Multiple Criteria Decision Making based

Clustering Technique for WSNs

By

Mansoor Mustafa

Registration Number: -REE-044/ISB

MS Thesis

In

Electrical Engineering

COMSATS Institute of Information Technology

Islamabad – PakistanSpring, 2013

CIIT/FA11

Multiple Criteria Decision Making based

Clustering Technique for WSNs

A Thesis presented to

COMSATS Institute of Information Technology

In partial fulfillment

of the requirement for the degree of

MS (Electrical Engineering)

By

Mansoor Mustafa

CIIT/FA11-REE-044/ISB

SPRING, 2013

COMSATS Institute of Information Technology

ii

Engineering).

Multiple Criteria Decision Making based

Clustering Technique for WSNs

A post Graduate Thesis submitted to Department of Electrical Engineering as

partial fulfillment of the requirement for the award of Degree of M.S

(Electrical

Name Registration Number

Mansoor Mustafa CIIT/FA11-REE-044/ISB

Supervisor

Dr. Safdar H. Bouk

Assistant Professor, Department of Electrical Engineering

Islamabad Campus

COMSATS Institute of Information and Technology (CIIT)

Islamabad

Co-Supervisor

Dr. Nadeem Javaid

Assistant Professor,

Center for Advanced Studies in Telecommunications (CAST),

COMSATS Institute of Information and Technology (CIIT)

Islamabad

Spring, 2013

iii

Final Approval

This thesis titled

Multiple Criteria Decision Making based

Clustering Technique for WSNs

By

Mansoor Mustafa

CIIT/FA11-REE-044/ISB

Has been approved

For the COMSATS Institute of Information Technology, Islamabad

External Examiner: __________________________________

(To be decided)

Supervisor: ________________________

Dr. Safdar H. Bouk/Assistant Professor,

Department of Electrical Engineering CIIT, Islamabad

Co-Supervisor: ________________________

Dr. Nadeem Javaid/Assistant professor,

Center for Advanced Studies in Telecommunications (CAST),

CIIT, Islamabad.

Head of Department:________________________

Dr. Raja Ali Riaz / Associate professor,

Department of Electrical Engineering,

CIIT, Islamabad.

iv

Declaration

I Mansoor Mustafa , CIIT/FA11-REE-044/ISB hereby declare that I have

produced the work presented in this thesis, during the schedule period of study. I

also declare that I have not taken any material from any source except referred to

wherever due that amount of plagiarism is within acceptable ra nge. If a violation

of HEC rules on research has occurred in this thesis, I shall be liable to punishable

action under the plagiarism rule of the HEC.

Date: ___________

_____________

Mansoor Mustafa

CIIT/FA11-REE-044/ISB

v

Certificate

Date: _________________

Supervisor: ________________________

Dr. Safdar H. Bouk/Assistant Professor,

Department of Electrical Engineering CIIT, Islamabad

Co-Supervisor: ________________________

Dr. Nadeem Javaid/Assistant professor,

Center for Advanced Studies in Telecommunications (CAST),

CIIT, Islamabad.

Head of Department:________________________

Dr. Raja Ali Riaz / Associate professor,

Department of Electrical Engineering,

CIIT, Islamabad.

It is certified that Mansoor Mustafa, CIIT/FA11-REE-044/ISB has carried out

all the work related to th is thesis under my supervision at the Department of

Electrical Engineering, COMSATS Institute of Information and Technology,

Islamabad and the work fulfills the requirements for award of MS degree.

vi

DEDICATION

Thisthesisisdedicatedtomyparents

Fortheirendlesslove,supportandencouragement

.

vii

ACKNOWLEDGMENT

Mansoor Mustafa

CIIT/FA11-REE-044/ISB

I thank to Almighty ALLAH for His Blessing and Guidance in completing this project

in time.

.

I would like to admit the great and unconditional academic support and

encouragement from my family. This success is all because of their prayers and

help in my university career.

I would also like to specially thank my highly regarded teacher and supervisor of

thesis Dr. Safdear Hussain Bouk and co-supervisor Dr. Nadeem Javaid for their

utmost help and precious guidance and expert advices during execution of the

work and throughout my studies at COMSATS Institute of Information Technology,

Islamabad.

I am extremely obliged to my research fellows Tauseef Shah , Imran Israr , Aziz-ur-

Rehman, Mohammad Mateen Yaqoob and Haad Akmal, who helped me in some

way or the other in completing my thesis.

At the end my gratitude is for my parents and friends who supported me morally

to reach this stage.

.

viii

ABSTRACT

Multiple Criteria Decision Making based Clustering

Technique for WSNs

Energy saving is a critical issue in Wireless Sensor Networks as they have limited

amount of energy and non rechargeable batteries. There are different met hods to

preserve energy in WSNs and clustering is one of them . Clustering plays an effective

role in utilization and saving of the limited energy resources of the deployed sensor

nodes, where nodes are grouped into clusters and one node, called the cluster head is

responsible for collecting data from other nodes, aggregates them and sends them to the

BS, where data can be retrieved later. Cluster head is responsible to provide

communication bridge between members and the base station. In this thesis , we

propose a distributed clustering scheme that uses multiple criteria i.e. residual energy,

node degree, distance to the base station and average distance between a node and its

neighbors, to select a cluster head. Fuzzy Technique for Preference by similarity to

Ideal Solution (Fuzzy-TOPSIS) method is used to outrank the potential nodes as cluster

heads. The realistic multi-hoping communication model is used in both, inter-cluster

and intra-cluster communication, instead of single hop as in previous schemes.

