of factors.
Energy -saving link -layer protocols decrease, if not completely eliminate,
energy wast e from the sources mentioned above by managing the radio.
With comprehensive energy management strategies that not only pay
attention to the sensor node radio but also to other sources of energy
usage, more energy gains can be made.
3.2.2 Common protocols
The main determining factor in a WSN's success is the selection of the
MAC technique. The shared media access issue has been addressed using
a number of different tactics. These strategies make various efforts to
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58 decision with the quality of the decision. Three broad categories: fixed
assignment, demand assignment, and random assignmentcan be used to
group these systems.
1. Fixed assignment protocols: Each node is given a specified fixed
quanti ty of the channel resources in fixed -assignment schemes. Each
node uses its allotted resources solely without engaging in inter -node
competition. Frequency -division multiple access (FDMA), time -
division multiple access (TDMA), and code -division multiple ac cess
are common protocols that fall under this category (CDMA).
2. Demand assignment protocols: Demand assignment protocols'
primary goal is to increase channel usage by optimally or almost
optimally assigning the channel's capacity to competing nodes.
Demand assignment methods ignore idle nodes and only take into
account nodes that are prepared to transmit, in contrast to fixed -
assignment schemes, where channel capacity is exclusively provided
to the network nodes in a predetermined manner independent of thei r
present communication demands. The chosen node is given access to
the channel for a predetermined period of time, which can range from
a fixed time slot to the duration of a data packet transmission.
The access to the channel between competing nodes must typically be
arbitrated by a network control mechanism when using demand
assignment protocols. Furthermore, in order for competing stations to
dynamically seek access to the communication medium, a logical
control channel other than the data channel may b e necessary. The
requirement to seek access to the channel may cause data transfer to be
delayed depending on the protocol's characteristics. Demand
assignment protocols can also be divided into centralized and
distributed types. Whereas token - and reserva tion-based systems
employ distributed control, polling systems are an example of
centralized control.
3. Randomassignment protocols: Each communicating node in fixed -
assignment schemes is given a frequency band in FDMA systems or a
time slot in TDMA systems. Regardless of whether the node has data
to send or not, this assignment is static. So, if the traffic source is
bursty, these techniques may not be effective. The allocated bandwidth
is lost when the node is idle and there is no data to be transferred. In
order to overcome this flaw, random assignment schemes do away
with pre -allocating bandwidth to communication nodes.
Random assignment techniques have no influence over which
communicating node will next be able to access the media.
Additionally, no node i s given a predicted or scheduled time to
broadcast according to these methodologies. To access the
transmission medium, all backed -up nodes must compete. When
multiple nodes attempt to communicate at once, collision happens. The
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59 scheduling colliding packets for later retransmissions in order to deal
with collisions.
The first long radio links and satellite communications used random
access techniques. One of the earliest such media access protoc ols was the
ALOHA protocol, often known as pure ALOHA. Simply put, ALOHA
enables nodes to communicate anytime they have data to send. The
creation of numerous methods, such as carrier -sense multiple access
(CSMA), carrier -sense multiple access with collisi on detection
(CSMA/CD), and carrier -sense multiple access with collision avoidance
(CSMA/CA), was prompted by efforts to enhance the performance of pure
ALOHA.
3.3 MAC PROTOCOLS FOR WSNS
The most important consideration for designing scalable and reliable
MAC layer protocols for WSNs is the requirement to conserve energy.
Overhead that is too high, listening that isn't being used, packet collisions,
and overhearing are some of the factors that lead to energy waste.The
competition between the nodes necessita tes the sharing of control and
synchronization information in order to control access to the media. These
control and synchronization packets might consume a lot of energy when
they are explicitly exchanged. Extended durations of inactive listening
might a lso reduce network speed and use more energy. Sometimes, the
energy used by a sensor over the course of its lifespan is more than half
squandered by idle listening.Another big source of energy waste is the
retransmission of collision packets. The MAC -layer protocol may
experience considerable performance reduction if there are many of these
collisions. Similar to excessive overhearing, excessive overhearing results
in a node receiving and decoding packets meant for other sensor nodes,
which increases energy consumption and significantly reduces network
throughput. After the node recognizes that the destination address is
different from its own address, these packets are eventually dropped.
Most MAC -layer protocols' primary goal is to lessen energy loss broug ht
on by collisions, idle listening, overhearing, and excessive overhead.
Schedule - and contention -based MAC -layer protocols are two broad
categories that fit these protocols. A class of deterministic MAClayer
protocols known as schedule -based protocols ba ses channel access on a
schedule. One sensor node at a time can access a channel. This is
accomplished by allocating resources in advance to each sensor node.
Contention -based MAC -layer methods prevent resource pre allocation to
specific sensors. Instead, all nodes share a single radio channel that is
made available as needed. Yet, attempts to access the communications
medium simultaneously are met with collision.
In order to arrange channel access among competing sensor nodes,
distributed, randomized techn iques are often used to resolve collisions. As
nodes become inactive, the fundamental strategy employed to reduce
overhearing is to push them into a sleep state. Yet, uncoordinated slumber
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60 MAC-layer protocols have proposed a range of less restrictive schedules
to alleviate this weakness and synchronize the activities of the network
sensors.
3.3.1 Schedule -based protocols
The existence of a timetable that controls access to resources to preven t
contention between nodes is presummated by schedule -based MAC
protocols for WSNs. Resources like time, a frequency range, or a CDMA
code are typical. Schedule -based MAC protocols' primary goal is to
maximize energy efficiency in order to increase the lif espan of the
network. Scalability, adaptability to shifts in traffic load, and network
topology are further desirable qualities. The majority of scheduled -based
protocols for WSNs employ a kind of TDMA that divides the channel into
time slots, as shown in Figure 1.

Figure 1: TDMA -based MAC protocols for wireless sensor networks
A logical frame is made up of N consecutive slots, where N is a system
variable. This logical structure keeps coming back in cycles. Each sensor
node is given a set of precise time slots for each logical frame. The
schedule that the sensor node follows for each logical frame is made up of
this set. The schedule can either be hybrid, where the structure varies over
various time scales and sensor behavior, or fixed, where it is built on
demand by the base station on a per -frame basis to represent the current
requirements of sensor nodes and traffic pattern.
A sensor switches between its active mode and sleep mode in accordance
with its assigned schedule. In the active mode, the sensor transmits and
receives data frames using the slots that have been given to them within a
logical frame. Sensor nodes enter sleep mode when they are not in their
designated slots. In order to save energy, the sensor nodes in this mode
turn off their radio t ransceivers.
1. Self organizing medium access control for sensornets (SMACS): A
medium access control protocol called SMACS enables the creation of
haphazard network topologies without the need for network node
global synchronization. Nonsynchronous scheduled communication, a
major characteristic of SMACS, enables links to be generated and
planned concurrently throughout the network without the requirement
for expensive global connectivity information exchange or time
synchronization. Each node in the network keeps a super frame, which
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61 superframe has a defined length. The superframe is additionally
broken into smaller frames. Each frame's size is not constant and may
change over time for a single node a s well as from one node to
another. Each node must conduct a neighborhood discovery operation
on a regular basis in order to identify nearby nodes according to
SMACS. By giving each detected neighbor a time slot, each node
creates a link with them. The tim e slots are chosen so that at each slot,
the node only converses with its neighbors. The link construction
mechanism must make sure that there is no interference between
neighboring links, despite the fact that a node and its neighbors are not
needed to tr ansmit at distinct slot times.This is accomplished by
spreading code (CDMA) or randomly selecting a channel from a vast
pool of channels for each link. Each node in the superframe structure
keeps its own time slot schedules with all of its neighbors, and t o
communicate, nodes must set their radios to the appropriate frequency
channel or CDMA code.
2. Bluetooth: A centralized TDMA -based protocol serves as the main
media access control mechanism for the developing technology known
as Bluetooth. With one common s hort-range radio link, Bluetooth is
intended to replace cables and infrared links used to connect various
electronic devices, including cell phones, headsets, PDAs, digital
cameras, laptop computers, and their accessories. The ISM frequency
range at 2.45 G Hz is where Bluetooth operates. Its physical layer is
based on a technique for allocating hopping sequences and a
pseudorandom frequency -hopping scheme with a hopping frequency
of 1.6 kHz. With 1 -MHz spacing, a set of 79 hop carriers are defined.
Each hop sequence establishes a Bluetooth channel with a 1 Mbps data
rate.A piconet is a collection of devices that communicate via a single
channel.
In order to support broadcasting by a slave to all members of its
piconet, Bluetooth defines four different types o f communication
between nodes: intra piconet unicast for slave -to-slave communication
within a piconet; intra piconet broadcast; inter piconet unicast for
piconet -to-piconet communications; and inter piconet broadcast for
piconet -to-all scatternet node com munications.
The source slave enters its own MAC address in the data packet's
equivalent field for intra -piconet unicast transmission, sets the packet's
forward field to 1, and sets the destination address to the desired
destination node. The master examin es the forward field after
receiving the message. If it is, the master sends the message to the
intended slave device indicated by the destination address of the
original packet, replacing the MAC address field with its own MAC
address.
The source slave wr ites its own MAC address, sets the forward field to
1, and sets the destination address to 000 for intra piconet broadcast
communication. The forward field is already set when the master
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62 the piconet by the master, who substitutes its own address for the
MAC address.
For inter piconet unicast communication, the source device sends the
data packet with its own MAC address and sets the forward field to 1,
the broadcast field to 1 and the des tination address to the relay of the
next piconet. Furthermore, the source device sets the routing vector
field (RVF) of the packet to contain the logical path to the targeted
destination device in the intended piconet. The RVF is a sequence of
tuples of t he form (LocId, Mac_Addr), where LocId represents the
identity of the local master and Mac_Addr its corresponding piconet
MAC address. Upon receiving the message, the master forwards it to
the relay node. The relay extracts from the RVF the next pair,
containing the local identity and the MAC address of the master, and
sends the message to this master. This process is repeated until the
RVF becomes empty, signaling that the destination device has been
reached.The relay sends the message to this master by ex tracting the
following pair from the RVF, which contains the local identity and
MAC address of the master. Up until the RVF is empty, indicating that
the destination device has been reached, this procedure is repeated.
The source device produces a packet w ith its own MAC address and
sets the forward and broadcast fields to 1 and the destination address to
000 for inter -piconet broadcast communication. The master is then
notified of the packet.The packet is sent to all slaves in the piconet,
including relay nodes, when the master observes that the broadcast
field is set to 1. Relay nodes receive broadcast packets and forward
them to all connected masters save the one from which they originated.
3. Low -Energy Adaptive Clustering Hierarchy (LEACH): Nodes are
clust ered using a hierarchical approach using LEACH. Nodes take
turns acting as cluster heads inside each cluster. To establish
communication between nodes and their cluster head, LEACH
employs TDMA. Messages from the cluster head's cluster nodes are
forwarded to the base station.
A TDMA schedule is established by the cluster head node and sent to
every other node in the cluster. Data message collisions are avoided by the
scheduling.The nodes can utilize the schedule to identify the times when
they need to be ac tive as well. With the exception of the head cluster, this
enables each cluster node to turn off its radio components until the
designated time intervals. LEACH presupposes that cluster nodes begin
the cluster setup phase simultaneously and maintain synchr onization
throughout. Sending synchronization pulses to every node from the base
station is one method of synchronization that could be used.
LEACH employs a code assignment system based on transmitters to
lessen inter -cluster interference. Direct -sequence spread spectrum (DSSS)
is used to communicate between a node and its cluster head. Each cluster
is given a specific spreading code, which is utilized by all nodes in the
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63 spreading codes are given to cluster heads starting with the first one to
announce its position and moving on to succeeding cluster heads. Nodes
must modify their transmission power in order to lessen interference from
neighboring clusters.
The cluster head ag gregates the data after receiving data packets from its
cluster nodes and sends the data to the base station. With fixed spreading
code and CSMA, a cluster head and base station can communicate with
one another. The cluster head must sense the channel to m ake sure no
other cluster heads are currently broadcasting data using the base station
spreading code before it can transmit data to the base station. The cluster
head delays data transmission until the channel is free of traffic if the
channel is felt to be busy. The cluster head delivers the data using the base
station spreading code when this incident takes place.
3.3.2 Random access -based protocols
Standard random -access Contention -based protocols, also referred to as
MAC -layer protocols, don't need coo peration from the nodes accessing the
channel. Nodes that have collided back off for an arbitrary period of time
before trying to access the channel once more. Nevertheless, WSN
environments are not well suited for these protocols.The addition of
collision avoidance, request -to-send (RTS), and clear -to-send (CTS)
techniques to these protocols boosts their functionality and increases their
resistance to the hidden terminal issue. Yet, due to collisions, idle
listening, overhearing, and significant control ov erhead, contention -based
MAC -layer protocols continue to have low energy efficiency. The design
of random -access MAC -layer protocols made an effort to address this flaw
by focusing on minimizing energy waste in order to increase the network
lifetime.
By us ing a separate signaling channel, the power aware multiaccess
protocol with signaling (PAMAS) prevents overhearing between nearby
nodes. In order to enable nodes to turn off their radio transceivers when
they are not actively sending or receiving packets, the protocol combines
the usage of a busy tone with RTS and CTS packets. Nevertheless, the
protocol does not offer any techniques to cut down on energy loss brought
on by inactive listening.
Latency is exchanged for energy efficiency in the sparse topology and
energy management (STEM) protocol. Two radio channels: a data radio
channel and a wake -up radio channelare used to accomplish this. In a
STEM form, the wake -up signal is not encoded data but a busy tone. A
pseudoasynchronous planned scheme is called a s STEM. According to this
technique, a node disables its data radio channel until it needs to
communicate with another node. A node starts sending on the wake -up
radio channel when it has data to send. Similar to a paging signal, the
wake -up signal channel is used. This signal is transmitted for a sufficient
amount of time to page all nearby nodes.A node may stay awake long
enough to receive a "session" of packets after being roused from
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64 pending packets before entering the sleep mode once more. Because it is a
broad protocol, STEM can be utilized with other MAC -layer scheduling
techniques. However, the approach only works in network situations when
events happen seldom. If events happen of ten, the energy lost from
sending wake -up signals continuously may equal or even be greater than
the energy obtained from sleeping modes.
Using RTS and CTS packets, a variety of contention -based protocols
modelled after IEEE 802.11 avoid overhearing. These protocols frequently
employ the technique of forcing a contending node into sleep mode by
overhearing the RTS and CTS packet exchange between two other
contending nodes. To prevent idle listening, these protocols also rely on
coordinated schedules between nearby nodes. Particularly when the size of
the data packets is of the same order of magnitude as the size of the RTS
and CTS packets, these protocols differ in how they maintain low duty
cycles and achieve energy economy.
A contention -based MAC -layer pro tocol called the timeout -MAC (T -
MAC) was created for applications with low message rates and low
sensitivity to latency. T -MAC nodes communicate with one another using
RTS, CTS, and acknowledgement packets to prevent collision and
guarantee reliable transm ission. The protocol also employs an adaptive
duty cycle to lower energy usage and adjust for changes in traffic load.
The T -MAC protocol's fundamental goal is to minimize idle listening by
sending all messages in bursts of varying length. Between bursts, nodes
are allowed to sleep. Also, based on the current load, the protocol
estimates the ideal active time duration dynamically. Since messages must
be buffered between active times, the buffer capacity establishes an upper
limit on the maximum frame time.
A lower -power carrier -sense media access protocol for WSNs is the
Berkeley media access control (B -MAC). The B -MAC protocol
incorporates a modest core of media access capabilities in contrast to
conventional IEEE 802.11 -inspired MAC -layer protocols, which also
include techniques for network organization and clustering. B -MAC
employs listening for low -power communication, link -layer
acknowledgments for reliability, clear channel assessment (CCA), and
packet back -offs for channel arbitration. B -MAC does not d irectly support
multipacket techniques that handle message fragmentation, deal with
hidden terminal issues, or impose specific low -power policies.
3.4 SENSOR -MAC CASE STUDY
The sensor -MAC (S -MAC) protocol is specifically developed to minimize
energy loss b rought on by overhearing, idle listening, collision, and
control overhead.The objective is to achieve high levels of reliability and
scalability while improving energy efficiency. The protocol does,
however, suffer some performance degradation in terms of per-hop
fairness and latency. S -MAC employs a variety of methods to lower
energy usage, manage overhead, and decrease latency in order to enhance
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65 methods for achieving energy efficiency whi le maintaining low latency
are covered in the sections that follow.
3.4.1 Protocol overview
The protocol design anticipates a sizable number of sensor nodes with
constrained computing, communication, and storage resources. Ad hoc,
self-organized, and self -managed wireless networking is how the nodes
are set up. Sensor data is processed and transmitted in a store -and-forward
fashion. It is expected that the applications supported by the network
alternate between extended periods of inactivity, during which n othing
happens, and brief active times, during which data flow towards the base
station through peer sensor node messaging occurs. Additionally, it is
anticipated that the applications will tolerate higher latency for a longer
network lifespan.The protecti on of vital infrastructure as well as
surveillance and monitoring of natural ecosystems are typical uses that
come under this category. In these applications, the sensors must be on
guard for protracted periods of time, after which they go dormant until an
event happens. These events often happen orders of magnitude less
frequently than it takes for a message to be transmitted across the network
towards the base station.
S-MAC establishes low -duty-cycle operation on nodes in a multi -hop
network and results in significant energy savings by taking advantage of
the bursty characteristic of sensor applications.S -MAC nodes occasionally
switch between listening and sleep modes during the protracted periods of
no sensing. Every node establishes a wakeup time and a slumber period,
during which its radio is off. The node resumes operation when the timer
expires. The protocol makes use of synchronized sleeping amongst nearby
nodes to further decrease control overhead while maintaining low message
latency.Sleeping perio dically results in less energy use but more delay.
The requirements of the sensing application have a significant impact on
how important message delay is. Applications that can tolerate latency on
the order of seconds are the main emphasis of S -MAC. Yet, latency can
considerably rise when nodes faithfully adhere to their schedule. S -MAC
employs adaptive listening to solve this flaw and maintain message delay
within the targeted -second -level latency.
As previously mentioned, the S -MAC design is concentrated on
applications that work together, such as monitoring and surveillance
applications. The programs work together to complete a particular
objective, like safeguarding a vital infrastructure. These applications'
nature makes it possible for one sensor node to have a lot of information to
share with its neighbors at any given time. The idea of message passing, in
which a node is permitted to deliver a lengthy message in bursts, is used
by S-MAC to satisfy this need while also lowering overhead. Overhearing
is reduced and avoided via message passing.
3.4.2 Periodic listen and sleep operations
By reducing idle listening, the S -MAC architecture aims to cut energy
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66 allows for this. Nodes periodic ally enter a sleep state during which their
radios are fully disabled. When there is activity on the network, nodes
become active.Figure 2 shows the fundamental periodic listen and sleep
schedule.Each node in this system sets a wake -up timer and sleeps for the
allotted amount of time. As the timer runs out, the node awakens and starts
listening to see whether it needs to connect with other nodes. A frame is
the collective term for the full cycle of listening and sleeping. The
listening interval to frame len gth ratio, or duty cycle, is what distinguishes
each frame. The listening interval duration can be separately chosen by
sensor nodes, however for the sake of simplicity, the protocol takes the
value to be the same for all nodes.

Figure 2: S -MAC period li sten and sleep modes of operations
Nodes have complete control over the times they sleep and listen. To
minimize the amount of management required to establish connections
between these nodes, it is preferred that the schedules of nearby nodes be
coordinat ed.S-MAC nodes build virtual clusters around schedules, but
instead of communicating through a master node like a cluster head to
achieve coordination, they speak directly with one another to exchange
and coordinate their sleep and listen schedules.
3.4.3 Schedule selection and coordination
In order for everyone to listen and sleep at the same time, the nearby
nodes coordinate their listening and sleeping schedules. Each node
chooses a schedule and shares it with its neighbors during the
synchronization tim e in order to coordinate their listening and resting.
Every node has a schedule table with all of its known neighbors'
schedules.
A node must first listen to the channel for a predetermined period of time,
at least equal to the synchronization period, befo re choosing a schedule. If
the node does not get a schedule from another node before the end of this
waiting period, it immediately selects its own schedule. The node informs
all of its neighbors of the schedule it has chosen by broadcasting a SYNC
packet. The node must first carry out physical carrier sensing before
broadcasting the SYNC packet, it's important to note.This lessens the
possibility of SYNC packet collisions between rival nodes. The node sets
its schedule to be the same as the schedule receiv ed if, during the
synchronization period, it receives a schedule from a neighbor before
deciding on and publishing its own schedule. The node waits until the
following synchronization period to inform its adjacent nodes about the
schedule.
It should be not ed that after selecting and announcing its own schedule, a
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67 interference or collision contaminate the SYNC packet. The node simply
discards its own schedule and adopts the new one if it has no neighbors
with whom it shares a timetable. The node, on the other hand, adopts both
schedules if it is aware of other nearby nodes that have already accepted it.
The node must then awaken at the two adopted schedules' listen intervals.
Figure 3 illustrates this.With various schedules, border nodes only need to
broadcast one SYNC packet, which is advantageous. The drawback of this
strategy is that border nodes use more energy because they don't spend as
much time in sleep mode.
It should be observed that if a SYNC packet is delayed or lost,
neighboring nodes may still be unable to find one another. S -MAC nodes
must frequently discover their neighbors in order to fix this flaw. To do
this, a node must periodically listen to the full synchronization time.
Nodes without neighbors at the moment are anticipated to carry out
neighbor discovery more frequently.

Figure 3: Border node schedule selection and synchronization
3.4.4 Schedule synchronization
To stop long -term clock drift, neighboring nodes must periodical ly
synchronize their schedules. Sending a SYNC packet is used to update the
schedule. Figure 4 illustrates how the listen interval is split into two
subintervals to allow a node to receive both SYNC packets and data
packets. Three cases are represented in this figure. The sender sends
simply a SYNC packet in the first scenario, a data packet in the second
scenario, and a SYNC packet along with the data packet in the third
scenario.

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68 During these subintervals, the channel access of competing nodes is
controlled by a multi -slotted contention window. SYNC packet
transmission takes place during the first subinterval, while data packet
transmission occurs during the second subinterval. At either of these
subintervals, a competing station chooses a time slot at random, conducts
carrier sensing, and initiates packet transmission if it notices that the
channel is empty. The RTS/CTS handshake is used during data packet
transmission to guarantee exclusive access to the channel. This access
method ensures that both the synchronization and data packets reach the
nearby nodes.
3.4.5 Adaptive listening
As a message is stored and transferred across nearby network nodes, it
may experience increased lat ency, according to a closer examination of
the periodic listen and sleep scheme. Data packets could be delayed at
each hop if a sensor must precisely adhere to its sleep pattern. The
protocol makes use of an assertive method known as adaptive listening to
remedy this flaw and enhance latency performance. According to this
method, a node that hears, during its listen period, a neighboring node and
another node exchanging a CTS or RTS packet assumes that it might be
the next hop along the routing path of the overheard RTS/CTS packet,
disregards its own wake -up schedule, and schedules an additional listening
period around the time the transmission of the packet terminates.The
duration field of the overheard CTS or RTS packet is used by the
overhearing node to c alculate the amount of time required to finish
transmitting the packet. The node sends an RTS packet to start an
RTS/CTS handshake with the overhearing node as soon as it receives the
data packet. If the latter node is awake, it is ideal since then the pac ket
forwarding process between the two nodes can start right away. The
overhearing node returns to sleep until the next scheduled listen interval if
it doesn't receive an RTS packet during adaptive listening.
3.4.6 Access control and data exchange
S-MAC us es a CSMA/CA -based technique, incorporating physical and
virtual carrier sensing and the use of RTS/CTS handshake to lessen the
impact of the hidden and exposed terminal difficulties, to govern access to
the communication channel among competing sensor nod es. The network
allocation vector (NAV), a variable whose value contains the amount of
time left in the current packet transmission, is used to implement virtual
carrier sensing. The NAV value is initially set to the value contained in the
transmitted pack et's duration field.As time goes on, the value decreases
until it eventually equals zero. A node cannot start a transmission on its
own until the NAV value is zero. When doing physical carrier sensing, the
channel is listened to for signs of active transmi ssion. To prevent
collisions and hunger, carrier sensing is randomly distributed within a
contention window. If the channel is clear according to both virtual and
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69 Nodes may need to listen to every broadcast from their neighbors in order
to execute virtual carrier sensing efficiently. It may be necessary for nodes
to listen to packets that are meant for other nodes as a result. Overhearing
packets could waste a lot of energy. S -MAC permits nodes to e nter sleep
mode once they hear the exchange of an RTS or a CTS packet between
two other nodes in order to prevent overhearing.The node enters the sleep
state until the NAV value reaches zero after initializing its NAV with the
value found in the duration f ield of the RTS or CTS packets.The
overhearing avoidance procedure may result in significant energy savings
because data packets are often larger than control packets. Figure 5 depicts
the collision -avoidance strategy utilized by S -MAC.