Simulation results show that our purposed technique performs almost five times better

than previous methods in terms of energy efficiency, network life time, less cluster

heads deformation and control overhead

ix

Table of Contents

1 Introduction 2

1.1 Brief Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.4 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Wireless Sensor Networks (WSNs) 8

2.1 Architecture of WSN Node . . . . . . . . . . . . . . . . . . . . . . . 9

2.1.1 Sensing Subsystem . . . . . . . . . . . . . . . . . . . . . . . 9

2.1.2 Processing Subsystem . . . . . . . . . . . . . . . . . . . . . 10

2.1.3 Communication Subsystem . . . . . . . . . . . . . . . . . . . 10

2.1.4 Power Subsystem . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Challenges in WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.1 Power Consumption/ Network Lifetime . . . . . . . . . . . . 12

2.2.2 Fault Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.3 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.4 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.5 Accuracy/Latency . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.6 Node Deployment . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.7 Data Aggregation . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2.8 Hardware Constraints . . . . . . . . . . . . . . . . . . . . . 13

2.2.9 Security Issues . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3 Applications of WSNs . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3.1 Military Applications . . . . . . . . . . . . . . . . . . . . . . 14

2.3.2 Environmental Monitoring . . . . . . . . . . . . . . . . . . . 14

2.3.3 Medical Applications . . . . . . . . . . . . . . . . . . . . . . 14

2.3.4 Other Applications . . . . . . . . . . . . . . . . . . . . . . . 15

2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

x

3 WSN Network Layer 18

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.2 Routing Challenges and Design Issues in WSNs . . . . . . . . . . . 19

3.3 RoutingIProtocols in WSNs . . . . . . . . . . . . . . . . . . . . . . 20

3.3.1 Protocols based on Network Organization . . . . . . . . . . 20

3.3.2 ProtocolsIbased onIProtocol Operation . . . . . . . . . . . . 23

3.3.3 Protocols based on Route Discovery . . . . . . . . . . . . . . 24

3.4 Clustering in WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4.1 Energy Saving Schemes in WSN Clustering . . . . . . . . . . 25

3.4.1.1 Cluster Formation and Rotation . . . . . . . . . . 25

3.4.1.2 Cluster Head Election and Rotation . . . . . . . . 27

3.4.1.3 Efficient Intra-cluster Communication . . . . . . . 28

3.5 Clustering protocols in WSNs . . . . . . . . . . . . . . . . . . . . . 29

3.5.1 Low-EnergyIAdaptiveIClusteringIHierarchy (LEACH) . . . . 29

3.5.2 Centralized LEACH (C-LEACH) . . . . . . . . . . . . . . . 31

4 Proposed Clustering Scheme 35

4.1 Energy Model for WirelessISensor Node . . . . . . . . . . . . . . . . 35

4.2 Proposed Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.2.1 Network Deployment Phase . . . . . . . . . . . . . . . . . . 37

4.2.2 Neighbor Discovery Phase . . . . . . . . . . . . . . . . . . . 38

4.2.3 CH selection and Cluster Formation Phase . . . . . . . . . . 38

4.2.4 Communication Phase . . . . . . . . . . . . . . . . . . . . . 42

5 Simulation and Results 45

5.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . 45

5.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2.1 Number of Dead Nodes . . . . . . . . . . . . . . . . . . . . . 46

5.2.2 Number of Alive Nodes . . . . . . . . . . . . . . . . . . . . . 47

5.2.3 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . 47

5.2.4 Change of Cluster Head . . . . . . . . . . . . . . . . . . . . 48

5.2.5 Control Overhead (Hello) Packets . . . . . . . . . . . . . . . 49

5.2.6 Packets Sent to Base Station . . . . . . . . . . . . . . . . . . 49

6 Conclusion 52

References 52

xi

List of Figures

2.1 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 WSN Node Architecture . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 Environmental Applications of WSNs . . . . . . . . . . . . . . . . 15

2.4 Medical Application of WSNs . . . . . . . . . . . . . . . . . . . . . 15

3.1 Single-hop routing versus multi-hop routing model . . . . . . . . . . 18

3.2 ClassificationIof WSN RoutingIProtocols . . . . . . . . . . . . . . . 21

3.3 Architecture of LEACH . . . . . . . . . . . . . . . . . . . . . . . . 30

4.1 Wireless Sensor Node Energy Model . . . . . . . . . . . . . . . . . 35

4.2 Sensor Nodes Deployed in Field . . . . . . . . . . . . . . . . . . . . 37

4.3 Procedure for CH Change . . . . . . . . . . . . . . . . . . . . . . . 41

4.4 Inter and Intra-cluster Communication . . . . . . . . . . . . . . . . 42

4.5 Flow Diagram of Proposed Scheme . . . . . . . . . . . . . . . . . . 43

5.1 Number of Dead Nodes . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2 Number of Alive Nodes . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.3 Energy Consumption of Network . . . . . . . . . . . . . . . . . . . 48

5.4 Change of CHs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.5 Control Overhead Packets . . . . . . . . . . . . . . . . . . . . . . . 50

5.6 Packets sent to BS . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

xii

List of Tables

3.1 Difference between flat and hierarchical routing . . . . . . . . . . . 22

3.2 Detailed Comparison of WSN Clustering Protocols [30] . . . . . . . 33

4.1 Criteria Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2 Fuzzy Membership Functions . . . . . . . . . . . . . . . . . . . . . 39

5.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 45

xiii

Chapter 1

Introduction

1

Chapter 1

Introduction

Rapid advances in Wireless Sensor Networks (WSNs) have enabled densely deploy-

ment of nodes. WSNs is an emerging technology that consists of large number of

low cost, low power sensor nodes; a sensor node is an electronic device that is ca-

pable of detecting environmental conditions. Those sensor nodes can be deployed

randomly to perform many applications such as monitoring physical events, for

example environmental monitoring, battlefield surveillance, disaster relief, target

tracking, etc. and they work together to form a wireless sensor network.

1.1 Brief Overview

A typical node of a WSN is equipped with four components [1]: a sensor that

performs the sensing of required events in a specific field, a radio transceiver that

performs radio transmission and reception, a microcontroller, which is used for

data processing and a battery that is a power unit providing energy for operation.

The limited energy given to each node, supplied from non-rechargeable batteries,

with no form of recharging after deployment is one of the most crucial problems

in WSNs. Many routing protocols have been proposed for WSNs. Most of the

hierarchical algorithms proposed for WSNs concentrate mainly on maximizing the

lifetime of the network by trying to minimize the energy consumption [1].

Researchers agreed that clustering of nodes in wireless sensor networks is an effec-

tive program of energy conservation [2]. Clustering is defined as the grouping of

similar objects or the process of finding a natural association among some specific

objects or data. In WSNs it is used to minimize the number of nodes that take

part in long distance data transmission to a Base Station (BS), what leads to

lowering of total energy consumption of the system.

2

Clustering reduces the amount of transmitted data by grouping similar nodes

together and electing one node as a Cluster Head (CH), where aggregation of data

is performed to avoid redundancy and communication load caused by multiple

adjacent nodes, then sending the aggregated data to the next CH or to the BS,

where it is processed, stored and retrieved.

In any clustering organization intra-cluster communication as well as inter-cluster

communication can be single hop or multi-hop. However, the hot spot and network

partitioning arises when using multi-hop routing in inter-cluster communications.

Because the CHs close to the BS, are burdened with heavy relayed traffic that will

make them die faster than other CHs, resulting in loss of coverage of sensing.

CH selection in any routing protocol is most challenging issue, because network

lifetime and stability strongly depends on selected CH. Research has proved that

CH selected on single criterion is to not much energy efficient. Hence an ideal CH

is one which is selected on multiple criteria. Solution of using multiple criteria can

be solved using Multiple Criteria Decision Making (MCDM)[3] technique.

MCDM methods are used to solve the decision making problem in eld of engineer-

ing and sciences, with multiple attributes. MCDM techniques compare and rank

multiple alternatives based on degree of desirability of their respective attributes

[3]. There are different types of MCDM approach. Fuzzy logic and fuzzy set the-

ory is applied to decision making process. TOPSIS is a solution for multi-criteria

optimization problem. TOPSIS was initially proposed by Hwang and Yoon [3].

Fuzzy-TOPSIS consists of decision matrix with m number of a alternatives and n

number of attributes for each alternative. This technique is applied is scientic and

engineering problem solving. Fuzzy-TOPSIS uses relative importance of attributes

instead of using precise values, because is some situations precise assignment is

not possible due to any reason.

1.2 Motivation

Low-power electronic devices have grown interest in recent years. WSNs use these

many low-power devices along with communication capabilities for sensing and

monitoring various fields. Major areas of WSNs include environmental sensing

of temperature and humidity, earthquake monitoring, healthcare monitoring and

battle field surveillance [4]. In some applications sensor nodes are deployed in

strategic manner but in most of the applications like battle field, these nodes are

dispersed randomly.

3

After deployment in the field, sensor nodes have to be self organized without hu-

man interference. These sensor nodes consist of battery operated radio devices,

which have limited memory and processing capabilities. Their batteries cannot

be charged or replaced after deployment. Hence, energy is one of the major is-

sues in WSNs. The sensor nodes not only sense data but they also process data

and communicate with the BS. The processing and communication also consume

energy and it is major requirement of WSNs, therefore, it has to is to be energy

efficient. lots of research have been conducted in WSNs on energy efficiency to

prolong network life time and stability. This research includes design of various

energy efficient MAC and routing protocols. Routing protocols may be either be

flat or hierarchical. In flat protocols, each sensor node sends data to BS directly or

in a multi-hop fashion. On the other hand, in hierarchical architecture, WSNs are

divided into optimum number of groups or clusters. Inside each cluster a cluster

head (CH) is selected to perform management and routing tasks for that cluster.