Figure 5: S -MAC c ollision avoidance scheme
A node must first perceive the channel before attempting to transmit a
message. If the channel is congested, the node sleeps and awakens when
the channel is free. When transferring a data packet across an empty
channel, a node fir st sends an RTS packet and then waits for a CTS packet
from the recipient. The node delivers its data packet after obtaining the
CTS packet. After the node gets a confirmation from the recipient, the
transaction is finished. It is important to note that th e connecting nodes
continue to exchange data packets during their regular sleep periods after
the successful exchange of the RTS and CTS packets. The nodes wait until
the data transfer is finished before starting their usual sleep
cycle.Moreover, the excha nge of the RTS and CTS packets is not
necessary for the transmission of a broadcast packet, such as a SYNC
packet.
3.4.7 Message passing
S-MAC introduces the idea of message forwarding, where a message is a
meaningful unit of data that a node can process, to enhance application -
level performance. The messages are broken up into little pieces. Then, a
single burst of these fragments is delivered. A single RTS/CTS exchange
is used to transport the message fragments between the transmitting and
receiving nodes .The medium is set aside following this exchange for the
duration required to successfully transfer the entire message. Each
fragment also includes the time required to transmit all of the next pieces
and their related acknowledgments in its duration field . Figure 6 illustrates
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70

Figure 6: S -MAC message passing
The sender waits for the receiver to acknowledge the transmission of a
fragment. The sender transmits the next fragment if it receives the
acknowledgment. Nevertheless, if the acknowled gement is not received,
the sender immediately retransmits the unacknowledged frame and
extends the time needed to finish transmission of the segment to account
for the time needed to transmit one more fragment and its accompanying
acknowledgment. Notably, only expanded fragments or their related
acknowledgements will alert sleeping nodes to this extension. The
transmission extension is not heard by nodes that only heard the first RTS
and CTS packet exchange. The S -MAC has the potential to significantly
reduce energy consumption.It works well for situations when higher
latency is acceptable and fairness is not a crucial design goal.
3.5 SUMMARY
A new technology called sensor networking has numerous potential uses,
including the protection of vital infrastruc ture, environmental monitoring,
smart cities, all -pervasive and ubiquitous healthcare, and robotic
exploration. Typically, a WSN is made up of a sizable number of
dispersed, battery -powered nodes that are furnished with one or more
sensors, embedded CPUs, and low -power radios. As a multihop wireless
network, these nodes cooperate with one another. To successfully
complete the task for which they are deployed, wireless sensor nodes
depend on the design of effective MAC -layer protocols for WSNs.
The primary d eterminant of WSN performance is the selection of the
medium access control protocol. An effective MAC layer protocol for
WSN must take into account a number of factors. Battery -powered sensor
network nodes are typical, and it is frequently challenging, if not
impossible, to replace or recharge them. To increase the network's
lifespan, an efficient MAC -layer protocol design for a WSN must also be
energy efficient. In order to accommodate changes in network size, node
density, and topology, the MAClayer prot ocol must also be scalable.
Finally, when designing MAC layer protocols for WSNs, access equity,
low latency, high throughput, and bandwidth utilization are also crucial
considerations.
As more WSNs continue to appear, interest in the development of a MAC -
layer protocol for sensor networks is expected to remain high. Also, recent
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71 MAC -layer protocol for WSNs is designed. A wireless network device
fitted with such a radio will be better able to adapt to and interact with its
environment while carefully controlling its energy consumption thanks to
the direct environment interaction capabilities of cognitive radios.
3.6 LIST OF REFERENCES
1) Protocols and Architectures for Wireless Sensor Network , Holger
Kerl, Andreas Willig, John Wiley and Sons, 2005
2) Wireless Sensor Networks Technology, Protocols, and
Applications,Kazem Sohraby, Daniel Minoli and TaiebZnati, John
Wiley & Sons, 2007
3) Mobile communications, Jochen Schiller,2nd Edition, Addison wis ely,
Pearson Education,2012
4) Fundamentals of Wireless Sensor Networks, Theory and Practice,
WaltenegusDargie, Christian Poellabauer, Wiley Series on wireless
Communication and Mobile Computing, 2011
5) Networking Wireless Sensors, Bhaskar Krishnamachari, Cam bridge
University Press, 2005
3.7 UNIT END EXERCISES
1) Illustrate the performance requirements of MAC protocols.
2) Describe the MAC Protocols for WSNs.
3) Write a note on Schedule -based protocols.
4) Explain the Random access -based protocols.
5) Describe the Sensor -MAC Case Study.
6) What do you mean by Periodic listen and sleep operations?
7) Explain Schedule selection and coordination.
8) What do you mean by Schedule synchronization?
9) What is Adaptive listening?
10) Explain Access control and data exchange.
11) Write a note on Message passing.

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ROUTING PROTOCOLS
Unit Structure
4.0 Objectives
4.1 Introduction
4.2 Data Dissemination and Gathering
4.3 Routing Challenges and Design Issues in Wireless Sensor Networks
4.3.1 Network Scale and Time -Varying Characteristics
4.3.2 Resource Constr aints
4.3.3 Sensor Applications Data Models
4.4 Routing Strategies in Wireless Sensor Networks
4.4.1 WSN Routing Techniques
4.4.2 Flooding and Its Variants
4.4.3 Sensor Protocols for Information via Negotiation
4.4.4 Low -Energy Adaptive Clustering Hi erarchy
4.4.5 Power -Efficient Gathering in Sensor Information Systems
4.4.6 Directed Diffusion
4.4.7 Geographical Routing
4.5 Summary
4.6 List of References
4.7 Unit End Exercises
4.0 OBJECTIVES
 To examine fundamental routing difficulties in WSNs and offer
various development methods for routing protocols in these networks
 To draw focus on the particular characteristics of the traffic that is
often generated in WSNs
 To understand basic routing strategies used to strike a balance between
responsiveness and energy efficiency
4.1 INTRODUCTION
Whether they are made up of fixed or mobile sensor nodes, WSNs can be
deployed to support a wide range of applications in a number of contexts.
Depending on the application, these sensors are placed in different ways .
For example, sensor nodes are often installed ad hoc in environmental
monitoring and surveillance applications to cover the precise area to be
watched (e.g., C1WSNs). Smart wearable wireless devices and
biologically compatible sensors can be strategicall y affixed to or
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73 patient's vital signs. As soon as they are deployed, sensor nodes form an
autonomous wireless ad hoc network that needs little to no
maintenance.After that, sensor nodes wo rk together to complete the duties
required by the application for which they were installed.
The primary duty of wireless sensor nodes is to detect and gather data
from a target domain, process the data, and communicate the information
back to specified s ites where the underlying application lives,
notwithstanding the diversity in the goals of sensor applications. The
creation of an energy -efficient routing protocol is necessary to create
pathways between sensor nodes and the data sink in order to do this
operation effectively. The network lifetime must be maximized by the
path selection process. The routing challenge is extremely difficult due to
the features of the environment that sensor nodes normally operate in as
well as significant resource and energ y limitations.
4.2 DATA DISSEMINATION AND GATHERING
An essential component of WSNs is how data and queries are transmitted
from the base station to the location where the target phenomena are being
observed. Direct data interchange between each sensor node and the base
station is an easy way to complete this task. Nevertheless, a single -hop
solution is expensive since nodes that are distant from the base station risk
fast running out of energy, substantially reducing the network's
lifetime.This is especiall y true if the wireless sensors are set up to cover a
big area of land or if they are movable and could wander away from the
base station.
Data sharing between the sensors and base stations is typically done
utilizing multi -hop packet transmission over shor t communication
distances to solve the drawbacks of the single -hop strategy. In particular in
very dense WSNs, such an approach results in significant energy savings
and lowers communication interference amongst sensor nodes competing
for the channel. Figu re 1 shows data forwarding between the sensors that
collect data and the sinks that make it available. Data gathered by the
sensors is transferred to the base station utilizing multi -hop pathways in
response to requests made by the sinks or when particular events take
place in the region being monitored.It is important to note that, depending
on the application, sensor nodes may gather data that has been linked
while travelling to the base station.

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74 Data packet s between the source and the destination must be forwarded by
intermediate nodes in a multi -hop WSN. The main purpose of the routing
algorithm is to choose which group of intermediary nodes should be used
to create a data -forwarding path between the source and the destination.
Routing in large -scale networks is, in general, a demanding problem
whose solution must take into account a number of complex design
requirements, such as correctness, stability, and optimality with regard to
several performance metri cs. In order to meet the traffic demands of the
supported application while extending the life of the network, it is
necessary to solve additional issues brought about by the fundamental
features of WSNs in combination with severe energy and bandwidth
limits.
4.3 ROUTING CHALLENGES AND DESIGN ISSUES IN
WIRELESS SENSOR NETWORKS
Although WSNs and wired and ad hoc networks have a lot in common,
they also have a few distinctive qualities that make them stand out from
other networks. These distinctive qualitie s bring novel routing design
requirements that go above and beyond those commonly found in wired
and wireless ad hoc networks into sharp focus. Achieving these design
specifications poses a different and particular set of difficulties. These
difficulties c an be attributed to a number of things, including severe
energy constraints, constrained computing and communication
capabilities, the dynamically changing environment in which sensors are
deployed, special data traffic models, and application -level requir ements
for quality of service.
4.3.1 Network Scale and Time -Varying Characteristics
With severe energy constraints, sensor nodes can only operate with limited
processing, storage, and communication capabilities. The densities of the
WSNs may differ greatly , ranging from very sparse to very dense, due to
the numerous possible sensor -based applications. However, in many
applications, the sensor nodeswhich can sometimes number in the
hundreds or even thousandsare set up haphazardly and frequently without
super vision over large coverage regions. As a result of the requirement to
self-organize and conserve energy, sensor nodes in these networks behave
in a dynamic and highly adaptive manner, continually adjusting to their
level of activity or lack thereof.In orde r to avoid the severe performance
deterioration of the supported application, sensor nodes may also be
necessary to modify their behavior in response to the irregular and
unpredictable behavior of wireless connections induced by excessive noise
levels and radio -frequency interference.
4.3.2 Resource Constraints
In order to deploy sensor nodes widely and cheaply, complexity is kept to
a minimum. WSNs must operate on limited battery reserves while
achieving a long lifetime, hence energy is a major challenge. A significant
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75 transmission. The duty cycle of the wireless sensors can be dynamically
controlled to lower energy consumption. Yet, many mission -critical sensor
applications make the energy management challenge particularly difficult.
Due to the demands of these applications, it is necessary to concurrently
maintain a set level of sensing and communication performance limits.
Hence, the topic of how to create scalable routing algorithms that can
function well under a variety of performance limitations and design
requirements arises.The development of these protocols is fundamental to
the future of WSNs.
4.3.3 Sensor Applications Data Models
The information flow between the sensor nodes and th e data sink is
described by the data model. How data are sought and used depends a lot
on the type of application used in these models. To fulfil the data -
gathering demands and interface requirements of various sensor
applications, a number of data models have been proposed. Data collection
models for a class of sensor applications must be based on periodic
sampling or be triggered by the occurrence of particular events. Before
being sent to the data sink, data can be collected, stored, and possibly
process ed by a sensor node in other applications.A third category of sensor
applications, however, necessitates bidirectional data models since they
call for two -way communication between sensors and data sinks.The
complexity of the route design challenge is incr eased by the requirement
to support numerous data models. It becomes a massive design and
engineering challenge to tailor the routing protocol to the particular data
needs of an application while also supporting a wide range of data models
and providing th e best possible performance in terms of scalability,
reliability, responsiveness, and power efficiency.
4.4 ROUTING STRATEGIES IN WIRELESS SENSOR
NETWORKS
It is possible to argue that the WSN routing problem represents a
conventional trade -off between re sponsiveness and efficiency. The
overhead necessary to accommodate the constrained processing and
communication capabilities of sensor nodes must be weighed against the
requirement to do so. In a WSN, overhead is typically assessed in terms of
mobile node processing demands, bandwidth use, and power consumption.
The key to solving the routing problem is devising a plan to effectively
balance these conflicting demands. Additionally, given the inherent
properties of wireless networks, it is crucial to conside r whether or not the
existing routing protocols created for ad hoc networks are enough to
handle this difficulty.
Ad hoc network routing methods can be divided into different categories
based on how information is collected, retained, and used to compute
paths based on the information that has been collected. Proactive, reactive,
and hybrid techniques can all be distinguished from one another. The
proactive approach, also known as table -driven, focuses on redistributing
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76 consistent across all network nodes. The network's structure can either be
flat or hierarchical. Flat proactive routing strategies may be able to
determine the best routes. In a situation where things are changing
quickly, th e overhead needed to compute these pathways can be too
high.Large ad hoc networks' routing needs are better met by hierarchical
routing.Reactive routing techniques create routes to a small number of
destinations as needed. Typically, these methods don't ke ep track of global
data on all network nodes. In order to find paths between a source and a
destination, they must consequently rely on a dynamic route search. Often,
this entails flooding a route discovery query with responses that are sent
back via the o pposite direction. The ways in which the flooding process is
managed by the reactive routing strategies to cut down on communication
overhead and the ways in which routes are calculated and reestablished
when failure occurs are different.
In order to achie ve stability and scalability in massive networks, hybrid
techniques rely on the existence of network structure. These techniques
divide the network into clusters that are mutually nearby and are
dynamically maintained as nodes enter and exit the clusters t o which they
are assigned. Clustering offers a framework that can be used to condense
the routing algorithm's response to alterations in the network environment.
The use of proactive routing within a cluster and reactive routing between
clusters can be use d to create a hybrid routing method. The primary
difficulty is lowering the overhead needed to keep the clusters running.
4.4.1 WSN Routing Techniques
The routing protocols for WSNs must take into account the network nodes'
power and resource constraints, the wireless channel's time -varying
quality, and the potential for packet loss and delay. There have been
various proposed routing strategies for WSNs to address these design
constraints. All nodes are treated as peers in a flat network topology used
by on e class of routing techniques. A flat network architecture provides a
number of benefits, including low infrastructure maintenance costs and the
opportunity for various paths to be discovered between communication
nodes for fault tolerance.
A second catego ry of routing protocols puts a structure on the network in
order to increase its stability, scalability, and energy efficiency. In this
class of protocols, network nodes are grouped together into clusters, with
the cluster leader, for instance, being the n ode with the highest residual
energy. The cluster leader is in charge of directing information between
clusters and organizing activity inside the cluster. Clustering has the
ability to save energy use and increase network longevity.
A third category of ro uting protocols uses a data -centric strategy to spread
information throughout the network. The method uses attribute -based
naming, where a source node instead of a specific sensor node queries an
attribute for the phenomena. By giving duties to sensor node s and
expressing inquiries about particular properties, the interest diffusion is
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77 multicasting, geo -casting, and any casting, can be used to convey interests
to the sensor nodes.
A sensor node is addressed by a fourth class of routing protocols based on
its location. Applications where the node's location inside the network's
geographic coverage is important to the query sent by the source node can
benefit from location -based routing. Such a query could indicate the
location of a certain place in the network environment or a specific area
where a phenomenon of interest might occur.
4.4.2 Flooding and Its Variants
In both wired and wireless ad hoc networks, flooding is a typical approach
widely employed for path discovery and information dissemination. The
routing approach is straightforward and doesn't rely on pricey network
topology upkeep or difficult route discovery algorithms. Each node that
receives a data or control packet delivers the packet to all of its neighbors
as part of the reactive strategy known as flooding.An information package
follows every route after transmission. The packet will ultimately get there
unless the network is shut down. Moreover, the transmitted packet takes
the new routes when the network architecture changes. The idea of
flooding in a data communications network is depicted in Figure 2. As
seen in the image, flooding in its most basic form can lead to network
nodes endlessly replicating packets.

Figure 2:Fl ooding in data communications networks
A hop count field is typically present in a packet to prevent it from
recirculating endlessly in the network. The hop count is initially chosen to
roughly equal the network's diameter. The hop count decreases by one f or
each hop the packet makes as it moves through the network. The packet is
only discarded when the number of hops reaches zero. A time -to-live
field, which keeps track of how many time units a packet is permitted to
live in the network, can be used to ach ieve a similar result. The packet is
no longer forwarded once this period has passed.By giving each data
packet a unique identification, flooding can be made even worse by
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78 transmitted.A recent hi story of the traffic must be kept as part of this
method in order to track which data packets have already been forwarded.
Flooding has a number of drawbacks when employed in WSNs, despite
how straightforward its forwarding rule is and how little maintenan ce it
needs. The first problem with flooding is that it might cause a traffic jam.
Duplicate control or data packets transmitted repeatedly to the same node
result in this unwanted outcome. Flooding's second negative aspect is the
overlap issue it creates. Resource blindness is the third and most severe
disadvantage of flooding. Flooding utilizes a straightforward forwarding
rule that ignores the sensor nodes' energy limitations while routing
packets. As a result, the node's energy may quickly run out, dras tically
decreasing the network's lifetime.
Despite its straightforward forwarding rule and reasonably inexpensive
cost A derivative strategy called gossiping has been suggested as a remedy
for flooding's drawbacks. Similar to flooding, gossiping also relie s on a
straightforward forwarding rule and does not demand expensive topology
maintenance or sophisticated route discovery techniques. Instead of
broadcasting a data packet to every neighbor as is the case with flooding,
gossiping calls for each node to de liver the incoming packet to a randomly
chosen neighbor. The packet is forwarded to the neighbor picked by the
randomly chosen neighbor after it has been received by that
neighbor.Until the packet arrives at its designated location or the
maximum hop count is reached, this procedure iteratively continues. By
restricting the number of packets each node delivers to its neighbor to one
copy, gossip avoids the implosion issue. A packet may have high latency
while travelling to its destination, especially in a l arge network. This is
primarily due to the protocol's randomness, which essentially investigates
one path at a time.
4.4.3 Sensor Protocols for Information via Negotiation
A family of data -centric negotiation -based information dissemination
protocols for W SNs is called Sensor Protocols for Information through
Negotiation (SPIN). These protocols' principal goal is to effectively
distribute observations made by individual sensor nodes to all of the
network's sensor nodes. In WSNs, straightforward protocols li ke flooding
and gossiping are frequently proposed to achieve information
dissemination. Flooding calls for sending copies of the data packet to each
node's neighbors until it reaches every node in the network.In contrast,
gossip just needs that a node rece iving a data packet transmit it to a
randomly chosen neighbor, using randomization to decrease the number of
duplicate messages.
Both flooding and gossiping are easy and appealing because they don't
require topology upkeep and use basic forwarding principl es.
Nevertheless, as the network's size and traffic load increase, these
algorithms' performance quickly deteriorates in terms of packet latency
and resource use. This performance flaw is often brought on by
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79 are sent to the same sensor node as a result of traffic
implosion.Geographic overlap, on the other hand, results in nodes
covering the same region disseminating identical data items to network
sensor nodes unnecessarily. Basic prot ocols like floods and chitchat do not
modify their behavior to adjust communication and computation to the
present status of their energy supply. This lack of resource awareness and
flexibility could significantly shorten the network's lifespan as highly
active nodes risk quickly running out of energy.
The fundamental goal of SPIN and its associated family members is to fix
the flaws and improve the performance of traditional information
distribution protocols. This family of protocols' fundamental principl es are
resource adaptability and data negotiation. Before any data are sent across
network nodes, semantic -based data negotiation demands that nodes
running SPIN "learn" about the content of the data. By having nodes
associate metadata with the data they p roduce, SPIN takes advantage of
data naming to undertake negotiations before delivering the actual data.A
receiver may send a request to access the advertised data if they show
interest in its content. By ensuring that data are sent only to interested
node s, this type of negotiation prevents traffic implosion and greatly
lowers the amount of redundant data that is transmitted over the network.
Furthermore, by allowing nodes to limit their requests to mention only the
data they are interested in receiving, t he usage of meta data descriptors
avoids the chance of overlap.
Resource adaption enables SPIN -powered sensor nodes to adjust their
operations to the state of their available energy sources. Before sending or
processing data, each node in the network can p robe the corresponding
resource management to keep track of its resource usage. When the energy
level drops, the node may scale back or stop performing specific tasks,
such forwarding third -party information and data packets. The SPIN
resource adaptability function enables nodes to prolong their lives and, as
a result, the network's lifespan.
Three different sorts of messages are used by SPIN -running nodes for
negotiation and data delivery. New data is advertised among nodes using
the first message type, AD V. A network node can advertise its data to the
other nodes in the network by first sending an ADV message that contains
the information describing the data. Requesting an advertised piece of
valuable data is done using the second message type, REQ.A netwo rk
node interested in obtaining specific data sends a REQ message to the
metadata advertising node after receiving an ADV containing metadata,
and the node subsequently sends the requested data. The actual data
gathered by a sensor and a metadata header ar e both included in the third
message type, DATA.Generally speaking, the data message is bigger than
the ADV and REQ messages. The latter messages are typically much
smaller than the corresponding data packet and just carry metadata.
Energy use can be signi ficantly reduced by limiting the duplicate
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80 The fundamental operation of SPIN is shown in Figure 3, where sensor
node A, the data source, sends an ADV message containing the
information cha racterizing its data to sensor node B, its close neighbor.
Node B sends a REQ message to request the data after expressing interest
in it. Node B transmits an ADV message after receiving the data to inform
its close neighbors of the new information. Only n odes C, E, and G, three
of these neighbors, show interest in the information. These nodes send
node B a REQ message, and node B responds by sending the requested
data to each of the requesting nodes.

Figure 3: SPIN basic protocol operations
4.4.4 Low -Energy Adaptive Clustering Hierarchy
A routing technique called Low -energy Adaptive Clustering Hierarchy
(LEACH) is created to gather and send data to a base station or other data
sink. The primary goals of LEACH are:
 Prolongation of the network's life
 Each s ensor node in the network using less energy
 Using data aggregation to minimize communication messages
LEACH uses a hierarchical technique to divide the network into a number
of clusters in order to accomplish these goals. A chosen cluster leader is in
charge of overseeing each cluster. The cluster leader takes on the
obligation to complete several duties.Data from the cluster's members are
periodically collected as the first task.The cluster head aggregates the data
after collecting it in an effort to elimi nate duplication among associated
values. The direct transmission of the aggregated data to the base station is
the cluster head's second primary responsibility. The sent data is combined
and sent over a single hop. Figure 4 shows the network model utilize d by
LEACH.The creation of a TDMA -based schedule, in which each node of
the cluster is given a time slot that it can use for transmission, is the cluster
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81 to the other cluster membe rs. LEACH nodes employ a code -division
multiple access -based communication protocol to lessen the possibility of
sensor collisions both inside and outside the cluster.