Research has proved that hierarchical protocols perform much better than the flat

protocols in terms of energy efficiency and stability. The CH is selected either

randomly or based on some criteria [5]. After selection of a CH, other nodes join

that CH and act as member nodes. These member nodes send their data to CH,

which then aggregates the data and sends to the BS. Hence, the selection of a CH

largely affects whole network’s performance and stability. In most of the previous

clustering schemes, CH selection is based on single criterion. In single criterion,

CH is mostly selected randomly or based on residual energy, node density or

distance form BS. If CH is selected on the basis of residual energy, then problem

arises when a node with higher residual energy but located far away form BS is

selected as CH. That node consumes more energy to forward aggregated data to

BS. Similarly if CH selected no the bases of shortest distance form BS, then similar

type of problem arises if node near to BS is selected as CH, but with not sufficient

residual energy to communicate with BS. Hence single criterion is not sufficient

for CH selection. So the motivation behind this work is to design a clustering

technique which is based on multiple criteria rather than single criterion, also

which overcomes the bottlenecks in previous proposed clustering protocols.

1.3 Problem Statement

In most of the existing clustering techniques CH is selected randomly and based

on single criterion. They use centralized scheme, means BS performs CH selection

process. Also they use single hop communication model. In previous protocols CH

4

is changing in every round. The bottlenecks of previous protocols are discussed

below.

• Problems with single criterion:

– Mostly based on residual energy

– Dont consider other information, like location of nodes, number of

neighbor nodes etc

– So normal nodes consumes more energy to send their data to CHs.

• Problems with centralize algorithm:

– Increased processing over head. Results in shorter lifetime of nodes.

• Problems with single hop communication:

– Data from nodes away form CHs/BS have to travel longer distance as

compared to nearer nodes, hence they die earlier.

• Problems with frequent CH change:

– Frequent CH change increases processing over head. Results in shorter

lifetime of nodes.

To overcome these problems, we proposed a technique based on multiple crite-

ria, in which we use distributed algorithm of CH selection i-e nodes themselves

decide whether to become CH or not. Our proposed scheme is based of MCDM

fuzzy-TOPSIS method, we consider four criteria for CH selection which are resid-

ual energy, number of neighbors, average distance from neighbors and distance

between node and BS. In our proposed scheme we avoid quick deformation of CH,

which in result reduces control overhead. Because of reduction in control over-

head energy consumption is minimized. In our proposed scheme we use realistic

communication model by introducing multi-hop communication model in both

intra-cluster (communication between normal nodes and CH) and inter-cluster

(communication between CH and BS)

1.4 Research Methodology

In this work we comprehensively analyse the bottlenecks in existing clustering pro-

tocols, and design a new clustering scheme based on multiple criteria. Different

weights are assigned to each criteria according to their importance. Each node

5

share all four criteria value with all its neighbors, then rank index value is calcu-

lated for each node. The node with highest rank, elect itself as CH. The elected

CHs remain CHs until their rank index value differ from any other node value

by 0.1. (threshold value). In intra cluster, nodes with 5m range of CH directly

communicate to CH, other nodes perform multi-hoping with neighbor nodes. Sim-

ilarly, in inter cluster CHs within 20m range of BS directly communicate with BS,

remaining CHs perform multi-hoping with neighbor CHs.

1.5 Thesis Organization

Remaining thesis is organized as follows:

Chapter 2 provides the background of Wireless Sensor Networks (WSNs). This

include basic architecture of wireless sensor node, current challenges in WSNs, like

power consumption, network life time, scalability etc. and common applications

of WSNs, like military, environment and currently merging healthier applications.

Chapter 3 discusses WSNs network layer. This includes brief introduction, routing

challenges in WSNs, and Types of routing protocols. In this chapter we also

discuss concept of clustering in WSNs, and provide overview of existing clustering

protocols along with their working principle.

Chapter 4 describes our proposed clustering scheme. This include basic energy

model of wireless sensor nodes and different phases of our proposed scheme, along

with complete mathematical equations, explanations and flow charts.

Chapter 5 discusses simulation parameters and simulation results of our proposed

scheme. In this chapter we compare the results of proposed scheme with previous

clustering techniques, and analyze the improvements in our proposed scheme in

terms of network lifetime and stability.

Chapter 6 concludes the manuscript of our research work

6

Chapter 2

Wireless Sensor Networks

(WSNs)

7

Chapter 2

Wireless Sensor Networks

(WSNs)

Wireless sensorInetworks areIgaining lot ofIattention in researchIareas as wellIas in

theIdevelopment of variousI applications. It has becomeIone of the leadingIand ef-

ficientItechnologies in wirelessIcommunication. TheseInetworks are usedIto mon-

itor differentIapplications suchIas snow monitoring, homeIand industry automa-

tion,Iand most importantlyIin military applications to monitorIthe information.

WSNIis a new network technologyIwhich integrates low-power communication,

sensor and Micro-Electro-Mechanics [MEMS] [6]. It is collection of many number

of sensor nodes which communicate themselves to acquire monitored information.

In WSNs sensor nodes can be deployed in either random (adhoc fashion) or in a

manual way depending upon the application. These networks which are grouped

with sensors are linked through a wireless medium to perform their required tasks.

Communication between these sensors is occurred with the help of infrared devices

or base stations or radios [7]. This radio network helps user to access informa-

tion from any remote location and allows to visualizing and analyzing the sensor

data. WSNs which consists of many number of sensor nodes are able to commu-

nicate within themselves and as well as with the BS. Each sensor device consists

of transceiver, a micro controller and is equipped with a power source which is

usually an AA and AAA batteries. The specifics of each WSN depend on the

nature of the application.

8

Figure 2.1: Wireless Sensor Networks

2.1 Architecture of WSN Node

Sensor nodes are small devices which are battery powered and are a part of this

typical wireless sensor network. A typical sensor node consists of four basic com-

ponents: sensing subsystem, processing subsystem, power subsystem and com-

munication subsystem [8]. The fig. 2.2 shown at the end of this section, is the

architecture of a single node. The explanation of each subsystem is given as fol-

lows:

2.1.1 Sensing Subsystem

Sensors play a crucial role in WSNs architecture as they establish a link between

the real-time world and the computational environment. Sensors are the hardware

devices which are used to monitor the data for required applications and to react

to the environmental changes. After sensing the environment, the function of the

sensor is to collect the sensed data and send it to further system for processing.

The energy in sensor nodes is transformed from one form to another form using

transducers. Sensor nodes normally include analog, digital and A/D converters

and a microcontroller [9]. Sensors are categorized depending on the application

and these can act according to the requirements of each application. Also, the

factors to choose in a sensor are size and battery consumption.

9

2.1.2 Processing Subsystem

Sensor nodes also consist of a processing unit along with memory units and con-

verters. The communication interface processes the data. Later the collected data

can be analyzed to verify the performance of the network. Here, the unit is re-

sponsible for adapting the routing information and align the topology if needed.

Also performs data gathering, data acquisition and however processes the received

data (incoming and outgoing) [10]. This subsystem also involves data fusion where

the different packets arriving from the sensor nodes are gathered to form a sin-

gle packet, thereby reducing the transmission energy between the sensor and user

(observer).

2.1.3 Communication Subsystem

This subsystem is responsible for the transmission of data. Sensor nodes use radio

frequencies to carry the signals from sensors through the BS to the required end

user. The role of the BS is to maintain the communication between sensor network

and external source (user). In a network, there can be a single or multiple base

stations depending upon the requirement, area and number of sensors to monitor.

In a network, each individual node communicates and co-ordinates with other

nodes. There are two types of communications: infrastructure and application

[11].Communication which is required to build, maintain, optimize a network is

referred as infrastructure. Due to environmental changes in the network there can

be a varying topology and sometimes nodes can fail. Therefore, these situations

can be managed by conventional protocols [11]. Hence even in a static sensor

network, there is a need of infrastructure communication and external commu-

nication which is required to re-configure the topology [11]. The data which is

gathered should be transferred further to the monitoring end and is referred as

application [11]. The amount of energy required to transmit a packet to sink is

depended on distance and more over the energy required for a node to transmit is

fixed. But, if the distance is far then that requires high amount of energy. Hence,

this can be eliminated by choosing the shortest path for transmission of data.

Also this communication refers to application based. For example, when there is

a necessity to communicate, nodes should communicate and data should be sent

continuously. Another example is when the application depends on event driven,

sensor suppose to act when the event or environment change occurs. Therefore, it

is good to decrease communication cost in order to increase life time of a network.

10

2.1.4 Power Subsystem

All the above mentioned subsystems require a power unit to function and perform

their individual tasks. Power subsystem provides the supply voltage and the

requirements of the power are strict due to energy consumption constraints. It also

supplies sufficient levels of current during the radio transmission and reception.

A battery can act as energy storage which is generally AA or AAA size and

voltage regulator is also included in the power subsystem. In most of the hardware

platforms there is a possibility to allow switching of the states i.e. between on, off

and idle for each device to minimize the power usage. These sensors collaborate

with each other at certain interval of time to carry out the required task. Data

from each sensor is collected and analyzed by a data processor (computer) outside

the network [12]. These nodes can be self organized and self healed depending

upon the routing topology that is used for the communication between them.

Also, it is very difficult to replace a sensor node if they are placed in extreme

geographical areas.

Figure 2.2: WSN Node Architecture

2.2 Challenges in WSNs

Before formation of the sensor network and deployment of sensor nodes the prior

and fundamental understanding about connecting and managing the network in

needed to achieve beneficial scalability and efficiency. Even though sensor net-

works are grouped under the class of ad-hoc networks but these differs with their

characteristics. Both ad-hoc and sensor networks share the challenges of energy

constraints and routing techniques [13]. Generally, in an ad-hoc networks nodes

are considered as mobile where as in the sensor networks nodes are static for most

11

of the applications such as military. Hence, these networks may differ in their traf-

fic patterns [13]. These are some of the most important aspects that the wireless

sensor networks should overcome, and they are described below.

2.2.1 Power Consumption/ Network Lifetime

Power consumption is one of the crucial challenges required to manage in sensor

networks. Many researchers are focusing their efforts to improve energy efficiency

in these networks [13]. As many of the sensors are battery powered, energy con-

sumption is a very crucial metric and should be managed wisely in order to extend

the network lifetime. For example in the military applications, it is difficult to

replace the batteries in the battle field. Hence the sensors may fail and might not

function if the batteries are exhausted. So, efficient routing may overcome this

issue and extend the network lifetime.