Figure 4: LEACH network model
LEACH's fundamental processes are divided into two separ ate phases.
Figure 5 presents an illustration of these stages. Cluster creation and
cluster -head selection are the two steps that make up the setup phase's
initial phase. Data gathering, aggregation, and delivery to the base station
are the main objectives of the second phase, often known as the steady -
state phase. To reduce the protocol overhead, it is believed that the setup
will last considerably less time than the steady -state phase.

Figure 5: LEACH phases
LEACH possesses a number of characteristics t hat allow the technique to
use less energy. All sensor nodes in LEACH are required to use a certain
amount of energy since they round -robin take on the cluster head function
according to their remaining energy. As LEACH is a fully distributed
algorithm, th e base station's control information is not needed. Local
cluster management eliminates the requirement for knowledge of the
entire world's networks. Additionally, since nodes are no longer required
to send their data straight to the sink, data aggregation by the cluster also
makes a significant contribution to energy savings. LEACH outperforms
traditional routing protocols, such as direct transmission and multihop
routing, minimum -transmission -energy routing, and static clustering -based
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82 4.4.5 Power -Efficient Gathering in Sensor Information Systems
A family of routing and information -gathering protocols for WSNs
includes hierarchical PEGASIS and the power -efficient collecting in
sensor information systems (P EGASIS) extension. PEGASIS has two
dual-purpose goals. Secondly, by establishing a high level of energy
efficiency and uniform energy usage across all network nodes, the
protocol attempts to increase the lifespan of a network. Second, the
protocol aims to shorten the time that data takes to reach the sink.
The network model that PEGASIS is considering assumes a uniform
distribution of nodes over a region. It is assumed that nodes are aware of
the placements of all other sensors globally. Also, they can adju st their
power to cover virtually any area. The nodes might additionally have radio
transceivers that support CDMA. Data collection and delivery to a sink,
often a wireless base station, is the responsibility of the nodes. The
objective is to create a rout ing structure and an aggregation system to
balance energy consumption among the sensor nodes, reduce energy
consumption, and transmit the aggregated data to the base station with the
least amount of delay possible.PEGASIS employs a chain structure as
oppos ed to other protocols, which rely on a tree structure or a cluster -
based hierarchical organization of the network for data collection and
dissemination.
Nodes communicate with their nearest neighbors using this structure. The
farthest node from the sink is where the chain is assembled from. Network
nodes are gradually added to the chain, starting from the end node's
nearest neighbor. The nearest neighbor to the top node in the existing
chain is added to the chain first when adding nodes that are currently
outside of it, and this process continues until all nodes have been added. A
node utilizessignal strength to calculate the distance to each of its
neighbors, then uses that distance to determine which neighbor is the
closest. This data is used by the node t o modify the signal strength such
that only the closest node is audible.
The chain leader is chosen from among the nodes in the chain. Its duty is
to deliver the compiled data to the base station. After each round, the
chain's position is changed by the ch ain leader. Rounds can be regulated
by the data sink, and a strong beacon that it issues can trigger the change
from one round to the next. The chain's nodes alternate taking the leading
position to ensure that the overall energy consumption of the network is
balanced.However, it should be noted that nodes acting as chain leaders
may be arbitrarily far from the data sink.
In PEGASIS, data aggregation is accomplished along the chain. The
aggregation procedure can be carried out successively as follows in its
most basic form. The last node on the right end of the chain receives a
token first from the chain leader. The end node sends its data to its
downstream neighbor in the chain towards the leader after getting the
token. The downstream neighboring node rece ives the data from the
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83 leader receives the compiled data.The same aggregation method is used
until the data reach the leader after the leader provides a token to the left
end of the chain after getting the data from the right side of the chain. The
leader gathers the data and sends it to the data sink after receiving it from
both ends of the chain. Although straightforward, the sequential
aggregation approach may cause significant del ays in the delivery of the
aggregated data to the base station. However, if arbitrarily close
simultaneous transmission cannot be done without causing signal
interference, then such a sequential system might be required.
Using parallel data aggregation alo ng the chain is one possible method to
lessen the time needed to send aggregated data to the sink. If the sensor
nodes have CDMA -capable transceivers, a high level of parallelism can be
attained. A hierarchical structure can be "overlaid" over the chain an d
utilized to do data aggregation using the additional capability to carry out
arbitrarily near transmissions without interference. Nodes at a specific
level of the hierarchy broadcast to a close neighbor at a higher level of the
hierarchy once each round. The leader at the top of the hierarchy receives
the aggregated data at the end of this process. The latter sends the base
station the final data aggregate.
Consider the scenario shown in Figure 6 to provide an example of the
chain -based approach. In this illustration, it is assumed that every node
uses a greedy algorithm to build the chain and has global knowledge of the
network. Moreover, it is presumable that nodes transmit to the base station
in rounds, with node i mod N where N is the total number of n odes
transmitting the aggregate data to the base station in round i. Node 3, in
the chain's third position, is the round 3 leader according to this
assignment.The neighbor to the right must get data from every node in an
even position. Node 3 stays in an o dd position at the following level.
Because of this, all nodes in an even position combine their data and send
it to the appropriate neighbors. Node 3 is no longer at an unusual position
at the third level. The only node other than node 3 to reach this lev el is
node 7, which gathers its data and delivers it to node 3. Node 3 then
combines the information received with its own information before
sending it to the base station.

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84 The chain -based bina ry technique uses significantly less energy because
nodes work in close proximity to one another. In addition, the technique
ensures that the leader will receive the aggregated data after log2N steps
because the hierarchical, treelike structure is balanced . In PEGASIS, a
chain -based binary aggregation approach has been utilized as an
alternative to high parallelism. It has been demonstrated that the strategy
works best with CDMA -capable sensor nodes in terms of the energy -delay
product needed for each round of data collection, a parameter that
balances the energy and delay costs.
4.4.6 Directed Diffusion
A data -centric routing protocol for information collection and sharing in
WSNs is called directed diffusion. The protocol's primary goal is to
generate sign ificant energy savings in order to increase the network's
lifespan. This goal is accomplished through directed diffusion, which
maintains message -exchange interactions between nodes contained inside
a certain network area. Direct diffusion can nevertheless achieve resilient
multipath delivery and adapt to a small portion of network pathways by
using localized contact. Significant energy savings are achieved by the
protocol's special feature and the nodes' capacity to aggregate responses to
queries.
Direct d iffusion's primary components are interests, data messages,
gradients, and reinforcements. Directed diffusion uses a publish -and-
subscribeinformation model in which an inquirer expresses an interest
using attribute –valuepairs. An interest can be thought o f as a question or
an interrogation that expresses the object of the inquiry. An example of
how an interest in hummingbirds can be communicated using a set of
attribute -value pairs is shown in Table 1. Sensor nodes that can serve the
request return the rel evant data.
Table 1:Interest Description Using Value and Attribute Pairs

The data sink periodically broadcasts an interest message to each neighbor
for each current sensing task. As a named data interest, the message
spreads throughout the sensor network . This exploratory interest message's
main goal is to see if there are any sensor nodes that can accommodate the
desired interest.An interest cache is maintained by each sensor node. The
interest cache contains entries for each different interest. The cach e item
has a timestamp field, several gradient fields for each neighbor, and a
duration field, among other fields.The timestamp of the most recent
matching interest was contained in the timestamp column. Both the data
rate and the direction in which data a re to be delivered are specified in
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85 determine the data rate's value.The duration field provides an estimate of
the interest's lifespan. The attribute's timestamp is used to calculate th e
duration's value. Interest spread in a WSN is shown in Figure 7.

Figure 7: Interest propagation
A gradient can be compared to a reply link that directs traffic to the node
next to the source of the interest. The discovery and development of
pathways be tween the data sinks interested in the identified data and the
nodes that can serve the data are made possible by the diffusion of
interests over the entire network in conjunction with the establishment of
gradients at the network nodes. An event -detection sensor node looks for a
corresponding record in its interest cache. In the event that a match is
found, the node computes the greatest event rate among all of its outgoing
gradients first.It then configures its sensing component to sample events at
this m aximum pace. The node then broadcasts an event summary to each
neighbor it has a gradient for. When a neighboring node receives data, it
looks through its cache for an entry with the same interest. In the absence
of a match, the node discards the data mess age without taking any further
action. If such a match occurs and the received data message does not
already have a corresponding data cache entry, the node adds the message
to the data cache and broadcasts the data message to the surrounding
nodes.
A node checks its interest cache after receiving an interest to see if it
already has an entry for that interest. The receiving node makes a new
cache entry if such an entry does not already present. The node then
instantiates the parameters of the freshly gener ated interest field using the
data from the interest. Moreover, the entry is configured to have a single
gradient field pointing at the neighboring node from where the interest is
received, with the event rate provided. The node updates the timestamp
and d uration fields of the matching entry if there is a match between the
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86 value specified in the interest message if the entry does not already have
one for the sender of the interest. The no de merely updates the timestamp
and duration fields if the matching interest item has a gradient for the
interest sender. When a gradient expires, it is taken out of the interest
entry. The first gradient configuration is shown in Figure 8.

Figure 8: Ini tial gradient setup
A sink creates numerous pathways during the gradient building stage. By
raising its data flow, the sink can employ these routes to events of greater
quality. Via a process called path reinforcement, this is accomplished. The
sink may de cide to support one or more specific neighbors. In order to
accomplish this, the sink resends the initial interest message across the
chosen paths at a greater data rate, reinforcing the source nodes' incentives
to submit data more frequently. The most eff ective path can then be kept
while the others are negatively reinforced.By timing out any high -data-rate
gradients in the network besides those that are explicitly reinforced,
negative reinforcement can be produced. Data delivery along a reinforced
channel is depicted in Figure 9.

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87 Directed diffusion can be used to fix link failures brought on by external
elements that affect the communications channel as well as node failures
or performance degradation broug ht on by node energy loss or total
depletion. Data loss or a reduction in rate are often indicators of these
failures. An alternative path that is sending at slower rates can be found
and strengthened when a path connecting a sensing node and the data sink
fails. Lossy links can also be negatively reinforced by allowing the
neighbor's cache expire over time or by delivering interests at the
exploratory data rate.
Diffusion that is directed has the ability to save a lot of energy. It can
attain comparatively high performance over non -optimized paths thanks to
its localized interactions.The resulting diffusion processes are also
resilient to a variety of network dynamics. Node addressing is unnecessary
due to its data -centric methodology.The query -on-demand da ta model,
however, is closely related with the directed diffusion paradigm. This
might restrict its applicability to applications that make sense as a data
model, where the process of interest matching can be carried out quickly
and clearly.
4.4.7 Geograph ical Routing
Geographical routing's major goal is to create an effective route search
towards the target using location data. Geographical routing is ideal for
sensor networks because it removes redundant packets from diverse
sources, allowing for a reduct ion in the number of broadcasts to the base
station. In sensor networks, the need for data aggregation results in a shift
from the traditional address -centric paradigm, where communication takes
place between two addressable endpoints, to a data -centric pa radigm,
where the content of the data is more significant than the identity of the
node that gathers the data.In this new paradigm, an application may send a
query to learn more about a phenomenon that is occurring in or close to a
certain physical locatio n or prominent landmark. For instance, scientists
interested in traffic flow patterns may want to know the typical quantity,
size, and speed of cars using a certain stretch of a roadway. The data
substance is more significant than the name of the sensors t hat gather and
distribute data about traffic flow on a particular stretch of route. Also, a
number of nodes that are situated near the specified stretch of the roadway
might take part in gathering and aggregating the information required to
respond to the query.Conventional routing techniques are not well adapted
to handle multidimensional queries that are spatially specific because they
are typically designed to find a path between two addressable endpoints.
Geographical routing, on the other hand, uses lo cation data to get from one
place to another, using the location of each node as the address.
Geographical routing has a low computational and communication
overhead and is compatible with data -centric applications. With
conventional routing methods, such as those used in distributed shortest -
path routing protocols for wired networks, a router may need to be aware
of the full network topology or a summary of it in order to determine the
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88 state representing the current topology on a regular basis and whenever a
link fails in order to maintain correct pathways to all destinations.
According to the product of the number of routers and the rate at which
the topology changes in the network, the requirement to update the
topology state continuously may result in significant overhead.
Geographical routing, on the other hand, does not necessitate keeping a
"heavy" state at the routers to monitor the topology's present state. Making
the appropria te forwarding decisions simply requires the propagation of
single -hop topological information, such as the location of the "best"
neighbor. Maintaining internal data structures like routing tables is
unnecessary due to the self -describing nature of geograp hical routing and
its localized decision -making process.As a result, the control overhead is
significantly decreased, improving its scalability on big networks. These
characteristics make geographical routing a workable option for routing in
sensor network s with limited resources.
4.5 SUMMARY
The routing challenge is extremely difficult due to the features of WSNs
and the environment in which sensor nodes are frequently installed.In this
chapter, we concentrated on the fundamental problems with routing in
WSNs and discussed various methods for creating routing protocols for
these networks. We provide a quick taxonomy of the fundamental routing
techniques utilized to balance responsiveness and energy efficiency. We
reviewed several protocols that deal with th e issue of routing in
contemporary WSNs. The routing issue has several workable solutions
that have come to light. As the use of WSNs in many industries grows,
improvements in network hardware and battery technology will open the
door to realistic, economi cally viable implementations of these routing
protocols.
4.6 LIST OF REFERENCES
1) Protocols and Architectures for Wireless Sensor Network, Holger
Kerl, Andreas Willig, John Wiley and Sons, 2005
2) Wireless Sensor Networks Technology, Protocols, and
Applicatio ns,Kazem Sohraby, Daniel Minoli and TaiebZnati, John
Wiley & Sons, 2007
3) Mobile communications, Jochen Schiller,2nd Edition, Addison wisely,
Pearson Education,2012
4) Fundamentals of Wireless Sensor Networks, Theory and Practice,
WaltenegusDargie, Christian Poellabauer, Wiley Series on wireless
Communication and Mobile Computing, 2011
5) Networking Wireless Sensors, Bhaskar Krishnamachari, Cambridge
University Press, 2005
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89 4.7 UNIT END EXERCISES
1) What do you mean by Data Dissemination and Gathering?
2) Illustrate Routing Challenges and Design Issues in Wireless Sensor
Networks.
3) Explain Network Scale and Time -Varying Characteristics.
4) What is Resource Constraints?
5) Write a note on Sensor Applications Data Models.
6) Explain Routing Strategies in Wireless Sensor Networks.
7) Describe WSN Routing Techniques.
8) Write a note on Flooding and Its Variants.
9) What is Sensor Protocols for Information via Negotiation?
10) Explain Low -Energy Adaptive Clustering Hierarchy.
11) Describe Power -Efficient Gathering in Sensor Information Systems.
12) Write a note on Directed Diffusion.
13) What is Geographical Routing?