2.2.2 Fault Tolerance

While processing and communication between the sensors, some sensors may fail

to communicate because of link failures, lack of power supply or due to any phys-

ical damage or even by environmental interventions. In order to overcome these

mentioned problems, accommodation of new links is required. Also, maintaining

the transmission power and signaling rates; rerouting of packets and redundancy

is necessary to establish a robust and fault tolerant network.

2.2.3 Scalability

Scalability is a critical factor especially for sensor networks which contains many

number of nodes and can be responsible for degradation of network performance as

well. Topological changes in network such as network size and node density should

not affect the performance of the network. Hence, routing protocols employed in

WSN must be scalable enough to maintain the sensor states when it changes its

state from sleep to ideal or vice versa.

2.2.4 Throughput

Most of the times sensor must transmit its data to the BS, the required number

of successful packet transmission of a given node per time slot is determined as

12

throughput.

2.2.5 Accuracy/Latency

Acquiring the exact information without any distortions is the most primary objec-

tive in a WSNs. Also, there should not be any sort of delay. The routing protocols

and network topology will ensure the delivery of the data with minimum delay.

2.2.6 Node Deployment

The sensor nodes are placed manually in a random fashion and are deployed

depending upon the required application. Another way of deployment is self or-

ganizing systems, where the sensor nodes are scattered and topology is formed in

an ad-hoc manner. Uniform distribution of nodes and optical clustering schemes

can efficiently maintain the network [14].

2.2.7 Data Aggregation

Data aggregation is the combination of data arriving from different sources by

using some functions such as suppression (finding and eliminating duplicates),

minimum, maximum and average [14]. As sensor node generates the meaningful

data, data from multiple nodes can be aggregated in order to reduce the num-

ber of transmissions. This aggregation technique is used to reduce the energy

consumption and achieve data transfer optimization in the routing protocols.

2.2.8 Hardware Constraints

Since sensor nodes are very small in size and are operated under low power. These

have limited energy capacity, low storage and in addition to these, sensors have low

computational capability. Therefore, there is a need of adequate network design

for routing protocols that can overcome mentioned challenges.

2.2.9 Security Issues

As the routing protocols have limited capability, some of these protocols cannot

accommodate all the crucial information acquired by the sensor, challenging the

security of data. Data is sent to the end users by getting direct access to the

13

messages present in the sensors through internet services. Hence, there is a need

to prevent the data from unauthorized parties or from any malicious actions.

2.3 Applications of WSNs

WSNs have a capability to monitor wide range of applications including physi-

cal conditions [15] such as temperature, humidity, light, pressure, noise intensity

level, object movement and its characteristics etc. WSN node promises many new

applications by implementing concept of micro-sensing and wireless communica-

tion. There are many application related to WSNs and some of these are explored

below;

2.3.1 Military Applications

Wireless sensor network helps in surveillance and tracking of information in mil-

itary command control. The Ad-Hoc deployment of the sensor nodes, self orga-

nization and fault tolerance characteristics of WSNs, improves the firm sensing

capability of this application. Some of the other military applications are mon-

itoring the friendly forces, ammunition and equipment, attack detection, battle

surveillance and targeting etc. [16]

2.3.2 Environmental Monitoring

Applications like snow monitoring which is used to monitor the snow conditions

and avalanche forecasting; habitat monitoring which helps to deliver the informa-

tion about localized environmental conditions of each individual habitat, such as

issues affecting animals, plants and humans [17]; humidity and temperature mon-

itoring, wild life monitoring, traffic control, fire detection, flood detection etc also

utilizes WSNs. Also another important example that comes under environmental

monitoring is disaster management. Sensor networks help in detection of location

that could be useful for rescue operations, also used for prevention of potential

hazards. Figure 2.3 shows environmental applications of WSNs.

2.3.3 Medical Applications

Sensor networks have also focused its attention on medical application. These are

used to monitor the patient’s physiological condition, also used to administrate the

14

Figure 2.3: Environmental Applications of WSNs

drug section, monitor the patients and the doctors within the hospital [18]. These

are also used to detect the different types of viruses by monitoring the infected

area. Figure 2.4 shows medical application of WSNs. This is called Wireless Body

Area Networks (WBANs).

Figure 2.4: Medical Application of WSNs

2.3.4 Other Applications

For commercial purposes, sensors are widely used in home and industry automa-

tons. Also, the commercial buildings and offices are equipped with sensors and

15

actuators to monitor the room temperatures and air flow thereby improving the

living conditions. In home automation these applications are used for remote me-

tering and for smart intelligence purposes. Vehicle tracking and detecting is also

an application of WSNs that can help avoid car thefts.

2.4 Summary

In this chapter, a brief overview of WSNs is presented. Starting with introduc-

tion, then basic basic architecture of wireless sensor node, current challenges in

WSNs, like power consumption, network life time, scalability etc. and common

applications of WSNs, like military, environment and currently merging health-

ier applications. Next chapter gives overview of WSN network layer, concept of

clustering, and some well known clustering protocols.

16

Chapter 3

WSN Network Layer

17

Chapter 3

WSN Network Layer

3.1 Introduction

Data collected by sensor nodes in a WSNs is typically propagated toward a BS or

gateway that links the WSNs with other networks where the data can be visualized,

analyzed, and acted upon. In small sensor networks where sensor nodes and

a gateway are inclose proximity, direct (single-hop) communication between all

sensor nodes and the gateway may be feasible. However, most WSNs applications

require large numbers of sensor nodes that cover large areas, necessitating an

indirect (multi-hop) communication approach. That is, sensor nodes must not

only generate and disseminate their own information, but also serve as relays or

forwarding nodes for other sensor nodes. Difference between single-hop and multi-

hop is shown if fig. 3.1. The process of establishing paths from a source to a sink

(e.g., a gateway device) across one or more relays is called routing and is a key

responsibility of the network layer of the communication protocol stack [19].

Figure 3.1: Single-hop routing versus multi-hop routing model

18

The key responsibility of the network layer is to nd paths from data sources to

sink devices (e.g., gateways). In the single-hop routing model, all sensor nodes

are able to communicate directly with the sink device. This direct communication

model is the simplest approach, where all data travels a single hop to reach the

destination. However, in practical settings, this single-hop approach is unrealistic

and a multi-hop communication model must be used. In this case, the critical

task of the network layer of all sensor nodes is to identify a path from the sensor

to the sink across multiple other sensor nodes acting as relays. This design of a

routing protocol is challenging due to the unique characteristics o WSNs, includ-

ing resource scarcity or the unreliability of the wireless medium. For example,

the limited processing, storage, bandwidth, and energy capacities require routing

solutions that are lightweight, while the frequent dynamic changes in a WSN (e.g.,

topology changes due to node failures) require routing solutions that are adaptive

and exible. Further, unlike traditional routing protocols for wired networks, proto-

cols for sensor networks may not be able to rely on global addressing schemes(e.g.,

IP addresses on the Internet) [19].

ToI minimize energyI consumption, routingI techniques proposed in theI literature

for WSNs employI some well-known routingI tactics as well asI tactics special to

WSNs,I such as data aggregationI and in-networkI processing, clustering,I different

node roleI assignment, andI data-centric methods.

3.2 Routing Challenges and Design Issues inWSNs

The challenges and characteristics of WSNs are different than the conventional

Wireless Ad-hoc Networks (WANETs) due to their specific requirements. There-

fore, the designing task of a routing protocol in WSNs requires more careful con-

siderations than the other wireless ad-hoc networks (MANETs or WMNs). As

outlined below, the issues to consider for an efficient and reliable communication

in WSNs include network topology, data reporting methods, node and link het-

erogeneity, mobile adaptability, energy efficiency, coverage, data aggregation, and

quality of service [20]. These challanges are discussed below:

• Mobile adaptability: Most of the WSNs use the fixed nodes and base

stations. However, sensing the node or sink mobility can be a demand of

an application in different scenarios like vital sign monitoring of a mobile

patient in the hospital.

• Energy efficiency: Routing protocols need to maintain the connectivity

between the nodes and the base station with minimum energy consumption.

19

The periodicrouting updates help the nodes to refresh the status of neighbor

nodes. The flooding of these updates can shorten the nodes lifetime due to

the additional energy required.

• Coverage: Each sensor node can sense the environment within a certain

range of area coverage. The design of WSNs requires the deployment of the

nodes in a way that can get the maximum coverage. The routing protocol

needs to choose another node from the same sensor area where a node fails

to ensure the proper coverage of the whole sensor area.

• Data aggregation: Several methods, such as duplicate suppression, me-

dian, and minima-maxima, are used in routing protocols for data aggre-

gation to avoid the redundant transmissions and enhance the energy effi-

ciency. These techniques also help to reduce the traffic load and increase the

throughput.

• Quality of Service: Reliability-sensitive and delay-control algorithms are

used for routing protocols to fulfill the QoS demand of different WSN appli-

cations. These protocols help to monitor the sensor areas during a critical

situation.

3.3 RoutingIProtocols in WSNs

Several WSN routing protocols have been proposed by researchers in the last few

years. The WSN routing protocols can be classified in three ways: by its protocol

operations, by network structure, and by packet destinations [20]. Figure 3.2 shows

the classification of WSN routing protocols. The details of this classification are

given below.

3.3.1 Protocols based on Network Organization

The underlying network architecture plays an important role in the operations of

the routing protocol in WSNs. Routing Protocols based on network organization

can be divided into three categories. In this section the review of routing protocols

with respect to network structure is provided.

• Flat-Based:

The multi-hop routing approach is used by flat routing protocols. AllIthe nodes

inIthe network play the sameIrole. The BS generates the queries to the nodes and

20

Routing Protocol

Network Organization Protocol Operation Route Discovery

Flat- Based

Hierarchical-

Based

Location- Based

Negotiation-

Base

Multi-Path-

Based

Query-

Based

QoS-

Based

Coherent-

Based

Reactive

Proactive

Hybrid

Figure 3.2: ClassificationIof WSN RoutingIProtocols

in response nodes transmit data towards a base station. Scalability and simplicity

are the two major advantages of this kind of routingIprotocols. Because allIthe

nodes playIthe sameIrole, these protocols can easily accommodate a large number

of nodes or can add more nodes. Simplicity emerges from not choosing any cluster

head. All the nodes only send the data to the next hop or base station. The

complexity involved for electing the cluster head is not required. The disadvantage

of these routing protocols is the hot-spots. Every node is capable of sending its

own sensed data and of forwarding the other nodes dataIto the BS.IThe energy in

nodes around the sink drains quickly due to forwarding a lot of other nodes data

to the BS.