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Unit Structure
5.0 Objectives
5.1 Introduction
5.2 Traditional Transport Control Protocols
5.2.1 TCP
5.2.2 UDP
5.2.3 Mobile IP
5.3 Transport Protocol Design Issues
5.4 Examples of Existing Transport Control Protoco ls
5.4.1 CODA (Congestion Detection and Avoidance)
5.4.2 ESRT (Event -to-Sink Reliable Transport)
5.4.3 RMST (Reliable Multi segment Transport)
5.4.4 PSFQ (Pump Slowly, Fetch Quickly)
5.4.5 GARUDA
5.4.6 ATP (Ad Hoc Transport Protocol)
5.5 Performance of Transport Control Protocols
5.5.1 Congestion
5.5.2 Packet loss recovery
5.6 Summary
5.7 List of References
5.8 Unit End Exercises
5.0 OBJECTIVES
 To understand the different traditional transport controlprotocol
 To get familiar with the design issu es, examples and performance of
Transport Control Protocols
5.1 INTRODUCTION
Physical, data link, network (or internetworking), transport, and other
higher levels like session, presentation, and application make up the
architecture of computer and communic ation networks. For its immediate
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91 provider. Via service access points, neighboring layers communicate with
one another (SAPs). For instance, the network layer, which sits directly
above the link layer, receives link services from the data link layer. The
transport layer, which is the layer above it, receives addressing and routing
services from the network layer, while the layers above it receive message
transportation services from the t ransport layer. In this architecture,
practically all nodes only include the lower three layers.Yet, the transport
and the layers above it only exist at end points or hosts and carry out end -
to-end protocol operations.
End-to-end segment transportation is offered by the transport layer, in
which messages are split up into a series of segments at the source and
then put back together again at the destination nodes. The procedures
utilized to deliver the segments to the target nodes and/or the underlying
delivery protocol structures are not concerns of the transport layer.
Examples of transport protocols include the user datagram protocol
(UDP), the sequenced packet exchange protocol (SPX), the transport
control protocol (TCP), and NWLink (Microsoft's implemen tation of
IPX/SPX). The Internet frequently uses TCP and UDP. TCP can be
classified as either connection -oriented and connectionless.
5.2 TRADITIONAL TRANSPORT CONTROL
PROTOCOLS
5.2.1 TCP
On the Internet, TCP is the most widely used connection -oriented
transport control protocol. TCP is where some applications, such HTTP
and FTP, are located. In order to provide dependable, orderly,
controllable, and elastic transmission, TCP makes advantage of network
services offered by the IP layer. TCP operation is div ided into three
stages:
1) Connection establishment: During this stage, a logical connection for
TCP is formed. A logical connection is an association between a TCP
sender and recipient that can be uniquely identified by their IP
addresses and TCP port number s. There could be many connections
active between endpoints at once. These connections will have
separate TCP port numbers despite sharing the same IP address. A
three -way handshake is used by TCP to establish a connection.The
TCP sender and receiver will negotiate parameters like the beginning
sequence number, window size, and others during the handshake and
will let each other know when data transmission can start.
2) Data transmission: TCP enables dependable and well -organized
information transfer between t he sender and the recipient. When a
segment is lost, TCP utilizes (accumulative) ACK to find it. The
segment header's sequence number allows for an ordered transmission.
TCP also supports flow control and congestion control with sender -
adjustable transmiss ion rates. This task is carried out via TCP using a
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92 called cwnd (congestion window). The maximum number of segments
that the TCP sender can send is cwnd. After receiving an ACK from
the receiver or following a timeout, cwnd is updated. Both flow
control and delivery notification are performed using ACK, therefore
the two tasks are somewhat intertwined.
3) Disconnect: The connection will be cut off and the relevant resource
released once the data tra nsmission is finished.
Via cwnd, TCP controls its flow and congestion. The procedure is divided
into three stages:
1) Slow start: All transmissions begin slowly by default. For each ACK
that is received during this phase for a segment that was transmitted,
the cwnd rises by one.If ACK is not received because of segment loss,
cwnd consequently rises.
2) Congestion avoidance: The system enters the congestion avoidance
state when cwnd reaches a maximum value (threshold). After each
ACK is received in this condition, cwnd is only increased by 1/cwnd.
The sender keeps track of time for each segment sent. The system
enters the slow start phase once more, the threshold is set to half of the
current cwnd, the segment timer is doubled, and the cwnd is reset if
the timer en ds before an ACK corresponding to the segment is
received. Round -trip time (RTT), which is determined by the ACK, is
used to update the timer. A segment has been lost during transmission
if the sequence number acknowledged in two consecutive ACKs is in
sequence.In this scenario, cwnd will be cut in half at the same time
that the system status switches to fast recovery and fast retransmission
(FRFT).
3) FRFT: The same technique that updates cwnd in the congestion
avoidance state is also employed in the rapid re covery and fast
retransmission state. There is no need to reset cwnd because, usually
speaking, random segment loss does not necessarily indicate that there
is high congestion.A timeout, however, typically denotes significant
traffic volume and/or a broken link.
TCP mechanisms provide adaptable flow and congestion control as a
result. As can be seen from the foregoing, (1) high throughput results from
cwnd increasing quickly and oscillating around a big value when there is
little to no congestion and few se gment losses. On the other hand, if cwnd
has a low value, the TCP throughput will be low. (2) When RTT is low,
ACKs are received rapidly, and cwnd rises similarly quickly. The sender
will consequently experience significant throughput. (3) It goes without
saying that high throughput also results from big segment sizes. Moreover,
the theoretical analysis of TCP confirms these.
5.2.2 UDP
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93 datagrams without a sequence number between the transmitter and the
receiver. It cannot guarantee ordered transmission because the datagrams
do not include a sequence number. Moreover, it lacks features for flow
control or congestion. As UDP does not perform congestion or flow
control, it can end up outperforming TCP in situations where both
protocols are active. A TCP -friendly rate control (TFRC) for UDP has
been suggested in recent years to implement a certain amount of control in
this protocol.When TCP and UDP are available on a connection, the
fundamental principle underlying TFRC is to deliver almost comparable
throughput to both protocols.
5.2.3 Mobile IP
In order to offer terminal mobility in an all -IP network, mobile IP is
prese nted as a global mobility management solution at the network layer.
TCP/early IP's design did not take mobility into account. Currently, the IP
address serves as both a terminal identity and a network location identifier
for terminals. Addresses are also u tilized throughout the routing
procedure. To separate the two, however, there must be some sort of
process. Two new entities and one new IP address are introduced by
mobile IP, which is intended to alleviate this issue.The two new entities
are 1] the home agent (HA), which is situated in the home network of the
mobile terminal and is in charge of managing its IP addresses and packet
forwarding, and 2] the foreign agent (FA), which is situated in the network
that the mobile terminal visits. HA and FA can be addressed worldwide
and have static IP addresses.Care of address (COA), the IP address
acquired from FA after the mobile device enters a new network, is the new
IP address introduced for mobility.
When a terminal enters a new network, it registers with the FA of the new
network and then gets a COA. This is how mobile IP works. The COA is
now communicated to the terminal's HA by either the terminal or the FA.
The HA will then pass the packets to the mobile terminal's COA when a
related terminal sends data to the mobile terminal. Direct packet
transmission occurs from the mobile terminal to the matching
terminal.The triangular routing method, which results in a longer path
from the corresponding terminal to the mobile and, thus, low efficiency, is
hence an asy mmetrical routing procedure between the two terminals. Even
though the physical link may have enough bandwidth, the TCP sender is
obliged to drop its rate during the mobility process since handoff comes as
a result of movement and may result in packet loss and TCP timeout.
5.3 TRANSPORT PROTOCOL DESIGN ISSUES
WSNs should be developed with consideration for energy conservation,
traffic control, data dissemination reliability, security, and management.
These challenges can be studied independently in each lay er or
cooperatively across layers and frequently include one or more tiers of the
hierarchical protocol. Congestion control, for instance, might just affect
the transport layer, whereas energy conservation might affect the physical,
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94 control and loss recovery are the two primary objectives of the design of
transport control protocols. Finding the beginning of congestion and
pinpointing its location and timing is necessary for congestion co ntrol.For
instance, monitoring node buffer occupancy or link load can be used to
find congestion (such as wireless channel). Selective packet dropping at a
congestion point, as in active queue management (AQM) schemes, rate
adjustment at the source node, a s in the technique of additive increase
multiplicative decrease (AIMD) in TCP, and the use of routing techniques
are some of the methods used to control congestion in the traditional
Internet. Because sensors have limited resources, it is important to
carefully evaluate how to identify congestion and how to avoid it in
WSNs.These protocols must take into account energy conservation,
simplicity, scalability, and strategies for extending the lifespan of sensor
batteries.One may, for instance, use an end -to-end mechanism, like the
one found in TCP, or hop -by-hop backpressure, like the kind used in
frame relay networks or the asynchronous transfer mode (ATM).End -to-
end strategies are highly straightforward and reliable;however, they could
increase network traffi c. Yet, hop -by-hop methods typically identify
congestion rapidly and add less extra network traffic as a result. While
developing congestion control algorithms for WSNs, significant
consideration should be given to the trade -off between end -to-end and
hop-by-hop processes due to energy constraints at the sensors.
In wireless sensor networks, packet loss is typically brought about by poor
wireless channel quality, sensor failure, and/or congestion. In order to
convey accurate information, WSNs need to ensure a particular level of
reliability at the application or packet level through loss recovery. Packet -
level dependability is necessary for certain essential applications since
they depend on the reliable transmission of every packet. Application
reliability is more significant than packet -level reliability since certain
applications only require a proportionately reliable transfer of packets.
Wireless sensor networks can detect packet loss using the same
conventional techniques as packet -switched networks.Eac h packet, for
instance, may carry a sequence number, and a receiver may use sequence
numbers to detect packet loss. Using an end -to-end or hop -by-hop control,
ACK and/or NACK can be used to recover lost packets after packet loss
has been detected. Efficien cy is maintained in terms of energy if there
aren't many packets in transit and few retransmissions are needed. Less in -
transit packets may be produced as a result of effective congestion control.
Fewer retransmissions occur as a result of an efficient los s recovery
strategy. In conclusion, the issue of transport control protocols for sensor
networks essentially comes down to energy efficiency.The following
elements must to be taken into account while designing transport protocols
for WSNs:
1) Regulate traffic flow and ensure data delivery reliability: Since the
majority of the data come from the sensor nodes to the sink, there may
be congestion around the sink.Although the MAC protocol can recover
packets that have been lost due to bit errors, it is unable to handle
packets that have been lost due to buffer overflow. A packet loss
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95 TCP, is required for WSNs.Furthermore, whereas in traditional
networks every packet's accurate transmission is ensured , trustworthy
delivery with WSNs may mean something else.For some sensor
applications, WSNs simply need to reliably receive packets from a
portion of the local sensors, not from every sensor node. The design of
WSN transport protocols may benefit greatly f rom this discovery.
Also, as it may reduce packet loss and hence increase energy
conservation, using a hop -by-hop strategy for congestion control and
loss recovery may be more successful. The intermediary nodes' need
for buffers can be reduced by the hop -by-hop technique.
2) In order to hasten connection establishment, increase throughput, and
decrease transmission latency, transport protocols for wireless sensor
networks should be made simpler or use a connectionless protocol.
The majority of WSN applications are reactive, which means they
observe passively and hold data until an event occurs before delivering
it to the sink. Due to an occurrence, these programs might only need to
send a few packets.
3) As packet loss results in energy loss, WSN transport techniq ues should
minimize packet loss. The transport protocol should employ active
congestion control (ACC) to prevent packet loss at the cost of slightly
lower connection usage. Congestion avoidance is started by ACC
before congestion actually happens.As an ill ustration of ACC, the
sender (or intermediary nodes) may lower its sending (or forwarding)
rate when the downstream neighbors’ buffer size rises above a
particular threshold.
4) Fairness for a range of sensor nodes should be ensured by the transport
control p rotocols.
5) A transport protocol should, if at all possible, be created with cross -
layer optimization in mind. For instance, if a routing algorithm alerts
the transport protocol of a route failure, the protocol can infer that
packet loss is not due to conges tion but rather to the failure of the route
rather than congestion. In this scenario, the sender is free to stick with
its current rate.
5.4 EXAMPLES OF EXISTING TRANSPORT
CONTROL PROTOCOLS
Examples of several transport protocols designed for WSNs are sh own in
Table 1. Most examples can be grouped in one of the four groups:
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5.4.1 CODA (Congestion Detection and Avoidance)
The three components of CODA, an upstream congestion control method,
are closed -loop end -to-end multisource regulation, open -loop hop -by-hop
backpressure, and congestion detection. By keeping track of wireless
channel load and current buffer occupancy, CODA ma kes an effort to
identify congestion. When buffer occupancy or wireless channel load
surpasses a certain level, congestion is assumed to have taken place.Using
an open -loop hop -by-hop backpressure, the node that has detected
congestion will then alert its upstream neighbor to lower its rate. The
upstream neighbor nodes use techniques like AIM to reduce their output
rate.Finally, CODA regulates a multisource rate through a closed -loop
end-to-end approach, as follows: (1) When a sensor node exceeds its
theore tical rate, it sets a ‘‘regulation’’ bit in the ‘‘event’’ packet; (2) If the
event packet received by the sink has a ‘‘regulation’’ bit set, the sink sends
an ACK message to the sensor nodes and informs them to reduce their
rate; and (3) if the congestion is cleared, the sink will send an immediate
ACK control message to the sensor nodes, informing them that they can
increase their rate. CODA’s disadvantages are its unidirectional control,
only from the sensors to the sink; there is no reliability considera tion; and
the response time of its closed -loop multisource control increases under
heavy congestion since the ACK issued from the sink will probably be
lost.
5.4.2 ESRT (Event -to-Sink Reliable Transport)
ESRT which provides reliability and congestion contr ol, belongs to the
upstream reliability guarantee group. It periodically computes a reliability
figure (r), representing the rate of packets received successfully in a given
time interval. ESRT then deduces the required sensor reporting frequency
(f) from the reliability figure (r) using an expression such as f = G(r).
Finally, ESRT informs all sensors of the values of (f) through an assumed
channel with high power. ESRT uses an end -to-end approach to guarantee
a desired reliabilityfigure through adjusting the sensors’ reporting
frequency. It provides overall reliability for the application. The additional
benefit of ESRT is energy conservation through control of reporting
frequency. Disadvantages of ESRT are that it advertises the same
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97 contributed differently to congestion, applying different frequencies would
be more appropriate) and considers mainly reliability and energy
conservation as performance measures.
5.4.3 RMST (Reliable Multisegme nt Transport)
The upstream direction of packet transmission is guaranteed by RMST.
Either intermediate nodes operate in noncache mode, where only end hosts
cache the sent packets for end -to-end recovery, or they cache each packet
to enable hop -by-hop recov ery. Both cache and noncache modes are
supported by RMST. Moreover, for loss detection and alerting, RMST
employs timer -driven and selective NACK techniques. Lost packets are
tracked down in the cache mode hop by hop using the intermediary sensor
nodes. Th e NACK will be forwarded upstream towards the source node if
an intermediate node is unable to find the lost packet or if it is operating in
noncache mode. In order to assure application reliability, RMTS is created
to run above the routing protocol direct ed diffusion.Problems with RMST
are lack of congestion control, energy efficiency, and application -level
reliability.
5.4.4 PSFQ (Pump Slowly, Fetch Quickly)
By pacing data at a reasonably moderate rate and allowing sensor nodes
that suffer from data loss to recover any missing segments from close
neighbors, PSFQ distributes data from sink to sensors. This strategy is a
part of the downstream reliability guarantee category. The goal is to
localize data recovery among close neighbors in order to minimize los s
recovery and achieve loose delay bounds. Pump, fetch, and report are the
three processes that make up PSFQ. PSFQ functions as follows: Until all
of the data fragments have been sent, Sink broadcasts a packet to its
neighbors every T time units. The senso r node enters fetch mode when a
sequence number gap is found and sends a NACK in the reverse path to
retrieve the lost fragment.Unless the number of times the NACK is sent
exceeds a set limit, the neighbor nodes do not relay the NACK. Lastly,
using a strai ghtforward and scalable hop -by-hop report method, the sink
can request information from sensors regarding the status of data delivery.
The following drawbacks of PSFQ: Its slow pump causes a significant
delay, it cannot detect packet loss for single packet transmission, and its
hop-by-hop recovery with cache requires bigger buffer sizes.
5.4.5 GARUDA
The downstream reliability category includes GARUDA. It is built on a
two-tier node design, and core sensor nodes are chosen from nodes that
are 3i hops away f rom the sink (i is an integer). Second -tier nodes are the
noncore nodes that are still present. A nearby core node is selected by each
noncore sensor node to serve as its core node. Core nodes are used by
noncore nodes to recover lost packets. GARUDA detec ts and notifies
losses via a NACK message. Loss recovery is done in two different ways:
between core sensor nodes and between noncore sensor nodes and their
core node.Retransmission to recover lost packets appears to be a
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98 ensure the success of single or first packet delivery, GARUDA designs a
repeating wait for first packet (WFP) pulse transmission. In order to create
a two -tier node architecture, the hop number and core sensor node s are
also computed and chosen via pulse transmission. Inconsistency in the
upstream direction and a lack of congestion control are two drawbacks of
GARUDA. At the time of this writing, GARUDA's published results were
devoid of any reliability findings or performance evaluations against
alternative algorithms like PSFQ.
5.4.6 ATP (Ad Hoc Transport Protocol)
A receiver and network -assisted end -to-end feedback control algorithm
underlies ATP's operation. It makes use of selective ACKs (SACKs) to
recover from packet loss. The sum of exponentially distributed packet
queuing and transmission delay, or D, is computed by intermediate
network nodes in ATP. The inverse of D is chosen as the needed end -to-
end rate. The values of D are calculated over all packets that pass through
a certain sensor node, and if they are greater than the value piggybacked in
each outgoing packet, the field is updated before the packet is
forwarded.Inverse of D is calculated by the receiver and fed back to the
sender to determine the neces sary end -to-end rate. As a result, the
transmitter is able to intelligently modify its transmission rate based on the
value obtained from the receiver. Selective ACKs (SACKs) are used by
ATP as an end -to-end technique for loss detection to ensure reliabili ty.
Because ATP separates congestion control from reliability, it outperforms
TCP in terms of fairness and throughput. The question of whether ATP is
best for an end -to-end control method is raised by the fact that energy
concerns are not taken into accoun t for this design.
5.5 PERFORMANCE OF TRANSPORT CONTROL
PROTOCOLS
This section compares the performance of WSN congestion and loss
quantitatively. Energy consumption, which is calculated for end -to-end
and hop -by-hop situations, is the parameter used to compare congestion.
Loss performance is a different metric that is dependent on cache and
noncache methods.
5.5.1 Congestion
End to end and hop by hop are two common methods for reducing
congestion. The source node must identify congestion in either the
receiver -assisted (ACK -based loss detection) mode or the network -
assisted mode in an end -to-end protocol like standard TCP (using explicit
congestion notification). Rate modifications therefore only take place at
the source node. In hop -by-hop congestion con trol, intermediate nodes
alert the originating connection node when there is congestion. Hop -by-
hop control may be able to clear congestion more quickly than the end -to-
end method while also lowering packet loss and energy usage in sensor
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99 Here, a st raightforward model is presented to assist in understanding how
congestion control affects energy efficiency. We make the following
presumptions:
 Between sources and sink nodes, there are h > 1 hops, and each hop
causes a delay d. C is the link capacity.
 The network experiences uniform congestion. The frequency of
congestion is f, and it depends on the buffer size, traffic patterns, and
network topology.
 Congestion will be noticed when the total rate of source transmission
reaches C(1+a).
 The average amount of energy needed to send or receive a packet over
each link is e.
With the end -to-end strategy, 1.5hd is often needed to alert the source of
the beginning of congestion. All nodes can send up to C(1+a)(1.5hd)
packets during this window (between the time t hat congestion occurs and
the source is alerted), with the exception of the congested link, when
traffic is limited to C(1.5hd). As a result, n e=a.C (1.5hd) can be used to
estimate the number of packets lost in this situation as a result of
congestion.
The time needed to start congestion control corresponds to merely a single
hop's worth of delay (d) in the hop -by-hop method. As a result, before
congestion is reduced, packet loss is roughly equal to n b = aCd.
Let Nd(T) represent the number of packets droppe d owing to congestion
during the time interval T, and let Ns(T) represent the number of packets
successfully transmitted via the congested network. Each dropped packet
has made 0.5H hops on average. The energy effectiveness of a congestion
control device i s defined as

where Ec is the mean energy ratio required to send one packet
successfully. In ideal situations, when there is no congestion, Ec would be
1. Therefore, for end -to-end congestion control,

The two equations above show that an end -to-end mec hanism's energy
efficiency depends on the path length (H), but hop -by-hop control is
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100 The ratio of all packets discarded in the sensor network to all packets
received at the sink for hop -by-hop congestion control is the energy tax,
according to CODA. The lower ratio therefore denotes more energy
efficiency. Figure 1 shows CODA’s energy efficiency.

Figure 1: Energy tax in CODA as a function of network size for high - and
low-data-rate traff ic. The difference between the data points with and
without CODA indicates the energy saving achieved by CODA
5.5.2 Packet loss recovery
How to recover lost packets is the issue we address in this section.Cache
and noncache recovery are typically the two a pproaches that are available
for this purpose. A similar end -to-end ARQ (automatic repeat request) to
the conventional TCP is noncache recovery. Using a hop -by-hop
methodology, cache -based recovery relies on retransmissions between two
nearby nodes and cac hing at the intermediate nodes. Therefore,
retransmissions may happen in h hops in the noncache situation,
necessitating greater overall energy.The node that replicates transmitted
packets locally for a predetermined amount of time is referred to as the
cache point, while the node that packets are dropped due to congestion is
referred to as the loss point. The length of the retransmission path, or l p,
will be defined as the quantity of hops between the caching node and the
node where the loss occurs. l p = h1, where h1 is the number of hops from
the loss point to the source node, in the noncache case.If lost packets are
located on nearby nodes in the cache example, l p can be 1. Packet copies
can only be stored for a finite amount of time because sensor nodes have a
finite amount of buffer space. Because of this, l p in the cache scenario may
be greater than 1 but lower than h1(1cache -based recovery may introduce varied energy efficiency and have
different retransmission path len gths (l p).
By using cache -based recovery, each packet is kept at each intermediate
node it passes through until it is successfully received by its neighboring
node or until a timeout occurs (whichever is sooner). In this instance, l pis
probably extremely n ear to 1. Distributing caching is a different technique
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101 or a few intermediate nodes store each packet.In addition to using less
buffer space than traditional caching, distributed cachin g may have a
longer l p than traditional caching (but still be smaller than in the noncache
situation).
The performance of several loss recovery strategies that might offer
dependability via the connection, transport, and application levels were
examined by RMST.The effectiveness of end -to-end loss recovery and
hop-by-hop loss recovery in the transport layer is compared in Figure 2
from. The comparison is based on how many transmissions are necessary
to send 10 packets over a network in 10 hops. This graph d emonstrates
how the number of end -to-end retransmissions doubles when the success
rate falls below 0.95, which reduces energy efficiency.

Figure 2: Hop -by-hop versus end -to-end: number of transmissions
required to send 10 packets in 10 hops
5.6 SUMMARY
We gave a general overview of the wireless sensor network transport
control protocol in this chapter. The drawbacks of the TCP and UDP
protocols were examined, along with the reasons why they weren't
appropriate for wireless sensor networks. Also, a study o f various sensor
transport control protocols that are now in use was given, along with a list
of issues with those protocols. Designing transport control methods for
wireless sensor networks requires careful consideration of the following
points:
1. The effec tiveness of protocols and the effectiveness of congestion -
control techniques.Efficient techniques provide high throughput while
minimizing packet loss.
2. The reliability of the transport layer, the need for loss recovery at the
transport layer, and the most efficient and effective mechanism.Any
such techniques should ideally have minimal buffering requirements.
3. Fairness between sensor nodes located at various ranges from the sink.
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102 5.7 LIST OF RE FERENCES
1) Protocols and Architectures for Wireless Sensor Network, Holger
Kerl, Andreas Willig, John Wiley and Sons, 2005
2) Wireless Sensor Networks Technology, Protocols, and
Applications,Kazem Sohraby, Daniel Minoli and TaiebZnati, John
Wiley & Sons, 2007
3) Mobile communications, Jochen Schiller,2nd Edition, Addison wisely,
Pearson Education,2012
4) Fundamentals of Wireless Sensor Networks, Theory and Practice,
WaltenegusDargie, Christian Poellabauer, Wiley Series on wireless
Communication and Mobile Computin g, 2011
5) Networking Wireless Sensors, Bhaskar Krishnamachari, Cambridge
University Press, 2005
5.8 UNIT END EXERCISES
1) Explain: i] TCP ii] UDP.
2) Describe Mobile IP.
3) Illustrate and explain Transport Protocol Design Issues.
4) Explain the examples of Existing T ransport Control Protocols
5) Write a note on CODA (Congestion Detection and Avoidance).
6) Explain ESRT (Event -to-Sink Reliable Transport).
7) Discuss the RMST (Reliable Multi segment Transport).
8) What is PSFQ (Pump Slowly, Fetch Quickly)?
9) Explain GARUDA.
10) What is A TP (Ad Hoc Transport Protocol)?
11) Describe the performance of Transport Control Protocols.
12) What do you mean by Congestion?
13) Explain the concept of Packet loss recovery.
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INTRODUCTION, WIRELESS
TRANSMISSION AND MEDIUM
ACCESS CONTROL
Unit Structure
6.0 Objectives
6.1 Introduction
6.2 Applications
6.3 A short history of wireless communication.
6.4 Wireless Transmission: Frequency for radio transmi ssion
6.5 Signals
6.6 Antennas
6.7 Signal propagation
6.8 Multiplexing
6.9 Modulation
6.10 Cellular systems
6.11 Summary
6.12 List of References
6.13 Unit End Exercises
6.0 OBJECTIVES
 To get familiar with wireless transmission and medium access con trol
 To get acquaint with the signaling and propagation involved and
associated with the wireless transmission
6.1 INTRODUCTION
In ten years, what will computers look like? No one can forecast the future
with absolute certainty, but most computers will und oubtedly be portable.
How will consumers utilize computers or other communication tools to
access networks? a growing number wirelessly, that is, without any wires.
How will people spend the most of their time while on vacation at work?
Numerous people wil l be mobile, which is currently one of the main
features of contemporary civilization. Consider an aircraft with 800 seats,
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104 network connection, and future aircraft will provide simple Inter net
access.
In this case, the only method of transferring data to and from passengers
will be a mobile network travelling at a high rate of speed above ground
and connected by a wireless link. Consider vehicles with Internet
connection and countless embedd ed processors that must interact with
devices like cameras, cell phones, CD players, headsets, keyboards,
intelligent traffic signs, and sensors. This wide range of tools and
programmes demonstrates the current importance of mobile
communications.
The defi nitions of the terms "mobile" and "wireless" as they are used
should be given before showing more applications. User mobility and
device portability are two different types of mobility. The term "user
mobility" describes a user who has access to the same o r equivalent
telecommunication services at various locations; in other words, the user
is mobile and the services follow them. Simple call -forwarding solutions
from the telephone or computer desktops that enable roaming (i.e., have
the same appearance no m atter which computer a user logs into the
network with) are examples of systems that support user mobility.
When a communication device is portable, it can be moved (with or
without a user). To ensure that communication is still feasible when the
device is moving, numerous procedures both inside the device and in the
network must be in place. The mobile phone system is a common
illustration of a system that supports device portability, as the system
automatically switches the device from one radio transmitt er (also known
as a base station) to another if the signal deteriorates. Most of the
scenarios include simultaneous user mobility and gadget portability.
The word "wireless" is applied to gadgets. This only explains how to
connect to a network or other com munication partners without using a
wire. Transmission of electromagnetic waves through "the air" takes the
role of the cable (although wireless transmission does not need any
medium).
6.2 APPLICATIONS
Although wireless networks and mobile communications c an be
advantageous for many applications, some application settings appear to
be tailor -made for their use. Some of them are included in the sections
below:
1] Vehicles
While some already exist in today's cars, there will be many more wireless
communicatio n systems and mobility -aware applications in cars of the
future. Digital audio broadcasting (DAB) with 1.5 Mbit/s allows for the
reception of music, news, traffic updates, weather forecasts, and other
broadcast information. A universal mobile telecommunica tions system
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105 available for personal communication. Satellite communication can be
employed in remote places, and the global positioning system is used to
identify the car's current location. (GPS). To transmit information quickly
in an emergency or to preserve a safe distance from one another, nearby
cars create a small ad hoc network.In the event of an accident, not only
will the airbag deploy, but a provider will also receive an emergency c all
alerting the police and ambulance service. This technology is already in
some cars. In the future, vehicles will communicate with one another via
an ad -hoc network in order to alert them about accidents and help them
slowdown in time, even before a dri ver is aware of one. Already, trains,
trucks, and buses send maintenance and logistical data to their base of
operations, improving fleet management and saving time and money.
A typical setup for mobile communications including numerous wireless
devices is shown in Figure 1. Mobile phone networks (GSM, UMTS) and
trunked radio systems (TETRA) will connect to networks with a fixed
infrastructure to form wireless LANs. (WLAN). Additionally, satellite
communication lines may be utilized. It's more likely that t he networks
inside each automobile and those between cars would operate
haphazardly.Personal digital assistants (PDA), computers, and mobile
phones, such as those connected via Bluetooth, can all be a part of
wireless pico networks within a car.

Consider instances where there is train or air travel. Here, speed can lead
to a variety of issues. While trains and contemporary aeroplanes may
move at speeds of up to 900 km/h and 350 km/h, respectively, many
technologies cannot function if a mobile device's rel ative speed is greater
than, for example, 250 km/h for GSM or 100 km/h for AMPS. Only a few
technologies, such as DAB, are capable of speeds of up to 900
km/h(unidirectional only).
2] Emergencies
Just consider the advantages of an ambulance having a reliab le wifi
connection to a medical facility. From the accident scene, critical
information regarding injured people can be conveyed to the hospital. For
this specific accident type, the essential preparations can be made, and
professionals can be consulted fo r an early diagnosis. In the event of a
natural disaster, such as a hurricane or earthquake, wireless networks are
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106 hoc networks survive in the worst scenarios. In addition to the reg ular
cable telephone system failing, all mobile phone systems needing base
stations also fail if all cabling fails.
3] Business
Today's travelling salesperson requires immediate access to the company's
database to make sure that the files on his or her lap top represent the
current situation, to allow the business to monitor all of its travelling
employees' activities, to maintain consistent databases, etc. The laptop can
become a truly mobile office with wireless connection, but effective and
strong synchro nization techniques are required to guarantee data
consistency. Figure 2 depicts what could occur when staff members
attempt to communicate inappropriately.The laptop at home connects to
the Internet using DSL and a WLAN or LAN. When the WLAN coverage
runs out, it is necessary to switch to a different technology, like an
improved version of GSM, before leaving the house.Data rates decrease
when travelling at a higher pace due to interference and other causes. In
addition to gas, some gas stations provide WL AN hot spots.There is
already wifi connectivity available aboard trains. Before getting to the
office, it could be required to switch to a few more different technologies.
Mobile communications should always provide the best access to the
internet, the com pany's intranet, or the telephone network, regardless of
the time and location.

4] Replacement of wired networks
In some circumstances, such as with remote sensors, at trade exhibitions,
or in old buildings, wireless networks can also be used in place of wired
networks. It is frequently impractical to link remote sensors for weather
forecasts, earthquake detection, or to give environmental data due to
financial considerations. In this case, wireless connections, like those
provided by satellite, can be us eful. Tradeshows require a highly flexible
infrastructure, but installing cable takes a lot of time and often proves to
be excessively rigid. WLANs frequently take the place of cabling at
computer shows.Computers, sensors, or information displays in histor ic
structures are additional uses for wireless networks since excessive
cabling could damage priceless walls or flooring. The use of wireless
access points in a room corner may be the answer.
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107 5] Infotainment and more
Internet everywhere? Not if wireless n etworks are absent! Think about a
city's travel guide. Static data can be downloaded from a CD -ROM, DVD,
or even the Internet at home. However, wireless networks can deliver
current information at any suitable location. By determining your location
via GPS , a local base station, or triangulation, the tour guide may provide
you with information on a building's past while simultaneously
downloading details about a concert that will be taking place there that
same night over a local wireless network. You can s elect a seat, pay using
electronic money, and email these details to a service provider. In order to
enable, for instance, ad -hoc gaming networks as soon as players meet to
play together, entertainment and games are a rising area of wireless
network applic ations.
6] Location dependent services
Many research projects in mobile computing and wireless networks
attempt to conceal the fact that a wireless link is more error prone than a
connected one or that network access has changed (for example, from
mobile p hone to WLAN or between various access points). Many
protocols attempt to improve link quality via encoding methods or
retransmission to ensure that applications intended for fixed networks
continue to function. Mobile IP seeks to mask the fact that shifti ng access
points by diverting packets while preserving the same IP
address.However, it is frequently necessary for an application to "know"
anything about the location or the user may require location data for
additional activities. Different services that may depend on the actual
location can be identified include follow -up services, location aware
services, privacy services, information services, and support services.
7] Mobile and wireless devices
Even if there are already a lot of wireless and mobile ga dgets on the
market, there will be a lot more in the future. Such gadgets are not
specifically categorized in terms of size, shape, weight, or computer
capability. Currently, laptops are regarded as being at the top of the mobile
device spectrum.Future mob ile and wireless gadgets will be more potent,
lighter, and equipped with brand -new user and network interfaces. The
energy source is one significant issue that has not yet been resolved. A
device requires more power the more features are integrated into it .
Assuming the same technology, the device's battery life decreases with
increasing performance. Furthermore, energy is used up quickly during
wireless data transfer.
6.3 A SHORT HISTORY OF WIRELESS
COMMUNICATION
The history of Wireless Communications st arted with the understanding or
magnetic and electric properties observed during the early days by the
Chinese, Greek and Roman cultures and experiments carried out in the munotes.in

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108 17th and 18th centuries. Here are some selected events in the development
of Wireles s Communications.
1807 – French mathematician Jean Baptiste Joseph Fourier discovered
Fourier’s theorem
1820 – Danish physicist Hans Christian Orsted discovered the
electromagnetic field caused by electric current. The French physicist
Dominique Francois J ean Arago showed that a wire became a magnet
when current flowed through it. French mathematician and physicist
Andre -Marie Ampere discovered electrodynamics and proposed an
Electromagnetic Telegraph.
1831 – British scientist Michael Faraday discovered ele ctromagnetic
induction and predicted existence of electromagnetic waves.
1834 – American inventor Samuel Finley Breese Morse invented the code
for telegraphy named after him.
1847 – German physiologist and physicist Hermann Ludwig Ferdinand
von Helmholtz s uggested electrical oscillation
1853 – William Thomson (Lord Kelvin) calculated the period, damping
and intensity as a function of the capacity, self -inductance and resistance
of an oscillatory circuit.
1857 – Feddersen verified experimentally the resonant frequency of a
tuned circuit as suggested by Helmholtz in 1847.
1864 – Scottish mathematician and physicist James Clerk Maxwell
formulated the electromagnetic theory of light and developed the general
equations of the electromagnetic field. He formulated 20 equations that
were later simplified into the 4 basic equations we use today.
1866 - American dentist Dr. Mahlon Loomis described and demonstrated
a wireless transmission system which he patented in 1866. Loomis
demonstrated the transmission of signals between two mountains, a
distance of 22 km.
1882 – American physicist, Amos Emerson Dolbear, was granted a patent
for a wireless transmission system using an induction coil, microphone
and telephone receiver and battery. Nathan Stubblefield transmitted aud io
signals without wires.
1883 – Irish physicist and chemist George Francis FitzGerald published a
formula for the power radiated by a small loop antenna.
1884 – German physicist Heinrich Rudolf Hertz wrote Maxwell’s
equations in scalar form by discarding the concept of aether reducing it
from 20 to 12 equations.
1885 – Thomas Edison patented a system of wireless communication by
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109 1886 – Heaviside introduced impedance as the ratio of voltage over
current. Hertz started his work to de monstrate the existence of radio waves
and published his results in 1888.
1887 – English physicist Oliver Joseph Lodge discovered Sympathetic
Resonance (standing waves) in wires.
1888 – Hertz produced, transmitted, and received electromagnetic waves
(5 m t o 50 cm) using reflectors to concentrate the beam. Hertz also
discovered the principle for Radar. Heaviside wrote Maxwell’s equations
in vector form – the four equations we use today. Italian Galileo Farrari
and Croatian -American Nilola Tesla independentl y produced rotating
fields using 2 -phase currents. Austrian engineer Ernst Lecher established
the relation between frequency, wire length, velocity of propagation and
the electrical constants of the wire.
1890 – Lecher used standing waves produced in paral lel wires to measure
frequency. Tesla introduced high frequency currents in therapeutics as he
observed that current of high frequency could raise the temperature of
living tissue. Tesla also patented his Tesla Coil which was used later in
every spark gap generator to produce high frequency signals. Heinrich
Rubens and R. Titter made a sensitive bolometer which measured the
intensity of electromagnetic waves by means of the heat generated in a
thin wire.
1893 – English physicist Joseph John Thomson publishe d the first
theoretical analysis of electric oscillations within a conducting cylindrical
cavity of finite length suggesting the possibility of wave propagation in
hollow pipes (waveguides). Hertz conducted experiments of EM shielding
and for coaxial confi guration.
1895 – Marconi transmitted and received a coded message at a distance of
1.75 miles near his home in Bologna, Italy. Indian physicist, Sir Jagadis
Chunder Bose generated and detected wireless signals and produced many
devices such as waveguides, horn antennas, microwave reflectors and
more.
1897 – Marconi demonstrated a radio transmission to a tugboat over an 18
mile path over the English Channel. The first wireless company, Wireless
Telegraph and Signal Company was founded – they bought most of
Marconi’s patents. Lord Rayleigh suggests EM wave propagation in
waveguides and analysis of propagation through dielectrically filled
waveguides. Lodge patented various types of antennas.
1899 – Marconi sent the first international wireless message from Dov er,
England to Wimereux, France.
1900 – Tesla obtained patents on System of Transmission of Electrical
Energy which the US recognized as the first patents on Radio. Tesla is the
first person to describe a system of determining the location of an object
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110 1902 – Fessenden patented the Heterodyne receiver. American Cornelius
D. Ehret filed patents covering the transmission and reception of coded
signals or speech (Frequency Modulation – FM). Poulsen was the first to
develop the CW tra nsmitter.
1903 – Marconi established a transmission station in South Wellfleet, MA
– the dedication included exchanges of greetings between American
President Theodore Roosevelt and British King Edward VII. G.
1904 – Frank J. Sprague developed the idea of the printed circuit. W.
Pickard filed a patent application for a crystal detector where a thin wire
was in contact with silicon. It was the central component in early radio
receivers called crystal radios. J. C. Bose was granted a patent on point
contact diodes that were used for many years as detectors in the industry.
Fleming suggested the rectifying action of the vacuum -tube diode for
detecting high frequency oscillation – the first practical radio tube.
1905 – Fessenden invented the superheterodyne ci rcuit.
1906 – Lee de Forest patented the general principle of omni -range using a
rotating radio beam keyed to identify the sector forming 360 degree sweep
of the beam. He also invented the three -electrode valve or vacuum tube
triode that was instrumental i n the development of transcontinental
telephony in 1913. Poulsen transmitted music by wireless using an arc
transmitter with 1 kW of input power and a 200 feet high antenna that was
heard 300 miles away.
1909 – Marconi and Braun shared the Nobel Prize for Physics for their
contributions to the physics of electric oscillations and radiotelegraphy.
1911 – Von Lieben and Eugen Riesz developed a cascade amplifier. Hugo
Germsback, an American novelist, envisaged the concept of pulse radar in
one of his works whe re he proposed the use of a pulsating polarized wave,
the reflection of which was detected by an actinoscope.
1911 – Engineers start to realize that the triode can also be used for
transmitter and oscillator – the three -electrode vacuum tube was included
in designs for telephone repeaters in several countries.
1912 – G. A. Campbell developed guided wave filters. Sinding and Larsen
transmitted TV by wireless using 3 channels. The Institute of Radio
Engineers was formed in the US.
1914 – The German physicist Walter Schottky discovered the effect of
electric field on the rate of electron emission from thermionic -emitters
named after him. Fleming discovered the atmospheric refraction and its
importance in the transmission of EM waves around the Earth. Carl R.
Englund was the first to develop the equation of a modulated wave (AM)
and also discovered the frequencies related to sidebands. Frequency
modulation of a carrier was proposed to accommodate more channels
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111 1915 – Schottky stat ed work on the space -charge -grid tube and a screen
grid tube or Tetrode that achieved good amplification by placing a screen
grid between the grid and the anode.
1916 – Leon m Brillouin and Georges A. Beauvais patented the R -C
coupled amplifier. F. Adcock used open vertically spaced aerials for
direction finding in aircraft and granted British patent.
1918 – Armstrong invented the Superheterodyne Radio Receiver using 8
valves – most receivers still use this design today. Langmuir patented the
feedback ampli fier. E. H. O Shaughnessy development of direction finding
was one of the key weapons in England during WWI – Bellini -Tosi aerials
were installed around the coast to locate transmission from ships and
aircrafts. Louis Alan Hazeltime invented the neutrodyne circuit with tuned
RF amplifier with neutralization.
1919 – Marconi -Osram company developed the U -5 twin -anode full -wave
rectifier. Joseph Slepian filed a patent application for a vacuum tube
electron multiplier. Sir Robert Alexander Watson -Watt patented a device
for radiolocation by means of short -wave radio waves – the forerunner of
the Radar system.
1921 - E. S. Purington made the all -electric frequency modulator. A.W.
Hull invented the Magnetron oscillator operating at 30 kHz and output
power of 8 kW a nd 69 percent efficiency. E. H. Colpitt and O. B.
Blackwell developed modulation of an audio frequency carrier by signals
of lower audio frequency for carrying telephony over wires. S.
Butterworth published a classic paper on HF resistance of single coil
considering skin and proximity effect.
1922 – Walter Guiton Cady invented the piezoelectric (Quartz) crystal
oscillator. The BBC broadcasts is first news program.
1923 – The decibel (1/10th of a bel, after A. G. Bell, inventor of the
telephone) was used to express the loss in a telephone cable. H. W.
Nichols developed point -to-point communication using single side -band
communication. D.C Prince analyzed Class A and Class C amplifiers.
Scottish engineer Antoine Logie Barid built and patented the first practic al
TV. Watson -Watt perfected the radiolocation device by displaying radio
information on a cathode ray oscilloscope telling the radar operator the
direction, distance and velocity of the target. Ralph Vinton Lyon Hartley
showed that the amount of informati on that can be transmitted at a given
time is proportional to the bandwidth of the communication channel. H.
Flurschein filed a patent on radio warning system for use on vehicles.
1924 – J.R. Carson showed that energy absorbed by a receiver is directly
proportional to its bandwidth and extended Lorentz’s reciprocity theory to
EM fields to antenna terminals. Lloyd Espenschied invented the first
radio altimeter. The mobile telephone was invented by Bell Telephone
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112 1925 – First conference on frequency allocation was held in Geneva.
Joseph Tykocinski -Tykociner demonstrated that the characteristics of a
full size antenna can be replaced with sufficient accuracy from
measurements made on a small short wave in the rage of 3 to 6 m.
1926 – L.E. Lilienfield patented the theory of the Field -Effect Transistor.
Japanese engineers Hidetsugu Yagi and Shintaro Uda developed the Yagi
antenna, a row of aerials consisting of one active antenna and twenty
undriven members as a wave ca nal. Hulsenback and Company patented
identification of buried objects using CW radar.
1927 – R. V. Hartley developed the mathematical theory of
communications. Harold Stephen Black of Bell Laboratories conceived
the negative feedback amplifier. A. de Hass studied fading and
independently developed diversity reception system.
1928 – Baird conducted the first transatlantic TV broadcast and built the
first color TV. Nyquist published a classic paper on the theory of signal
transmission in telegraphy. He develo ped the criteria for the correct
reception of telegraph signals transmitted over dispersive channels in the
absence of noise. C.S. Franklin patented the coaxial cable in England to be
used as an antenna feeder.
1929 – L. Cohen proposed circuit tuning by wa ve resonance (resonant
transmission line) and its application to radio reception. H.A. Affel and L.
Espenscheid of AT&T/Bell Labs created the concept of coaxial cable for a
FDMA multi -channel telephony system. K. Okabe made a breakthrough in
cm-waves when operating his slotted -anode magnetron (5.35 GHz). Hans
Erich Hollmann patented the idea of a reflex klystron with his double -grid
retarding -field tube. W.H. Martin proposed the Decibel as a transmission
unit.
1931 – H. diamond and F. W. Dunmore conceived a radio beacon and
receiving system for blind landing of aircraft. H. E. Hollmann built and
operated the first decimeter transmitter and receiver at the Heinrich Hertz
Institute. He called the device the magnetron.
1932 – The word Telecommunication was coin ed and the International
Telecommunications Union (ITU) was formed. George C. Southworth and
J. F. Hargreaves developed the circular waveguide. Karl Jansky
accidentally discovered radio noise coming from outer space giving birth
to radio astronomy. R. Darb ord developed the UHF Antenna with
parabolic reflector.
1933 – Armstrong demonstrated Frequency Modulation (FM) and
proposed FM radio in 1936. C.E. Cleeton and N. H. Williams made a 30
GHZ CW oscillator using a split -anode magnetron.
1934 – The Federal Com munications Commission (FTC) was created in
the US. W.L. Everitt obtained the optimum operating conditions for Class
C amplifiers. F. E. Terman demonstrated a transmission line as a resonant
circuit. German physicist Oskar Ernst Heil applied for a patent o n munotes.in

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113 technology relating electrical amplifiers and other control arrangements
that was the theoretical invention of capacitive current control in FETs.
1935 – C. J. Frank of Boonton Radio Corp demonstrated Q -meter at the
fall meeting of IRE – the ratio of rea ctance to resistance of a coil as its
“Quality Factor” was first suggested about 1926. A French TV transmitter
was installed on top of the Eiffel Tower. Watson -Watt developed and
patented the first practical radar for use in the detection of airplanes in
England. H. E. Hollmann filed a patent for the multi -cavity magnetron
(granted in 1938).
1936 – H. W. Doherty developed a new high efficiency power amplifier
for modulated waves, Doherty amplifier, at Bell Labs. English engineer
Paul Eisler devised the Prin ted Circuit. N. H. Jack patented the semi -rigid
coaxial cable using thin soft copper tube as the outer conductor. Harold
Wheeler used two flat copper strips side by side to make a low loss
transmission line that could be rolled to save space. H. T. Friis a nd A. C.
Beck invented the horn reflector antenna with dual polarization.
1937 – Grote Rober constructed the first radio telescope. W. R. Blair
patented the first anti -aircraft fire control radar. Russell H. Varian and his
brother Sigurd Varian along with William Hansen developed the reflex
Klystron. Alex H. Reeves invented pulse -code modulation for digital
encoding of speech signals.
1938 – E. L. Chaffee determined the optimum load for Class B amplifiers.
IRE published standards on transmitters, receivers and antennas. Claude
Elwood Shannon recognized the parallels between Boolean algebra and
the functioning of electrical switching systems. W. R. Hewlett developed
the Wien -bridge (RC) oscillator. P. H Smith at RCA developed the well
known Smith Chart. N. E. Lindenblad of RCA developed a coaxial horn
antenna. John Turton Randall and Albert Boot developed the cavity
magnetron that becomes the central components to radar systems.
1941 – W. C. Godwin developed the direct -coupled push -pull amplifier
with inverse feedback. Siemens & Halske made the Ge diode – R. S. Ohl
made the Si junction diode. Sidney Warner realized a two -way police FM
radio.
1943 – H. J. Finden developed the frequency synthesizer. Austrian
engineer Rudolf Kompfner developed the traveling wave t ube. C. K.
Chang developed frequency modulation of RC oscillators. C. F. Edwards
developed microwave mixers. H. T. Friis developed noise figures of radio
receivers.
1944 – Harold Goldberg suggested pulse frequency position modulation.
E. C Quackenbush of Amphenol developed the VHF coaxial connectors.
Paul Neil of Bell Labs developed Type N connectors. Maurice Deloraine,
P. R. Adams and D. H. Ranson applied for patents covering switching by
pulse displacement a principle later defined as time -slot interchan ge –
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114 Research Lab developed radar countermeasures (jamming) in the 25 MHz
to 6 GHz range.
1946 – S. L. Ackerman and G. Rappaport developed a radio control
systems for guided missiles. E. M. Willia ms developed the radio
frequency spectrum analyzer.
1947 – G. E. Mueller and W. A. Tyrrel developed the dielectric rod
antenna. John D. Kraus invented the helical antenna. W. Tyrell proposed
hybrid circuits for microwaves, H. E. Kallaman constructed the VS WR
indictor meter.
1948 – W. H. Branttain, J. Bardeen and W. Shockley of Bell Labs built the
junction transistor. E. L. Ginzton and others developed distributed
wideband amplifier using pentodes in parallel. Shannon laid out the
theoretical foundations of digital communications in a paper entitled “A
Mathematical Theory of Communication.” Paine described the BALUN.
1949 – E. J. Barlow published the principle of operation of Doppler Radar.
1950 - J. M. Janssen developed the sampling oscilloscope.
1951 - Charle s Hard Townes published the principle of the MASER
(Microwave Amplification by Stimulated Emission of Radiation). The
Laboratoire Central des Telecommunications in Paris developed the first
model of a time -division multiplex system connecting subscriber l ine by
electronic gates handling amplitude modulated pulses.
1952 – C. L. Hogan demonstrated a microwave circulator.
1955 – R. H. DuHamel and D. E. IsBelll develop the log periodic antenna.
John R. Pierce proposed using satellites for communications. Sony
marketed the first transistor radio.
1957 – Soviet Union launched Sputnik I that transmitted telemetry signals
for about 5 months. German physicist Herbert Kroemer originated the
concept of the heterostructure bipolar transistor (HBT).
1958 – Robert Noyce (Intel) and Jack Kilby (TI) produced the first Si
integrated circuit (IC).
1962 – G. Robert -Pierre Marie patented the wide band slot antenna. S. R.
Hofstein and F. P. Heiman developed MOS IC.
1963 – W. S. Mortley and J. H. Rowen developed surface acoustic wave
(SAW) devices. John B. Gunn of IBM demonstrated microwave
oscillations in GaAs and InP diodes. The Institute of Electrical and
Electronic Engineers (IEEE) was formed by merging the IRE and AIEE.
1964 – R. L. Johnson, B. C. De Loach and B. G. Cohen dev eloped the
IMPATT diode oscillator. COMSAT and INTELSAT started launching a
series of communications satellites that were the building blocks in the
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115 1969 – The first digital radio -relay system went into operation in Japan
using 2 GHz operating frequency. ARPANET was launched (precursor to
Internet).
1971 – Statek began manufacturing and marketing quartz oscillators that
were made using their patented photolithographic process.
1978 – AT&T Bell Labs started testing a mobile telephone system based
on cells.
1980 – CW performance of GaAs MESFET reached 10 W at 10 GHz.
ATLAS I EM pulse simulator was built for testing large aircraft – it was
the largest wooden structure in the world (400 x 105 x 75 m).
1989 – F. Laleari invented the broadband notch antenna
1990 – WWW was developed
6.4 WIRELESS TRANSMISSION: FREQUENCY FOR
RADIO TRANSMISSION
Numerous frequency bands are available for radio transmission. There are
pros and downsides to each frequency band. The frequency range that can
be employed for data transmission is depicted in rough detail i n Figure
3.In the graph, frequencies from 300 Hz to over 300 THz are depicted.
The wavelength λ is directly connected to the frequency by the following
equation:
λ= c/f
where c ≅ 3·108 m/s (the speed of light in vacuum) and f the frequency.
For traditional wired networks, frequencies of up to several hundred kHz
are used for distances up to some km with twisted pair copper wires, while
frequencies of several hundred MHz are used with coaxial cable (new
coding schemes work with several hundred MHz even with twisted pair
copper wires over distances of some 100 m). Fiber optics are used for
frequency ranges of several hundred THz, but here one typically refers to
the wavelength which is, e.g., 1500 nm, 1350 nm etc. (infra -red).
At several kHz, or the very low f requency (VLF) band, radio transmission
begins. These waves are incredibly long. Submarines use low -frequency
(LF) waves because they can travel through water and track the surface of
the earth. These frequencies are still used by some radio stations, for
instance in Germany between 148.5 kHz and 283.5 kHz. The transmission
of hundreds of radio stations often takes place in the medium frequency
(MF) and high frequency (HF) bands using either amplitude modulation
(AM) between 520 kHz and 1605.5 kHz, short wa ve (SW) between 5.9
MHz and 26.1 MHz, or frequency modulation (FM) between 87.5 MHz
and 108 MHz.The frequencies setting the boundaries of these bands are
normally established by national law and vary from one nation to another.
Because of ionosphere reflec tion, short waves are frequently employed for
(amateur) radio transmission throughout the world. Up to 500 kW of munotes.in

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116 transmit power is available, which is significantly more than the 1 W of a
cell phone.
The TV stations follow when we go to higher frequencies. The 174 -230
MHz and 470 -790 MHz very high frequency (VHF) and ultra -high
frequency (UHF) bands are used to transmit traditional analogue TV. This
frequency range is also used for digital audio broadcasting (DAB) (223 –
230 MHz and 1452 –1472 MHz), as well as for planned or installed digital
TV (470 –862 MHz), which reuses parts of the previous analogue TV
channels. In addition, UHF is utilized for analogue mobile phones (450 -
465 MHz), digital GSM (890 -960 MHz, 1710 -1880 MHz), digital cordless
phones (1880 -1900 MHz), 3G cellular networks (1900 -1980 MHz, 2020 -
2025 MHz, 2110 -2190 MHz), and many other applications.
Super high frequencies (SHF) are primarily utilized for fixed satellite
services in the C -band (4 and 6 GHz), Ku -band (11 and 14 GHz), or Ka -
band (betwe en 2 and 40 GHz). (19 and 29 GHz). The extremely high
frequency (EHF) band, which is near to infrared, is where certain devices
are planned. To prevent interference, all radio frequencies are regulated;
for example, German law regulates frequencies between 9 kHz and 275
GHz.
Optical transmission, which is utilized for both wireless communications
and fibre optical networks, is the next step into higher frequencies.For
directed links, such as using laser links to connect several buildings,
infrared (IR) tran smission is employed. IrDA, the most used IR
technology, connects laptops, PDAs, and other devices using wavelengths
between 850 and 900 nm. Finally, wireless transmission has been possible
for thousands of years using visible light. Even while interferenc e makes
light less reliable, it is still helpful since it has human receivers built in.

Figure 3: Frequency spectrum
6.5 SIGNALS
Data is physically represented by signals. The only way for users of a
communication system to exchange data is through the s ignaling of
signals. Data, or bits, are converted into signals and back again via Layer 1
of the ISO/OSI basic reference model. Signals are products of space and
time. The data values are represented by signal parameters. Periodic
signals, particularly tho se using sine waves as carriers, are the most
intriguing sorts of signals for radio transmission. A sine wave's function as
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117

The signal's amplitude (A), frequency (f), and phase shift (φ) are its
parameters.As a result, A t, the amplitude as a component of the function g
may likewise fluctuate with time. The periodicity of the signal is
expressed by the frequency f, where T = 1/f is the period. (In equations ω,
is often substituted for 2f.) Additionally, the frequency f may alter with
time, so f t. The signal's shift in relation to the same signal without a shift is
finally determined by the phase shift. Figure 4 illustrates an illustration of
shifting a function. This compares a sine function with and without a
phase shift φ, with the same amplit ude and frequency, respectively.

Figure 4: Time domain representation of a signal
Sine waves are of special interest, as it is possible to construct every
periodic signal g by using only sine and cosine functions according to a
fundamental equation of Fo urier:

The coefficients a and bn in this equation represent the amplitudes of the
nth sine and cosine functions, while the parameter c specifies the Direct
Current (DC) component of the signal. The equation demonstrates that
arbitrary periodic functions can only be built using an endless number of
sine and cosine functions.However, the frequencies of these functions (the
so-called harmonics) are a multiple of the fundamental frequency f and
rise with a growing parameter n.Any media has a finite bandwidth,
including the air, cable, transmitters, etc. There is also a frequency upper
bound.Since all practical transmitting systems have bandwidth limitations
and can never transmit arbitrary periodic functions, it is sufficient to take
into account a small numbe r of sine and cosine functions to generate
periodic functions. We only need to be aware that transmitted signals can
be conceptualized as being made up of one or more sine functions.The
examples that follow always show the situation where there is just one
frequency, or one sine function.
The time domain is a common representation format for signals (Figure 4).
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118 expressed in seconds; amplitudes can, for example, be expressed in volts).
This is also the common oscilloscope representation that is well -known.
This illustration can also demonstrate a phase change.
If a signal contains a large number of distinct frequencies, representations
in the time domain can be troublesome (as the Fourier equ ation indicates).
In this instance, the frequency domain provides a more accurate
representation of a signal(Figure 5). Here, the signal's amplitude at a
certain frequency is plotted against frequency. Figure 5 only displays one
peak, and the signal is a s ingle sine function with only one frequency
component. Arbitrary periodic functions would have a wide range of
peaks, or the signal's frequency spectrum. A spectrum analyzer is a device
for displaying frequencies.Using the inverse Fourier transformation,
Fourier transformations are a mathematical tool for converting from the
time domain to the frequency domain.

Figure 5: Frequency domain representation of a signal
Figure 6's representation of the phase domain offers a third way to
represent signals. The a mplitude M and phase of a signal are shown in
polar coordinates in this diagram, which is also known as a phase state or
signal constellation diagram. (The vector's length denotes amplitude and
angle, phase shift.) The x -axis, which is also known as In -Phase (I), shows
a phase of 0. Quadrature (Q) would be a point on the y -axis with a phase
shift of 90° or /2.

Figure 6: Phase domain representation of a signal
6.6 ANTENNAS
Antennas transmit and receive electromagnetic radiation from space
through a wire or coaxial cable(or any other appropriate conductor).The
isotropic radiator, a point in space that radiates equally in all directions,
serves as a theoretical reference antenna. All places with equal power are
situated on a sphere with the antenna at its cen tre. Figure 7, which shows a munotes.in

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119 two-dimensional cross -section of the actual three -dimensional pattern,
shows that the radiation pattern is symmetric in all directions.

Figure 7: Radiation pattern of an isotropic radiator
But in reality, such an antenna does not exist. Real antennas all have
directional effects, which means that the radiation intensity varies
depending on the direction the antenna is facing. A thin, center -fed dipole,
also known as a Hertzian dipole, is the most basic actual antenna and is
depicted in Figure 8 (right -hand side). The dipole comprises of two equal -
length collinear conductors separated by a tiny feeding gap. The dipole's
length is not arbitrary, but, for instance, cutting the wavelength λ in half of
the signal's transmission yiel ds extremely effective energy radiation. The
length of λ/4 is effective when put on a car's roof. Additionally called the
Marconi antenna

Figure 8: Simple antennas
As seen in Figure 9, a λ/2 dipole radiates uniformly or omnidirectionally
in one plane and in a figure -eight pattern in the other two. The only way
this kind of antenna can overcome environmental obstacles is by
increasing the signal's power. Challenges may include hills, valleys,
structures, etc.

Figure 9: Radiation pattern of a simple dipol e
An omnidirectional radiation pattern is not very useful if an antenna is
installed, for instance, in a valley or between two structures. Directional
antennas with predetermined set favored transmission and reception
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120 directional antenna with its main lobe facing the x -axis is depicted in
Figure 10. Satellite dishes are an exceptional type of directional antenna.

Figure 10:Radiation pattern of a directed antenna
Cellular systems freq uently employ directed antennas. A sectorized
antenna can be built by combining several directed antennas on a single
pole. It is possible to sectorize a cell into, say, three or six sectors,
allowing for frequency reuse. The radiation patterns of these se ctorized
antennas are depicted in Figure 11.

Figure 11: Radiation patterns of sectorized antennas
It is also possible to combine two or more antennas to enhance reception
by reducing the impacts of multi -path propagation.Different diversity
methods are p ossible with these antennas, commonly known as multi -
element antenna arrays. Switched diversity or selection diversity is one
such strategy where the receiver always employs the antenna element with
the highest output. The power of all signals is combined to achieve gain by
diversity combining. To prevent cancellation, the phase is first rectified
(cophasing). As seen in Figure 12, various plans are viable. On top of a
ground plane, two λ/4 antennas are combined on the left with a λ/2 gap
between them.On th e right, three standard λ/2 dipoles are combined with a
distance of λ/2 between them. Spacing could also be in multiples of λ/2.

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121 Smart antennas, which combine several antenna elements (also known as
antenna array) with signal processing to optimize the radiation/reception
pattern in response to the signal environment, offer a more sophisticated
solution. These antennas can adjust to variations in transmission
conditions, reception power, and a variety of signal propagat ion effects.
Beam formation can also involve antenna arrays. This would be an
extreme example of a directed antenna that may employ space division
multiplexing to track a single user. Base stations wouldn't be the only
devices that could follow users with a specific beam. Wireless devices
may also aim their electromagnetic radiation at a base station rather than at
a person, for example. This would aid in lowering the radiation absorbed.
6.7 SIGNAL PROPAGATION
Wireless communication networks have signal sen ders and receivers much
like wired networks do. However, these two networks show significant
disparities in terms of signal propagation. In contrast to wired networks,
where a signal may only travel via a wire, wireless networks do not have a
wire for the signal to use to establish the direction of propagation(which
can be twisted pair copper wires, a coax cable, but also a fiber etc.). The
wire usually displays the same traits at each place as long as it is not cut or
damaged. Depending on the length, one can exactly predict how a signal
will behave while passing over this wire, for example, received power.
This predictable behavior for wireless transmission only applies when
there is nothing between the sender and the receiver, or in a vacuum. This
is illu strated in figure 13.

Figure 13
 Transmission range: Within a certain radius of the sender
transmission is possible, i.e., a receiver receives the signals with an
error rate low enough to be able to communicate and can also act
as sender.
 Detection range : Within a second radius, detection of the
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122 to differ from background noise. However, the error rate is too
high to establish communication.
 Interference range: Within a third even large r radius, the sender may
interfere with other transmission by adding to the background
noise. A receiver will not be able to detect the signals, but the
signals may disturb other signals.
Cells arranged around a transmitter are the result of this straightf orward
and excellent plan. Real life, on the other hand, does not take place in a
vacuum; radio transmission must deal with obstacles like our atmosphere,
mountains, structures, moving senders and receivers, etc. The three circles
mentioned above will actu ally be oddly shaped polygons whose shapes
vary with time and frequency.
6.8 MULTIPLEXING
Multiplexing is not only a fundamental mechanism in communication
systems but also in everyday life. Multiplexing describes how several
users can share a medium with minimum or no interference. One example,
is highways with several lanes. Many users (car drivers) use the same
medium (the highways) with hopefully no interference (i.e., accidents).
This is possible due to the provision of several lanes (space division
multiplexing) separating the traffic. In addition, different cars use the same
medium (i.e., the same lane) at different points in time (time division
multiplexing).
Multiple access schemes are used to allow multiple mobile users to share a
finite quantity o f radio spectrum at the same time.
6.8.1 Multiple Access Techniques
It is common in wireless communication systems for subscribers to be
able to send information from the mobile station to the base station while
simultaneously receiving information from th e base station to the mobile
station.
A cellular system splits an area into cells, with each cell's mobile unit
communicating with a base station. The primary goal of cellular system
design is to maximise channel capacity, or the ability to handle as many
calls as feasible in a given bandwidth while maintaining a sufficient level
of service.
There are numerous options for granting access to the channel. These
primarily comprise the following:
 Frequency Division Multiple Access (FDMA)
 Time Division Multiple Access (TDMA)
 Code Division Multiple Access (CDMA)
 Space Division Multiple Access (SDMA)
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123 These strategies can be classed as narrowband or wideband systems
depending on how the available bandwidth is allocated to the users.
 Narrowband systems
Narrow band systems operate with channels that are significantly narrower
than the coherence bandwidth. Narrow band TDMA allows users to share
the same channel but assigns each user a distinct time slot, allowing a
small number of users t o be separated in time on a single channel.
 Wideband systems
In wideband systems, a single channel's transmission bandwidth is
substantially wider than the channel's coherence bandwidth. As a result,
multipath fading has a minor impact on the received sign al in a wideband
channel, and frequency selective fades only occur in a limited portion of
the signal bandwidth.
6.8.2 Frequency Division Multiple Access (FDMA)
The FDMA standard is the foundation for enhanced mobile phone
services. The following are the c haracteristics of FDMA:
 For each user to access the network, FDMA assigns a different sub -
band of frequency.
 When FDMA is not in use, the channel is left idle rather than being
assigned to other users.
 Narrowband systems use FDMA, which is less complicated than
TDMA.
 To reduce adjacent channel interference, tight filtering is used.
 In FDMA, the base station BS and the mobile station MS transmit and
receive data at the same time.
6.8.3 Time Division Multiple Access (TDMA)
TDMA is utilised instead of FDMA in situations where continuous
transmission is not required. The following are some of the characteristics
of TDMA:
 TDMA allows several users to share a single carrier frequency by
using non -overlapping time intervals.
 In TDMA, data is transmitted in bursts r ather than continuously. As a
result, the process is simplified.
 Duplexers are not required because TDMA employs different time
slots for transmission and reception.
 TDMA offers the advantage of allowing different users to be assigned
varied numbers of tim e slots every frame.
 By concatenating or reassigning time slots based on priority,
bandwidth can be provided on demand to multiple users.

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124 6.8.4 Code Division Multiple Access (CDMA)
Multiple access techniques such as code division multiple access employ a
single channel to broadcast data simultaneously from multiple
transmitters. The following are its characteristics:
 Instead than being assigned a specific frequency, each CDMA
customer uses the entire available spectrum.
 For voice and data connections, CDM A is highly recommended.
 While many codes share the same CDMA channel, users with the
same code can connect with one another.
 CDMA has a greater capacity for airspace than TDMA.
 CDMA handles the hand -off between base stations quite well.
6.8.5 Space Divisi on Multiple Access (SDMA)
Space division multiple access, also known as spatial division multiple
access, is a MIMO (multiple -input multiple -output) architecture technique
that is commonly used in wireless and satellite communication. It has the
following characteristics:
 Using the same channel, all users can communicate at the same time.
 SDMA is fully interference -free.
 A single satellite can communicate with many satellites using the same
frequency receiver.
 The base station in SDMA can monitor a moving u ser thanks to the
use of directional spot -beam antennas.
 Controls the amount of energy radiated by each user in space.
6.8.6 Spread Spectrum Multiple Access
Signals with a transmission bandwidth greater than the minimum needed
RF bandwidth are used in spre ad spectrum multiple access (SSMA).
Spread spectrum multiple access approaches are divided into two
categories:
 Frequency hopped spread spectrum (FHSS)
 Direct sequence spread spectrum (DSSS)

 Frequency hopped spread spectrum (FHSS)
This is a digital multip le access system in which the carrier frequencies of
individual users within a wideband channel are altered in a pseudo random
manner. The digital data is split down into uniformly sized bursts that are
then sent over various carrier frequencies.
 Direct se quence spread spectrum (DSSS)
This is the most widely utilised CDMA technology. A Pseudo Random
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125 a code word that is orthogonal to the codes of other users, and the receiver
must know the transmitter's code word in order to detect the user.
Another sort of spread spectrum is hybrid, which is made up of
combinational sequences. Another type that is rarely mentioned is time
hopping.
Spread spectrum systems become bandwidth efficient in a multiple user
scenario because numerous users can share the same spread spectrum
bandwidth without interfering with one another.
6.9 MODULATION
Modulation is a process of changing the characteristics of the wave to be
transmitted by superimposing the messa ge signal on the high -frequency
signal. In this process video, voice and other data signals modify high -
frequency signals – also known as the carrier wave . This carrier wave can
be DC or AC or pulse chain depending on the application used. Usually, a
high-frequency sine wave is used as a carrier wave signal.
These modulation techniques are classified into two major types: analog
and digital or pulse modulation .
Different Types of Modulation
The two types of modulation: analog and digital modulation techniques
have already been discussed. In both the techniques, the baseband
information is converted to Radio Frequency signals, but in analog
modulation, these RF communication signals are a continuous range of
values, whereas in digital modulation these are prearranged discrete states.

Figure: Types of modulation
Analog Modulation
In this modulation, a continuously varying sine wave is used as a carrier
wave that modulates the message signal or data signal. The Sinusoidal
wave’s general function is shown in the figure below, in which, three
parameters can be altered to get modulation – they are mainly amplitude,
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126  Amplitude modulation (AM)
 Frequency modulation (FM)
 Phase m odulation (PM)
In amplitude modulation , the amplitude of the carrier wave is varied in
proportion to the message signal, and the other factors like frequency and
phase remain constant. The modulated signal is shown in the below figure,
and its spectrum con sists of a lower frequency band, upper -frequency
band, and carrier frequency components. This type of modulation requires
greater bandwidth, more power. Filtering is very difficult in this
modulation.

Types of Analog Modulation
Frequency modulation (FM) varies the frequency of the carrier in
proportion to the message or data signal while maintaining other
parameters constant. The advantage of FM over AM is the greater
suppression of noise at the expense of bandwidth in FM. It is used in
applications like radio, radar, telemetry seismic prospecting, and so on.
The efficiency and bandwidths depend on the modulation index and
maximum modulating frequency.
In phase modulation , the carrier phase is varied in accordance with the
data signal. In this type of modu lation, when the phase is changed it also
affects the frequency, so this modulation also comes under frequency
modulation.
Analog modulation (AM, FM, and PM) is more sensitive to noise. If noise
enters into a system, it persists and gets carried till the e nd receiver.
Therefore, this drawback can be overcome by the digital modulation
technique.
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127 AM
Digital Modulation
For better quality and efficient communication, the digital modulation
technique is employed. The main advantages of digital modulation over
analog modulation include permissible power, available bandwidth, and
high noise immunity. In digital modulation, a message signal is converted
from analog to digital message and then modulated by using a carrier
wave.
The carrier wave is keyed or switched on and off to create pulses such that
the signal is modulated. Similar to the analog, here the parameters like
amplitude, frequency, and phase variation of the carrier wave decides the
type of digital modulation.
The types of digital modulation are based on the type of signal and
application used such as Amplitude Shift Keying, Frequency Shift Keying,
Phase Shift Keying, Differential Phase Shift Keying, Quadrature Phase
Shift Keying, Minimum Shift Keying, Gaussian Minimum Shift Keying,
Orthogonal Frequency Division Multiplexing, etc., as shown in the figure.
Amplitude shift keying changes the amplitude of the carrier wave based
on the baseband signal or message signal, which is in digital format. It is
used for low -band requirements and is sensitive to nois e.
In frequency -shift keying, the frequency of the carrier wave is varied for
each symbol in the digital data. It needs larger bandwidths as shown in the
figure. Similarly, the phase shift keying changes the phase of the carrier
for each symbol and it is l ess sensitive to noise.
Frequency Modulation
In order to create a frequency modulated wave, the frequency of the radio
wave is varied in accordance with the amplitude of the input signal.

Frequency Modulation
When the audio wave is modulated with that of the radio frequency carrier
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128 The variation by which the wave moves upward and downward is to be
noted. This is termed as deviation and is generally represented as kHz
deviation.
As an instance, when the signal has a deviation of either + or – 3kHz, then
it is represented as ±3kHz. This means that the carrier signal has up and
downward deviation of 3kHz.
Broadcasting stations that need very high -frequency range in the
frequency spec trum (in the range of 88.5 – 108 MHz), they need certainly
a large amount of deviation which is nearly ±75 kHz. This is called wide -
band frequency modulation. The signals in this range hold the ability to
assist the high quality of transmissions, whereas t hey require higher
bandwidth too. In general, 200kHz is permitted for every WBFM. And for
narrowband FM, a deviation of ±3 kHz is enough.
While implementing an FM wave, it is more beneficial to know the
effectivity range of the modulation. This stands as t he parameter in stating
factors such as knowing the type of signal whether wide band or narrow
band FM signal. It also helps in making sure that the whole receivers or
transmitters that are in the system are programmed to adapt to the
standardized range of modulation as this shows an impact on the factors
such as the channel spacing, bandwidth of the receiver, and others.
So, to signify the modulation level, modulation index and deviation ratio
parameters are to be determined.
The different types of frequen cy modulation include the following.
Narrow band FM
 This is termed as the type of frequency modulation where the
modulation index value is too minimal.
 When the modulation index value is < 0.3, then there will be an only
carrier and corresponding sidebands having bandwidth as twice the
modulating signal. So, β ≤ 0.3 is called narrow band frequency
modulation.
 The maximum range of modulating frequency is of 3 kHz
 The maximum frequency deviation value is 75 kHz
Wide band FM
 This is termed as the type of frequ ency modulation where the
modulation index value is large.
 When the modulation index value is > 0.3, then there will be more than
two sidebands having bandwidth as twice the modulating signal. When
the modulation index value increases, then the number of s idebands
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129  The maximum range of modulating frequencies is in between 30 Hz –
15 kHz
 The maximum frequency deviation value is 75 kHz
 This frequency modulation needs a higher bandwidth ran ge which is
almost 15 times ahead of the narrow band frequency modulation.
The other types of modulation techniques used in the communication
system are:
 Binary phase shift keying
 Differential phase -shift keying
 Differential quadrature phase shift keying
 Offset quadrature phase shift keying
 Audio FSK
 Multi FSK
 Dual -tone FSK
 Minimum shift keying
 Gaussian minimum shift keying
 Trellis coded type of modulation
6.10 CELLULAR SYSTEMS
SDM is used in cellular networks for mobile communications. Each
transmitter, co mmonly referred to as a base station, serves a specific cell.
Cell radii can be as small as a few metres in a building, hundreds of metres
in a city, or even tens of kilometres across the nation. As seen in Figure
14, cells are never perfectly round or hex agonal; instead, the shape of a
cell depends on its surroundings (buildings, mountains, valleys, etc.), the
weather, and occasionally even the strain on the system. This strategy is
frequently used in mobile telecommunication systems, where a mobile
statio n inside a base station's cell can connect with that base station and
vice versa.

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130 In this context, the question of why mobile network operators do not
employ strong transmitters with large cells like, for example, radio stations
do, instead of installing thousands of base stations across a nation (which
is rather expensive), emerges.Small cell cellular systems provide the
following benefits:
 Greater capacity: Frequency reuse is possible with SDM. One
transmitter can use the same frequencies if i t is far from another, or
outside the interference range. This frequency is prohibited for other
users since most mobile phone systems assign frequencies to certain
users (or specific hopping patterns). However, frequencies are a finite
resource, and each cell can only support a very small number of
concurrent users. Large cells cannot accommodate more users. Instead,
they are constrained to fewer potential consumers per km2. In cities
where a significant number of people use mobile phones, there is
another justification for employing very small cells.
 Less transmission power: While power issues may not pose a
significant threat to base stations, they do pose a threat to mobile
stations. The few Watts of transmit power now available would not be
sufficient f or a receiver located far from a base station. However,
energy is a significant issue for portable, mobile electronics.
 Local interference only: Interference issues are exacerbated by large
separations between the sender and receiver. Mobile and base stati ons
only have to contend with "local" interference when using tiny cells.
 Robustness: Because they are decentralized, cellular systems are
better able to withstand the failure of a single component. If one
antenna malfunctions, communications are only impa cted locally.
Additionally, small cells have a few drawbacks:
 Cellular systems require a sophisticated infrastructure to link all base
stations. This costs quite a bit because it requires numerous antennas,
switches for call forwarding, position registers to locate a mobile
station, etc.
 Handover needed:When switching from one cell to another, the mobile
station must complete a handover. This can occur frequently,
depending on the size of the cell and the rate of movement.
 Frequency planning: To avoid inter ference between transmitters using
the same frequencies, frequencies have to be distributed carefully. On
the one hand, interference should be avoided, on the other, only a
limited number of frequencies is available.
Different transmitters that are inside each other's interference range
employ FDM to prevent interference. The hopping pattern must be
coordinated if FDM and TDM are mixed. Within the interference range,
the basic rule is to avoid using the same frequency at the same time(if
CDM is not applied) .Figure 14 depicts two potential models for producing
cell patterns with less interference. Clusters of cells are formed; on the left
side, three cells create a cluster, while on the right, seven cells do the
same. A cluster's cells all employ different se ts of frequencies. Sets f1, f2,
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131 on the left.The pattern will resemble something slightly different in actual
transmission. To illustrate the model simply, the hexagonal layout w as
chosen. The repeating of the same frequency sets is also demonstrated by
this pattern. A sender's transmission strength must be constrained to
prevent interference with the subsequent cell using the same frequencies.
Sectorized antennas can be utilised to further reduce interference
(particularly under specific traffic conditions, such as the number of users
per km2). In a cluster of three cells, as shown in Figure 15, three sectors
are utilised per cell. Generally, with larger cell radii, sectorized ant ennas
make more sense than omni -directional antennas.

Figure 15
If traffic demand varies, the fixed frequency assignment to cell clusters
and cells is not very effective. It might make sense to "borrow"
frequencies, for example, if one cell has a strong load while the
neighbouring cell has a light burden. More frequencies are dynamically
assigned to cells with more traffic.While the first fixed method is known
as fixed channel allocation (FCA), this one is known as borrowing channel
allocation (BCA). The GSM system uses FCA because it is easier to use,
but rigorous traffic analysis is needed before installation.
DECT has a dynamic channel allocation (DCA) system in place. In this
system, frequencies can be freely assigned to cells, but they can also only
be borrowed. The risk of interference with cells using the same frequency
exists with dynamic frequency assignment to cells. The adjacent cells can
block the "borrowed" frequency.Such intricate frequency planning and
elaborate channel allocation algorithms are not necessary in cellular
systems that use CDM rather than FDM. Users are distinguished in this
case based on the code they employ rather than frequency. Another issue
with cell planning is that cell size is dependent on the level of load. As a
result, CDM cells are frequently referred to as "breathing." A cell can
cover a wider area when under a light load, but as the load increases, it
contracts. If there are more users in a cell, the noise level will increase,
which is the cause of this.more noise le vels result in more route loss and
transmission mistakes. Last but not least, mobile stations farther from the
base station disconnect from the cell. (This is like attempting to talk to
someone who is far away at a busy party.) This behaviour is shown in
figure 16 with a user transmitting a high bit rate stream inside a CDM cell.
Two users leave the cell as a result of this additional user, causing the cell
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132 video broadcast (at a high bit rate), while the rest speak normally (low bit
rate).

Figure 16
6.11 SUMMARY
Antennas are required for the transmission and receiving of
electromagnetic waves, whi ch are the foundation of wireless
communication. Omni -directional antennas are preferred for mobile
devices while directed antennas are frequently used in mobile phone
system base stations. Electromagnetic waves can experience a variety of
side effects whe n travelling from originator to receiver. The common
effectsinclude shadowing, fading, reflection, diffraction, and scattering.
Multi -path propagation is one of the main issues in wireless
communication as a result of all these phenomena. As a result of
intersymbol interference, or when one symbol is "smeared" into another
symbol as a result of delay spread, multi -path propagation reduces the
channel bandwidth.
Since wireless transmission uses just one "medium," multiplexing
techniques can be used to increa se overall capacity. SDM, FDM, TDM,
and CDM are considered the standard schemes. Data must be "translated"
into a signal with a specific carrier frequency in order to achieve FDM.
Consequently, two modulation steps are possible. Analog modulation
pushes th e signal's centre frequency up to the radio carrier whereas digital
modulation encrypts data into a baseband signal. Many bits can be
encoded into a single -phase shift using some cutting -edge techniques,
which increases efficiency.
Spread spectrum technolo gy can be used to implement a number of
functionalities. One is (at least some) security because the signal appears
to be noise to someone who doesn't know the spreading code. The code
space is the foundation for spread spectrum special medium access
techn iques.Since the signal is dispersed over a wider bandwidth,
narrowband interference only affects a tiny portion of the signal, making a
transmission more resistant to it thanks to spread spectrum.
Finally, we discussed about Cellular systems. SDM is used b y cellular
systems to increase the total capacity of mobile phone systems.Although
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133 to the anticipated traffic), they offer one of the fundamental approaches to
effectively using th e limited frequency resources.
6.12 LIST OF REFERENCES
1) Protocols and Architectures for Wireless Sensor Network, Holger
Kerl, Andreas Willig, John Wiley and Sons, 2005
2) Wireless Sensor Networks Technology, Protocols, and
Applications,Kazem Sohraby, Daniel Minoli and TaiebZnati, John
Wiley & Sons, 2007
3) Mobile communications, Jochen Schiller,2nd Edition, Addison wisely,
Pearson Education,2012
4) Fundamentals of Wireless Sensor Networks, Theory and Practice,
Waltenegus Dargie, Christian Poellabauer, Wiley Serie s on wireless
Communication and Mobile Computing, 2011
5) Networking Wireless Sensors, Bhaskar Krishnamachari, Cambridge
University Press, 2005
6.13 UNIT END EXERCISES
1. State and explain the applications of wireless transmission.
2. Discuss about the history of wireless communication.
3. Explain Frequency for radio transmission.
4. Write a note on Signals.
5. Describe about Antennas and its types.
6. What is Signal propagation?
7. Explain Multiplexing.
8. Explain Modulation.
9. Write a note on Cellular systems.


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TELECOMMUNICATION,
SATELLITE AND BROADCAST SYSTEMS: GSM
Unit Structure
7.0 Objectives
7.1 Introduction
7.2 Mobile services
7.3 System architecture
7.4 Radio interface
7.5 Protocols
7.6 Localization and Calling
7.7 Handover
7.8 Security
7.9 DECT: System architecture
7.10 Protocol architecture
7.11 TETRA
7.12 UMTS and IMT - 2000
7.13 Satellite Systems: History, Applications
7.14 Basics: GEO, LEO, MEO
7.15 Summary
7.16 List of References
7.17 Unit End Exercises
7.0 OBJECTIVES
 To understand the concept of GSM and how it is utilised for voice
traffic
 To understand DECT architecture
 To gain basic insights about: GEO, LEO, MEO

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135 7.1 INTRODUCTION
Digital cellular networks are the fastest -growin g part of the market for
mobile and wireless devices. They are wireless extensions of standard
PSTN or ISDN networks, allowing for smooth roaming within the same
country or even globally. These systems are mostly utilised for voice
traffic nowadays. Howeve r, because data traffic is always increasing, this
chapter discusses many wireless data transfer strategies using cellular
systems.
The following are the global market figures for cellular networks (GSM
Association, 2002). GSM is the most widely used digit al system,
accounting for over 70% of the market. The analogue AMPS system still
has 3% of the market, while the Japanese PDC has 5%. (60 million users).
The rest is split between CDMA (12%) and TDMA (10%) systems, as
well as other technologies. Nearly eve ryone in Europe (about 370 million
people) uses the digital GSM system, with almost no analogue systems
remaining.In the United States and several other countries that have
absorbed US technology, the situation is different (e.g., South Korea,
Canada). Wit h 107 million TDMA, 135 million CDMA, and just 16
million GSM users (North America only), the digital market is divided
into TDMA, CDMA, and GSM systems. While Europe has only one
digital system, the US market is fragmented into numerous. This causes
serio us coverage and service availability issues, and is an example of how
market forces failed to deliver better services (compared to the common
standard in Europe).
The figure below depicts the global number of subscribers to various
mobile phone technology (GSM Association, 2002). The illustration mixes
various versions of the same technology (e.g., GSM working on 900,
1,800, and 1,900 MHz). The graph's two upper lines depict the total
number of users and 1998 predictions. It's interesting to note that no on e
predicted mobile communication technology's enormous success. The
graph also demonstrates that analogue systems are no longer in use, with
GSM dominating the present market.Second generation systems include
GSM, TDMA, CDMA, and PDC. It's worth noting tha t mobile phones are
now used by more people than landlines!In March 2002, the graphs of
mobile and fixed users crossed.

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136 7.2 MOBILE SERVICES
GSM makes it possible to combine various voic e and data services and
communicate with current networks. Customers find a network appealing
because of its services. Bearer, tele, and supplemental services are the
three different categories of services that GSM has established. The
subsections that fol low provide descriptions of them.A reference model
for GSM services is shown in Figure below.

Figure: Bearer and tele services reference model
A mobile station MS is connected to the GSM public land mobile network
(PLMN) via the Um interface. (GSM -PLMN i s the infrastructure needed
for the GSM network.) This network is connected to transit networks, e.g.,
integrated services digital network (ISDN) or traditional public switched
telephone network (PSTN). There might be an additional network, the
source/dest ination network, before another terminal TE is connected.
Bearer services now comprise all services that enable the transparent
transmission of data between the interfaces to the network, i.e., S in case
of the mobile station, and a similar interface for t he other terminal (e.g.,
S0 for ISDN terminals). Interfaces like U, S, and R in case of ISDN have
not been defined for all networks, so it depends on the specific network
which interface is used as a reference for the transparent transmission of
data. In t he classical GSM model, bearer services are connection -oriented
and circuit - or packet -switched. These services only need the lower three
layers of the ISO/OSI reference model.
The mobile termination (MT) in the mobile station MS handles all
network -specif ic duties (TDMA, FDMA, coding, etc.) and provides the
terminal (TE), which can then be network -independent, with an interface
for data transmission (S). According to the ISDN reference model,
additional interfaces, such R, may be required depending on TE's
capabilities. Since tele services are application -specific, all seven layers of
the ISO/OSI reference model can be required. These services are
described from one terminal TE to another, or end -to-end.
1] Bearer services
Different data transmission protoc ols are specified by GSM, with the
original GSM allowing for non -voice service data speeds of up to 9600
bit/s. Transparent and opaque, synchronous and asynchronous data
transmission is possible with bearer services. Only the physical layer's
(layer 1) cap abilities are used by transparent bearer services for data
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137 constant delay and throughput. Forward error correction (FEC), which
incorporates redundancy into the data stream and aid s in the reconstruction
of the original data in the event of transmission faults, is the only method
for improving transmission quality. Data rates of 2.4, 4.8, or 9.6 kbit/s are
feasible, depending on the FEC.
Error correction and flow control are impleme nted via protocols at layers
two and three in non -transparent bearer services. These services
incorporate a radio link protocol while utilising transparent bearer
services. (RLP). This protocol includes high -level data link control
(HDLC) mechanisms and un ique selective -reject techniques to force the
retransmission of inaccurate data. Although less than 10-7 bit errors were
obtained, throughput and delay may now differ depending on the quality
of the transmission.
2] Tele services
GSM primarily focuses on t eleservices that are voice -oriented. These
include message services, basic data connection with terminals that are
familiar from the PSTN or ISDN, and encrypted voice transfer. (e.g., fax).
However, as telephony is the principal service, the fundamental ob jective
of GSM was to provide high -quality digital voice transmission, at least
providing the normal bandwidth of 3.1 kHz of analogue phone networks.
While ordinary codecs are used to transmit analogue data for use with
conventional computer modems found i n, for example, fax machines,
special codecs (coder/decoder) are utilised for voice
communication.Another service offered by GSM is the emergency
number.A useful service for very simple message transfer is the short
message service(SMS), which offers trans mission of messages of up to
160 characters.Another non -voice tele service is group 3 fax, which is
available worldwide. In this service, fax data is transmitted as digital data
over the analog telephone network according to the ITU -T standards T.4
and T.3 0 using modems.
3] Supplementary services
GSM service providers may provide additional services in addition to tele
and bearer services. These services, which can vary from provider to
provider, provide numerous upgrades for the basic telephony service,
much like ISDN networks.User identification, call forwarding, and phone
redirection are examples of typical services. There may be access to
standard ISDN capabilities like multiparty communication and locked user
groups. Companies are particularly intereste d in closed user groups
because they enable features like a company -specific GSM sub -network
that is only accessible to group members.
7.3 SYSTEM ARCHITECTURE
GSM, like many telecommunications systems, has a hierarchical,
complicated system architecture wi th numerous entities, interfaces, and
acronyms. The GSM system, as defined by ETSI, is depicted in the
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138 subsystem (NSS), and the operation subsystem (OS) make up a GSM
system (OSS). The mobile st ations (MS) and some antenna masts of the
base transceiver stations are usually the only parts of the network that a
GSM customer notices (BTS).

Figure: GSM system functional architecture
7.3.1 Radio subsystem
All radio -specific elements, such as mobile stations (MS) and base station
subsystems, are included in the radio subsystem (RSS) (BSS). The RSS
and NSS are connected via the A interface (solid lines) while the OSS is
connected via the O interface (dashed lines) in the diagram above. The A
interface employs circuit -switched PCM -30 systems (2.048 Mbit/s) to
handle up to 30 64 kbit/s connections, whilst the O interface uses the
Signalling System No. 7 (SS7) based on X.25 to carry management data to
and from the RSS.
 Base station subsystem (BSS): A GSM n etwork has numerous base
station subsystems, each controlled by a base station controller (BSC).
The BSS is responsible for maintaining radio connections to an MS,
voice coding and decoding, and rate adaption to and from the wireless
network. The BSS conta ins many BTSs in addition to a BSC.

 Base transceiver station (BTS): It contains all radio equipment
required for radio transmission, such as antennas, signal processing,
and amplifiers. A BTS connects to the MS via the U m interface (ISDN
U interface for m obile use) and to the BSC via the A bis interface, and
can build a radio cell or many cells using sectorized antennas. The Um
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139 FDMA etc.) 16 or 64 kbit/s connections make up the A bis interface.
Depending on the context (buildings, open space, mountains, etc.) as
well as projected traffic, a GSM cell can measure between 100 m and
35 km.

 Base station controller (BSC): The BSC is in charge of the BTSs. It
manages the handover of radio frequen cies from one BTS to another
within the BSS and performs MS paging. At the A interface, the BSC
multiplexes the radio channels onto the fixed network connections.
7.3.2 Network and Switching subsystem
The network and switching subsystems are the "heart" of the GSM system
(NSS). The NSS connects the wireless network to conventional public
networks, handles handovers between multiple BSSs, includes capabilities
for global user localisation, and facilitates charging, accounting, and
roaming of users across dif ferent providers and countries. The NSS is
made up of the switches and databases listed below:
 Mobile Services Switching Centres: MSCs are high -speed digital
ISDN switches. They use the A interface to link to other MSCs and
BSCs, forming the GSM system's f ixed backbone network.Typically,
an MSC is in charge of numerous BSCs in a certain geographic area.
Other fixed networks, such as PSTN and ISDN, are connected to a
gateway MSC (GMSC). An MSC can additionally connect to public
data networks (PDNs) like X.25 using additional interworking
functions (IWF). An MSC is in charge of all signals for connection
setup, connection release, and connection handover to other MSCs.
For this, the standard signalling system No. 7 (SS7) is employed.SS7
includes all aspects of digital network control signalling (reliable
routing and delivery of control messages, call establishment and
monitoring).

 Home Location Register (HLR): The HLR is the most significant
database in a GSM system since it stores all user -relevant data. This
includes static data like the mobile subscriber ISDN number
(MSISDN), subscribed services (including call forwarding, roaming
limitations, and GPRS), and the international mobile subscriber
identity (IMSI). The current location area (LA) of the MS, the mo bile
subscriber roaming number (MSRN), the current VLR, and MSC are
all examples of dynamic information. The information in the HLR is
updated as soon as an MS departs its current LA.This data is required
to pinpoint a user's location within the global GSM network. In a
single HLR, which also allows billing and accounting, all of these
user-specific information pieces exist just once for each user. HLRs
can manage data for millions of clients and contain highly specialised
data bases that must meet particul ar real -time standards in order to
respond to queries within certain time frames.

 Visitor Location register (VLR): The VLR associated with each
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140 MS users currently in the LA associated wit h the MSC (e.g., IMSI,
MSISDN, HLR address). When a new MS joins a LA for which the
VLR is responsible, the HLR copies all necessary information for this
user. This VLR and HLR structure prevents frequent HLR updates and
long-distance user information comm unication.
7.3.3 Operation subsystem
The operating subsystem (OSS), the third component of a GSM system,
contains the functions required for network operation and maintenance.
The OSS has its own network entities and communicates with others via
SS7 signal ling (see Figure above). The entities listed below have been
defined.
 The operation and maintenance centre (OMC): It uses the O
interface to monitor and control all other network elements (SS7 with
X.25). Traffic monitoring, network entity status reports, subscriber and
security management, and accounting and billing are all common
OMC administration responsibilities.Telecommunication management
networks (TMNs), as defined by the ITU -T, are used by OMCs.

 Authentication Centre (AuC): Because the radio inter face and
mobile stations are particularly vulnerable, a separate authentication
centre (AuC) has been established to protect user identity and data
transmission. The AuC contains the authentication methods as well as
the encryption keys, and it generates t he values required for user
authentication in the HLR. The AuC might be located in a designated
protected area within the HLR.

 The Equipment Identity Register (EIR): EIR is a database that
contains all IMEIs, or all device identifications registered for t his
network. MSs are easily stolen because they are mobile. Anyone with
a valid SIM might use the stolen MS. The EIR maintains a stolen (or
locked) device blacklist. An MS is theoretically worthless once the
owner has reported a theft. Unfortunately, diffe rent providers'
blacklists are not always synchronised, making it feasible to use a
device in another operator's network without permission. A list of
valid IMEIs (white list) and a list of malfunctioning devices are also
included in the EIR (gray list).
7.4 RADIO INTERFACE
The radio interface is the most significant interface in a GSM system since
it contains several techniques for multiplexing and media access. GSM
uses cells with BTS to implement SDMA and assigns an MS to each BTS.
As demonstrated in Fig ure, FDD is also utilised to segregate the downlink
and uplink. TDMA and FDMA are combined in media access. For FDMA,
GSM 900 employs 124 channels, each 200 kHz wide, whereas GSM 1800
requires 374 channels. Channels 1 and 124 are not used for transmission in
GSM 900 due to technical reasons.In most cases, 32 channels are
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141 customers. After that, each BTS maintains a single channel for
organisational data and up to ten channels for user data, for example.The
sample below uses the GSM 900 system, while GSM operates similarly at
1800 and 1900 MHz.
The TDM used is also shown in the figure. A GSM TDMA frame
separates each of the 248 channels in time, i.e., each 200 kHz carrier is
subdivided into f rames that are repeated constantly. A frame lasts 4.615
milliseconds. A frame is split into 8 GSM time slots, each of which
represents a physical TDM channel and lasts 577 seconds. Every 4.615
ms, each TDM channel occupies the 200 kHz carrier for 577 secon ds.

Figure: GSM TDMA frame, slots and bursts
Bursts of data are sent out in short increments. Figure depicts a typical
burst utilised for data transmission (user and signalling data) inside a time
slot. The burst in the diagram is only 546.5 seconds long and comprises
148 bits. The remaining 30.5 seconds are used as guard space to prevent
bursts from overlapping due to varying path delays and to allow the
transmitter to turn on and off. Filling the entire slot with data enables for
156.25bit transmission in 577 seconds. Each radio carrier sends roughly
270 kbit/s across the Um interface, and each physical TDM channel has a
raw data throughput of about 33.8 kbit/s.
A regular burst's first and last three bits (tail) are all set to 0 and can be
exploited to i mprove receiver performance. The training sequence in the
middle of a slot is used to adapt the receiver's parameters to the current
path propagation characteristics and, in the case of multi -path propagation,
to select the strongest signal. If the data fi eld contains user or network
control data, the flag S is set.A frequency correction burst allows the MS
to correct the local oscillator to avoid interference with neighbouring
channels, a synchronisation burst with an extended training sequence
synchronise s the MS with the BTS in time, an access burst is used for the
initial connection setup between MS and BTS, and finally a dummy burst
is used if no data is available for a slot, according to ETSI (1993a).
Simple transmitter hardware is possible due to two factors: first, the uplink
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142 (45 MHz for GSM 900, 95 MHz for GSM 1800 using FDD). The TDMA
frames, on the other hand, are time shifted for three slots, thus if the BTS
provides data in s lot one on the downlink at time t 0, the MS accesses slot
one on the uplink at time t 0+3.577 microsecond. A full -duplex transmitter
is unnecessary for an MS; a basic half -duplex transmitter that switches
between receiving and sending is sufficient.
GSM spec ifies an optional slow frequency hopping strategy to mitigate
frequency selective fading. Based on a shared hopping sequence, MS and
BTS may change the carrier frequency after each frame. Between uplink
and downlink slots, an MS adjusts its frequency.
7.5 PROTOCOLS
GSM protocol architecture including signalling protocols and interfaces is
depicted in the diagram below. The U m interface is of particular
significance because the other interfaces are between entities in a fixed
network. All radio -specific func tions are handled by Layer 1, the physical
layer. This involves creating bursts in one of five possible formats,
multiplexing bursts into a TDMA frame, synchronisation with the BTS,
detection of idle channels, and downlink channel quality measurement.
The physical layer at Um uses GMSK for digital modulation and provides
data encryption and decryption, but only between MS and BSS over the
air interface, rather than end -to-end.

Figure: Protocol architecture for signalling
Individual route delays between an MS and the BTS are also corrected
during synchronisation. Because all MSs in a cell use the same BTS, they
must all be synced to it. The BTS creates the temporal structure of frames,
slots, and other elements. The differing round trip times is a problem i n
this situation (RTT). The RTT of an MS close to the BTS is quite short,
whereas an MS 35 kilometres away already has an RTT of roughly 0.23
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143 employed the slot structure without correction, b ecause 0.23 ms is already
40% of the 0.577 ms provided for each slot.As a result, the BTS sends the
current RTT to the MS, which adjusts its access time to ensure that all
bursts arrive to the BTS within their time limitations. This technology cuts
the gua rd time in half, from 30.5 seconds to 5%. The variable timing
advance can be used to control access, allowing a burst to be pushed up to
63 bits earlier, with each bit lasting 3.69 microseconds (which results in
0.23ms needed). A burst cannot be shifted so oner than 63 -bit times
because the variable timing advance cannot be extended.As a result, the
maximum distance between an MS and a BTS is 35 kilometres. It may be
possible to receive the signals at larger distances; however, access to the
BTS cannot be au thorised to avoid collisions.
The physical layer's key responsibilities include channel coding and error
detection/correction, which are closely related to the coding
techniques.Different forward error correction (FEC) techniques are used
extensively in ch annel coding. FEC adds redundancy to user data, making
it possible to discover and remedy certain problems. The degree of
redundancy, coding technique, and further interleaving of data to mitigate
the impacts of burst mistakes determine the strength of an FEC scheme.
The FEC is also the reason why, contrary to the ISO/OSI reference model,
error detection and correction occurs in layer one rather than layer two.
The GSM physical layer attempts to fix mistakes but does not send
incorrect data to the higher la yers.
GSM uses several coding systems with different correction capacities for
different logical channels. To obtain a data rate of 22.8 kbit/s (using the 13
kbit/s from the voice codec plus redundancy, CRC bits, and interleaving),
speech channels require further coding of voice data following analogue to
digital conversion. Because speech was anticipated to be the primary
service in GSM, the physical layer includes features like voice activity
detection (VAD), which transmits voice data only when a voice s ignal is
present.This approach helps to reduce interference because a channel may
be silent around 60% of the time (assuming only one person speaks at a
time and some extra time is required to move between speakers). The
physical layer provides a comfort n oise to simulate a connection (total
silence would likely mislead a user), but no actual transmission takes
occur during times of silence (e.g., if a user needs time to ponder before
talking). The noise is even customised to the communication partner's
existing background noise.
All of this data interleaving for a channel to reduce interference due to
burst mistakes and the logical channel's recurrence pattern results in a
transmission delay. A TCH/FS has a 60 ms delay and a TCH/F9.6 has a
100 ms delay (wit hin 100 ms signals in fixed networks easily travel
around the globe). If communicating with an MS instead of a regular fixed
station (telephone, computer, etc.), these periods must be added to the
transmission delay, and they may affect the performance of any upper
layer protocols, such as computer data transmission.
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144 7.6 LOCALIZATION AND CALLING
The automatic, worldwide localization of users is a key aspect of the GSM
system. The system always knows where a user is, and the same phone
number can be used an ywhere in the world. Even if a user does not use the
mobile station, GSM performs periodic location updates to provide this
service (assuming the MS is still logged into the GSM network and is not
totally switched off). The current location (just the locat ion area, not the
specific geographical location) is always stored in the HLR, and the VLR
in charge of the MS tells the HLR when the location changes.The HLR
transfers all user data to the new VLR as soon as an MS enters the range
of a new VLR (a new loca tion region). Roaming is the process of
switching VLRs while maintaining continuous availability of all services.
Roaming can take place within a single provider's network, between two
providers in the same country (national roaming is frequently not
suppo rted owing to operator rivalry), or between different carriers in other
countries (international roaming). People usually identify the term
roaming with international roaming because it is this form of roaming that
makes GSM so appealing: one device, 190 c ountries!
Several numbers are required to locate and address an MS:
 Mobile Station International ISDN Number (MSISDN): For a GSM
customer, the phone number is the most important number. Remember
that the phone number is not linked to a specific device, but rather to
the SIM, which is unique to each user. For addresses, the MSISDN uses
the ITU -T standard E.164, which is also used in fixed ISDN networks.
The country code (CC) (e.g., +49 179 1234567 with 49 for Germany),
the national destination code (NDC) (i. e., the network provider's
address, e.g., 179), and the subscriber number make up this number
(SN).

 International mobile subscriber identity (IMSI): GSM employs the
IMSI to identify subscribers internally. A mobile country code (MCC)
(e.g., 240 for Sweden , 208 for France), a mobile network code (MNC)
(i.e., the network provider's code), and lastly a mobile subscriber
identity number (MSIN) make up an IMSI (MSIN).

 Temporary mobile subscriber identity (TMSI): GSM employs the 4
byte TMSI for local subscriber identification to disguise the IMSI,
which would reveal the actual identity of the user signalling over the air
interface. TMSI is chosen by the current VLR and is only valid for a
limited time and within the VLR's location region (for ongoing
communicati on, TMSI and LAI are sufficient; the IMSI is not
required). A VLR may also modify the TMSI on a regular basis

 Mobile station roaming number (MSRN): MSRN is another
temporary address that hides a subscriber's identity and location. This
address is generate d by the VLR in response to a request from the
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145 (VCC), the visitor national destination code (VNDC), the current
MSC's identifier, and the subscriber number are all contained in the
MSRN. The MSRN assists the HLR in locating an incoming call
subscriber.
All of these numbers are required to locate a subscriber and maintain a
mobile station connection. The mobile terminated call (MTC) is an
intriguing circumstance in which a station calls an other mobile station
(which could be outside the GSM network or another mobile station). The
essential processes for connecting the calling station to the mobile user are
shown in the diagram. In step one, a user phones a GSM subscriber's
phone number. The fixed network (PSTN) recognises that the number
belongs to a GSM network user (based on the destination code) and
forwards the call setup to the Gateway MSC.The GMSC locates the
subscriber's HLR (which is encoded in the phone number) and informs it
of the call setup. The HLR now verifies that the number is valid and that
the user has subscribed to the requested services before requesting an
MSRN from the current VLR. The HLR can determine the MSC
responsible for the MS after receiving the MSRN and sends th is
information to the GMSC. The call setup request can now be forwarded to
the MSC defined by the GMSC.

Figure: Mobile Terminated Call (MTC)
The MSC is in charge of all subsequent steps from this point forward. It
starts by asking the VLR for the MS's cu rrent state. If the MS is available,
the MSC commences paging in all cells it is responsible for (i.e., the
location area, LA), because searching for the correct cell would take too
long (although this strategy puts some strain on the signalling channels,
therefore optimizations exist). This paging signal is sent to the MS by all
BSS BTSs. If the MS responds, the VLR must do security checks (set up
encryption etc.). The VLR then instructs the MSC to establish a link with
the MS (steps 15 to 17).
When compar ed to an MTC, making a mobile originating call (MOC) is
more easier (Referfigure below). The MS sends a request for a new
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146 determines whether or not this user is authorised to make a call us ing the
desired service, as well as the availability of resources across the GSM
network and into the PSTN. The MSC establishes a connection between
the MS and the fixed network if all resources are available.

Figure: Mobile originated call (MOC)
Other m essages are exchanged between an MS and a BTS during
connection establishment in addition to those stated above (in either
direction). Before the phone rings, these messages can be heard as
crackling noise on radios or poorly insulated loudspeakers. The me ssages
for an MTC and MOC are shown in the diagram below. Paging is only
required for an MTC, after which comparable message exchanges take
place. The channel access via the randomaccess channel (RACH) with
sequential channel assignment is the initial stag e in this context; the
channel assigned could be a traffic channel (TCH) or a slower signalling
channel SDCCH.

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147 The authentication of the MS and the switch to encrypted connection are
the following stages required for communication security.TCH is now
assigned by the system (if this has not been done). This has the advantage
of requiring an SDCCH only during the initial setup phases. No TCH has
been blocked if the setup fails. Using a TCH from the start, on the other
hand, has a speed advantage.
The actions that follow are dependent on whether you're using MTC or
MOC. If someone calls the MS, it now responds with 'alerting,' indicating
that the MS is ringing, and 'connect,' indicating that the user has clicked
the connec t button. If the MS has initiated the call, the identical
procedures occur in reverse. Both parties can share data after the
connection is acknowledged.
The connection is closed by sending a user -initiated disconnect message
(on both sides), then relinquis hing the connection and the radio channel.
7.7 HANDOVER
Handover or handoff refers to the procedure of transferring ongoing call or
data connectivity from one Base Station to another in cellular
telecommunications. When a phone goes to a different cell whi le a call is
in progress, the MSC (Mobile Switching Center) transfers the call to a
new channel associated with the new Base Station.

When a mobile user A moves from one cell to another, the signal strength
of BSC 1 decreases while the signal strength of BSC 2 improves, allowing
the mobile user to continue making calls or accessing data without
interruption.
7.7.1 Types of Handoff
1] Hard handoff
When transitioning from one Base Station to another Base Station, there is
an actual interruption in connectivity. T he Base Station and MSC are not
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148 aware of it. The quality of the connection is poor. The 'break before make'
policy was implemented by Hard Handoff.

2] Soft handoff
When radio signa ls are added or removed from the Base Station, at least
one of the links is retained in Soft Handoff. The'make before break'
principle was implemented by Soft Handoff. Hard Handoff is more
expensive than Soft Handoff.

7.7.2 Situations for triggering hand off
Handoffs occur in any of the following circumstances:
 When a subscriber in a call or data session travels out of one cell's
coverage region and into another cell's coverage area, a handoff is
triggered to ensure service continuity. The duties that the first cell was
performing are now being delegated to the second cell.
 Each cell has a specified capacity, which means it can only serve a
certain number of subscribers. A handoff occurs when the number of
users using a particular cell hits its maximum capa city. If the
subscriber is within the overlapping service area of both cells, some
calls are moved to nearby cells.
 Microcells are often subdivided from larger cells. When
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149 and vice ve rsa, a handoff may occur. A travelling user, for example, is
moving inside the jurisdiction of a huge cell. If the traveller comes to a
halt, the jurisdiction is moved to a microcell to relieve the large cell's
load.
 Handoffs can also happen when multiple calls using the same
frequency for communication collide.
7.7.3 Mobile assisted handoff
Mobile assisted handoff(MAHO) is a mechanism in which mobile devices
assist the Base Station Controller (BSC) in transferring a call to a different
BSC. GSM cellular ne tworks employ it. A handoff in other systems, such
as AMPS, is completely the responsibility of the BSC and the Mobile
Switching Centre (MSC), with no involvement from the mobile device.
When a mobile station in GSM is not using its time slots for
communic ation, it measures signal quality and communicates that
information to the BSC. This information is used by the BSC to complete
handoff.
7.8 SECURITY
GSM provides a variety of security services based on information saved in
the AuC and individual SIMs (whi ch is plugged into an arbitrary MS). The
SIM card stores personal and confidential information and is secured with
a PIN to prevent unwanted access.(The secret key Ki, for example, is
saved in the SIM and is used for authentication andencryption
procedures .) GSM's security services are described in detail below:
 Access control and authentication: The first step is to verify that the
SIM user is legitimate. To utilise the SIM, the user must enter a secret
PIN.The subscriber authentication is the next stage. A challenge -
response method is used in this step.

 Confidentiality: All user -related data is encrypted for privacy.
Following authentication, the BTS and MS encrypt speech, data, and
signalling. This level of confidentiality occurs just between MS and
BTS, not from end to end or throughout the entire fixed
GSM/telephone network.

 Anonymity: All data is encrypted before transmission to ensure user
anonymity, and user identifiers (which might indicate an identity) are
not used over the air. Instead, GSM sends out a temporary
identification (TMSI) that the VLR assigns after each position update.
The TMSI can also be changed by the VLR at any moment.
In order to provide security services in GSM, three algorithms have been
specified.A3 is used for authentication, A5 is used for encryption, and A8
is used to generate a cypher key. Only algorithm A5 was publicly
disclosed in the GSM standard, but algorithms A3 and A8 were kept secret
but standardised with open interfaces. Both A3 and A8 are no longer
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150 that security by obscurity is ineffective.The algorithms, it turned out, aren't
very good. Users can employ greater end -to-end encryption or network
providers can utilise stronger authentication te chniques. Algorithms A3
and A8 (or their equivalents) are proprietary and can be found on the SIM
and in the AuC.Only A5 must be identical across all providers in terms of
device implementation.
4.8.1 Authentication
A subscriber must be authenticated befor e he or she may utilise any GSM
network service. The SIM, which stores the individual authentication key
Ki, the user identity IMSI, and the authentication algorithm A3, is used for
authentication. The challenge -response approach is used for
authentication : the access control AC generates a random number RAND
as a challenge, and the SIM within the MS responds with SRES (signed
response) (Refer following Figure). For each IMSI, the AuC generates the
basic random values RAND, signed answers SRES, and cypher k eys Kc,
then sends this information to the HLR. The present VLR asks the HLR
for the necessary RAND, SRES, and Kc values.

Figure: Subscriber authentication
The VLR delivers the SIM the random value RAND for
authentication.With RAND and the key Ki, dubbed A3, both the network
and subscriber modules conduct the identical function. The MS returns the
SIM's SRES, allowing the VLR to compare the two values. The VLR
approves the subscriber if they are the same; otherwise, the subscriber is
rejected.
4.8.2 Encry ption
All transmissions including user -related information are encrypted in
GSM over the air interface to preserve privacy. MS and BSS can start
employing encryption after authentication by applying the cypher key K c
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151 is vendor dependant). The algorithm A8 is used to produce K c from the
individual key K i and a random value. It's worth noting that the SIM in the
MS and the network both use the same random number RAND to
calculate K c. Over the air interface, the key K c is not communicated.
The algorithm A5 and the cypher key K c can now be used by MS and BTS
to encrypt and decode data. As seen in the diagram below, K c should be a
64-bit key, which isn't particularly strong but provides ad equate security
against simple eavesdropping.However, the internet publishing of A3 and
A8 revealed that in some implementations, 10 of the 64 bits are always set
to 0, resulting in a key length of only 54 bits. As a result, the encryption is
significantly less secure.

Figure: Data Encryption
7.9 DECT: SYSTEM ARCHITECTURE
Depending on its intended function, a DECT system may have a variety of
distinct physical implementations. Different DECT entities can be spread,
replicated, etc., and merged into a sing le physical unit. On the same
logical reference model of the system architecture, as depicted in
followingfigure, all implementations are based.

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152 The local communication system is linked to the outside wor ld via a
global network, which provides its services over the D1 interface. Public
switched telephone networks (PSTN), public land mobile networks
(PLMN), such as GSM, or packet switched public data networks are
examples of global networks. (PSPDN). These networks provide a variety
of services, such as data transportation, address translation, and data
routing between local networks.
In the DECT environment, local networks provide local
telecommunication services that can range from straightforward switchin g
to clever call forwarding, address translation, etc. Such networks include
analogue or digital private branch exchanges (PBXs) or LANs, such as
those that adhere to the IEEE 802.x family of LAN standards.All normal
network tasks must be integrated in the local or global network, where the
databases home data base (HDB) and visitor data base (VDB) are also
located, despite the DECT system's relatively simple core. With features
that are comparable to those in the HLR and VLR in GSM systems, both
databases facilitate mobility. Incoming calls are automatically routed to
the DECT user's current subsystem, and the current VDB notifies the
HDB of any location changes.
The fixed radio termination (FT) and portable radio termination (PT) make
up the DECT core netw ork, which essentially just offers a multiplexing
service. At the fixed network side and mobile network side, respectively,
FT and PT cover layers one through three. Additionally, a device may
support a number of portable applications (PA).
7.10 PROTOCOL A RCHITECTURE
The OSI reference model is followed by the DECT protocol reference
architecture. The physical layer, medium access control, and data link
control8 for both the control plane (C -Plane) and the user plane are
represented in the following diagram as the layers covered by the standard.
(U-Plane). User data from layer two is transferred directly to the U -Plane
thanks to the specification of an additional network layer for the C -
Plane.All lower layers of a DECT system are vertically covered by a
manag ement plane.

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153 7.10.1 Physical layer
The physical layer of every wireless network includes all operations for
modulation and demodulation, incoming signal detection, sender/receiver
synchronization, and gathering status data fo r the management plane. The
physical channel structure is generated by this layer with a predetermined,
guaranteed throughput. The physical layer assigns a channel for data
transmission in response to a request from the MAC layer.
7.10.2 Medium access cont rol layer
By activating and deactivating physical channels, the media access control
(MAC) layer creates, maintains, and releases channels for higher layers.
Multiple logical channels are multiplexed onto physical channels using
MAC. There are logical chan nels for broadcast messages, user data
transmission, paging, and signalling network control. The segmentation
and reassembly of packets as well as error control and error correction are
additional services provided.
7.10.3 Data link control layer
The data link control (DLC) layer creates and maintains reliable
connections between the mobile terminal and the base station. Two
services have been defined for the C -Plane: a connectionless broadcast
service for paging (called Lb) and a point -to-point protocol si milar to
LAPD in ISDN, but adapted to the underlying MAC (called LAPC+Lc).
Several services exist for the U -Plane, e.g., a transparent unprotected
service (basically a null service), a forward error correction service, rate
adaptation services, and service s for future enhancements. If services are
used, e.g., to transfer ISDN data at 64 kbit/s, then DECT also tries to
transfer 64 kbit/s. However, in case of errors, DECT raises the transfer
rate to 72 kbit/s, and includes FEC and a buffer for up to eight blo cks to
perform ARQ. This buffer then introduces an additional delay of up to 80
ms.
7.10.4 Network layer
DECT's network layer, which only exists for the C -Plane, is comparable to
those in ISDN and GSM. This layer offers services for resource requests,
checks, reservations, controls, and releases at fixed stations (wireless
connections to fixed networks) and mobile terminals (wireless
connection). Identity management, authentication, and location database
management are all handled by the mobility management (MM)
component of the network layer. Setup, release, and negotiation of
connections are handled by call control (CC). The interworking unit that
links the DECT system with the outside world is connected by two
message services, the connectionless message service (CLMS) and the
connectionless message service (COMS).

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154 7.11 TETRA
Another means of transmitting wireless data are truncated radio
systems.These systems employ a wide range of radio carriers, but they
only ever temporarily pair a user with a partic ular carrier based on
demand. While traditional systems require organisations like taxi services,
transportation providers with fleet management systems, and rescue teams
to each have their own distinct carrier frequency, trunked radio systems
allow these organisations to share a large number of frequencies for
improved frequency reuse using FDM and TDM approaches. Although
they are not accessible to the general public, these radio systems
frequently provide interfaces to the fixed telephone network, includ ing
voice and data services.These networks are not only more straightforward
than the majority of other networks, but also more dependable and
reasonably priced to set up and run since they just need to service the local
users' operating areas, such as a c ity taxi service.
ETSI standardised the TETRA system (terrestrial trunked radio)9 in 1991
to enable a uniform system across Europe (ETSI, 2002; TETRA MoU,
2002). This system should take the place of regional systems that
commonly connect to an X.25 packet network, such as MODACOM,
MOBITEX, and COGNITO in Europe.TETRA offers two standards: the
Voice+Data (V+D) service (ETSI, 1998l) and the packet data optimized
(PDO) service (ETSI, 1998m). While V+D offers circuit -switched voice
and data transmission, PDO on ly offers packet data transmission, either
connection -oriented to connect to X.25 or connectionless for the ISO
CLNS (connectionless network service). The latter service can be point -to-
point or point -to-multipoint, the typical delay for a short message (1 28
byte) being less than 100 ms. V+D connection modes comprise unicast
and broadcast connections, group communication within a certain
protected group, and a direct ad hoc mode without a base station.
However, delays for short messages can be up to 500 ms or higher
depending on the priority.
TETRA also offers bearer services of up to 28.8 kbit/s for unprotected
data transmission and 9.6 kbit/s for protected transmission. Examples for
end-to-end services are call forwarding, call barring, identification, cal l
hold, call priorities, emergency calls and group joins. The system
architecture of TETRA is very similar to GSM. Via the radio interface
Um, the mobile station (MS) connects to the switching and management
infrastructure (SwMI), which contains the user d ata bases (HDB, VDB),
the base station, and interfaces to PSTN, ISDN, or PDN. The system itself,
however, is much simpler in real implementation compared to GSM, as
typically no handover is needed. Taxis usually remain within a certain area
which can be co vered by one TETRA cell. Several frequencies have been
specified for TETRA which uses FDD (e.g., 380 –390 MHz uplink/390 –
400 MHz downlink, 410 –420 MHz uplink/420 –430 MHz downlink). Each
channel has a bandwidth of 25 kHz and can carry 36 kbit/s. Modulation i s
DQPSK. While V+D uses up to four TDMA voice or data channels per
carrier, PDO performs statistical multiplexing. For accessing a channel,
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155 7.12 UMTS AND IMT - 2000
Figure below depicts a simplified UMTS reference design that applies to
both UTRA and non -UTRA solutions (3GPP, 2000). The UTRA network
(UTRAN) is a radio network subsystem that manages cell level mobility
(RNS). Radio channel ciphering and deciphering, handover control, radio
resource management, and other services are all performed by the RNS.
The radio interface U u (which is similar to the Um interface in GSM)
connects the UTRAN to the user equipment (UE). UTRAN interfaces with
the core network(CN) via the I u interface (which is comparable to the A
interface in GSM). If t here is no dedicated connection between the UE and
the UTRAN, the CN contains capabilities for inter -system handover,
gateways to other networks (fixed or wireless), and location management.

Figure: Main components of UMTS system architecture
The followi ng basic architecture is further subdivided into domains by
UMTS (Refer figure below). A single user is assigned to the user
equipment domain, which contains all of the functionalities required to
access UMTS services. The USIM domain and the mobile equipm ent
domain are both contained under this domain. The USIM domain contains
the UMTS SIM, which handles encryption and authentication operations
for users and maintains all user -related data for UMTS. This USIM is
usually associated with a service provider a nd includes a microprocessor
for a better programme execution environment (USAT, UMTS SIM
application toolkit). The end device is classified as mobile equipment. All
radio transmission functions, as well as user interfaces, are housed here.

Figure: UMTS domain and interfaces
All users share the infrastructure domain, which provides UMTS services
to all accepted users. The access network domain, which houses radio
access networks (RANs), and the core network domain, which houses
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156 network domain can be divided into three distinct domains, each with its
own set of tasks. All functions currently employed by a user to access
UMTS services are included in the serving network domain. The home
network domain encompasses all functions connected to a user's home
network, such as user data look -up. Finally, the transit network domain
may be required if the serving network is unable to communicate directly
with the home network.The core network's three domains could all be the
same physical network. These domains are merely functional descriptions.
7.13 SATELLITE SYSTEMS: HISTORY,
APPLICATIONS
 HISTORY
After World War II, satellite communications were first developed. The
ability to create rockets t hat could launch radio transmitters into orbit was
known to scientists. Arthur C. Clarke's essay on "Extra Terrestrial Relays"
was published in 1945. However, it wasn't until 1957, in the midst of the
Cold War, when the Soviet Union abruptly launched the f irst satellite
SPUTNIK, shocking the Western world.SPUTNIK was essentially a small
sender that transmitted a sporadic "beep," which is in no way analogous to
a satellite today. However, this was sufficient for the US to devote all of
its resources to creat ing its first satellite. The first reflecting
communication satellite ECHO was launched into space in about three
years, in 1960.ECHO was in space. ECHO was essentially a mirror in the
sky that reflected signals to allow for communication. The first
geosta tionary (or geosynchronous) satellite, SYNCOM, was launched
three years later. Geostationary satellites still serve as the foundation for
aerial news broadcasts today. Their permanent position in the sky is their
main advantage. They appear to be anchored to a specific point because
their spin is synchronised with the earth's rotation.
The first commercial geostationary communication satellite INTELSAT 1
(also known as ‘Early Bird’) went into operation in 1965. It was in service
for one -and-a-half years, we ighed 68 kg and offered 240 duplex telephone
channels or, alternatively, a single TV channel. INTELSAT 2 followed in
1967, INTELSAT 3 in 1969 already offered 1,200 telephone channels.
While communication on land always provides the alternative of using
wires, this is not the case for ships at sea. Three MARISAT satellites went
into operation in 1976 which offered worldwide maritime communication.
However, Sender and receiver still had to be installed on the ships with
large antennas (1.2 m antenna, 40 W tra nsmit power). The first mobile
satellite telephone system, INMARSAT -A, was introduced in 1982. Six
years later, INMARSAT -C became the first satellite system to offer
mobile phone and data services. (Data rates of about 600 bit/s, interfaces
to the X.25 pac ket data network exist.) In 1993, satellite telephone systems
finally became fully digital with INMARSAT -M. The actual mobility,
however, was relative from a user’s point of view, as the devices needed
for communication via geostationary satellites were he avy (several
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157 Nineteen ninety -eight marked the beginning of a new age of satellite data
communication with the introduction of global satellite systems for small
mobile phones, such as, e.g., Iridium and Globalstar. There are currently
almost 200 geostationary satellites in commercial use which shows the
impressive growth of satellite communication over the last 30 years.
However, satellite networks are currently facing heavy competi tion from
terrestrial networks with nationwide coverage or at least enough coverage
to support most applications and users.
 APPLICATIONS
Satellites have historically been applied in the following fields:
 Weather prediction: Several satellites send images o f the world using
infrared or visible light, for example. It would be impossible to
forecast hurricanes without the assistance of satellites.
 Radio and TV broadcast satellites: Satellites used for radio and TV
broadcasts make thousands of radio and televi sion programmes
accessible. Since it costs less to install and typically requires no
additional fees, this technology competes with cable in many
locations. In central Europe, modern satellite dishes have diameters of
30 to 40 cm. (the diameters in norther n countries are slightly larger).
 Military satellites: One of the earliest applications of satellites was
their use for carrying out espionage. Many communication links are
managed via satellite because they are much safer from attack by
enemies.
 Satellite s for navigation: The global positioning system (GPS), which
was once primarily utilised for military purposes, is today well -known
and accessible to everybody. Worldwide exact localization is possible
with the technology, and with some additional techniqu es, the
precision can reach a few metres. The majority of ships and aircraft
use GPS in addition to more conventional navigation systems. GPS
receivers are commonly seen in trucks and autos. This technology is
also employed, for instance, to manage the tru ck fleet or to locate
stolen vehicles.
7.14 BASICS: GEO, LEO, MEO
 Geostationary (or geosynchronous) earth orbit (GEO): GEO
satellites have a distance of almost 36,000 km to the earth. Examples
are almost all TV and radio broadcast satellites, many weather
satellites and satellites operating as backbones for the telephone
network

 Medium earth orbit (MEO): MEOs operate at a distance of about
5,000 –12,000 km. Up to now there have not been many satellites in
this class, but some upcoming systems (e.g., ICO) use this class for
various reasons
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158  Low earth orbit (LEO): While some time ago LEO satellites were
mainly used for espionage, several of the new satellite systems now
rely on this class using altitudes of 500 –1,500 km

 Highly elliptical orbit (HEO): This class comprises all satellites with
noncircular orbits. Currently, only a few commercial communication
systems using satellites with elliptical orbits are planned. These
systems have their perigee over large cities to improve communication
quality.

7.15 SUMMARY
GSM has been shown as the most successful second generation digital
cellular network for the most part in this chapter. Although GSM was
originally developed for voice communication, the chapter demonstrated
how allows for more data -oriented trans mission. This evolution comprises
the move from a circuit -switched network to a packet -switched system
that is more similar to the internet architecture.Other systems presented
include DECT, thedigital standard for cordless phones, and TETRA, a
trunked rad io system. DECT can be used for wireless data transmission on
a campus or indoors, but also for wireless local loops (WLL). For special
scenarios, e.g., emergencies, trunked radio systems such as TETRA can be
the best choice. They offer a fast connection s etup (even within
communication groups) and can work in an ad hoc network, i.e., without a
base station. This chapter also presented an overview of current and future
third generation systems. UMTS, a proposal of operators and companies
involved in the GSM business, was discussed in more detail.
7.16 LIST OF REFERENCES
1) Protocols and Architectures for Wireless Sensor Network, Holger
Kerl, Andreas Willig, John Wiley and Sons, 2005
2) Wireless Sensor Networks Technology, Protocols, and
Applications,Kazem Sohrab y, Daniel Minoli and TaiebZnati, John
Wiley & Sons, 2007
3) Mobile communications, Jochen Schiller,2nd Edition, Addison wisely,
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159 4) Fundamentals of Wireless Sensor Networks, Theory and Practice,
Waltenegus Dargie, Christian Poellabauer, W iley Series on wireless
Communication and Mobile Computing, 2011
5) Networking Wireless Sensors, Bhaskar Krishnamachari, Cambridge
University Press, 2005
7.17 UNIT END EXERCISES
1) State Mobile services.
2) Explain GSM System architecture.
3) Write a note on Radio i nterface.
4) Illustrate different Protocols.
5) Explain Localization and Calling.
6) Write a note on Handover.
7) Describe different Security.
8) Explain DECT: System architecture.
9) Explain the protocol architecture.
10) Write a note on TETRA.
11) Explain UMTS and IMT - 2000.
12) Desc ribe: Satellite Systems: History, Applications
13) Write a basic note on: GEO, LEO, MEO

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