• Location-Based:

The location informationIof the sensor nodes in WSNs is used to calculateIthe

distanceIbetween the nodes, which helps to choose the next hop. The deploy-

ment of nodes in WSNs is spatial in nature. The sensor nodes are addressed by

using their location information since there is no addressing scheme for WSNs

such as IP-addressing. The nodes locationIinformation assists the location-based

routing protocols to send the data in a desired region instead of the whole net-

work. Some of the examples of location-based routing protocols are Minimum

21

Energy CommunicationINetwork (MECN), Trajectory-BasedIForwarding (TBF),

GeographicIAdaptive FidelityI(GAF), and GeographicIand EnergyIAware Rout-

ingI(GEAR).

• Hierarchal-Based:

InIhierarchical routing,Ithe networkI is dividedI intoI clusters to achieve efficiency.

The selection of cluster heads and formation of clusters are the two important

concerns of hierarchical routing protocols. The advantage of hierarchical routing

is the aggregation of data. The data from all member nodes are sent to the cluster

head and then the cluster head forwards these data towards the sink after applying

compression techniques. The aggregated data are easy to handle and simple to

process. One of the major drawbacks of hierarchical routing is the increase in

energy consumption of cluster heads due to their additional functions. The nodes

are selected as cluster heads in rotation manner, which overcomes this issue. The

selection of cluster heads and the formation of clusters in each round require more

computations, which also causes more energy consumption.

Table 3.1: Difference between flat and hierarchical routing

Flat Hierarchical

Contention-basedI scheduling Reservation-basedI schedulingCollisionI overhead present CollisionsI avoidedVariableI duty cycle byI controllingsleep timeI of nodes

ReducedI duty cycleI due to peri-odicI sleeping

NodeI on multihopI path aggregatesincomingI data fromI neighbors

DataI aggregationI by clusterI head

RoutingI can be madeI optimal butIwith an added complexity.I

SimpleI but non-optimalI routing

LinksI formed onI the flyI withoutsynchronizationI

Requires globalI and localI synchro-nization

RoutesI formed onlyI in regionsI thathaveI data forI transmission

OverheadI of clusterI formationthroughoutI the network

LatencyI in wakingI up intermediateInodes and settingI up the multi-path

Lower latencyI as multipleI hops net-work formedI by cluster heads al-waysI available

Energy dissipationI depends onI traf-fic patterns

EnergyI dissipation isI uniform

EnergyI dissipation adaptsI to trafficpattern

EnergyI dissipation cannotI be con-trolled

FairnessI not guaranteed FairI channel allocationI

22

3.3.2 ProtocolsIbased onIProtocol Operation

In this classification, different routing functionalities of the routing protocols are

considered. The protocol operation based routing protocols are divided into dif-

ferent types given below [20]:

• Negotiation-Based:

InI these protocols, a high level of descriptors is used for the negotiation between

the nodes to prevent redundant data and reduce duplicate information. Generally

this negotiation is done between the source and the next node or base station

before real data transmission. SPIN [21] is an exampleI of thisI type of routing.

• Multipath-Based:

The routing protocols in this category use the multiple pathsI between a sourceI

and destination for data transmission to enhance the network performance. The

directedI diffusionI protocol [22] is an example of multipath-based routing.

• Query-BasedI:

These protocols depend upon the queries from a destination. The source node

sends its sensed data in response to a query generated by the destination node.

A natural language or high level query language is used to generate these queries.

An example of these protocols is the rumor routing protocol [23]. The directed

diffusion protocol [22] is also counted in query-based routing protocols.

• QoS-Based:

The algorithm used by these protocols ensures the QoS requirements of the data.

Some of the QoS metrics are reliability, delay, and bandwidth. Balancing energy

consumption while satisfying QoS conditions is an important task for these routing

protocols. SPEED protocol is a good example of QoS-based routing.

• Coherent-Based:

Different data processing techniques are used to reduce the processing computa-

tions, which help to reduceI the energy consumptionI of the node. Coherent and

non-coherent are the two major data processing methods used for this purpose.

In the non-coherent data processing technique, the sensed raw data are processed

locally by the node and then the node transfers it to the aggregator. Aggregator

is a node which receives the data from many sensor nodes and sends these data

to the sink or base station after aggregation. In the coherent method, the mini-

mum processing is done locally by the sensor node. After receiving the data, the

aggregator is responsible for the major and complex part of processing. Exam-

23

ples of coherent and non-coherent data processing techniques are Multiple Winner

Algorithm (MWE) [24] and Single Winner Algorithm (SWE) [24], respectively.

3.3.3 Protocols based on Route Discovery

These routing protocols are responsible for identifying or discovering routes from a

source or sender to the intended receiver. This route discovery process can also be

used to distinguish between different types of routing protocols. There are three

protocols based on this types.

• Reactive:

Reactive protocols discover routes on-demand, that is, whenever a sourceI wants

toI send data to a receiver and does not already have a route established.

• Proactive:

While reactive route discovery incurs delays before actual data transmission can

occur, proactive routing protocols establish routes before they are actually needed.

This category of protocols is also often described as table-driven, because local

forwarding decisions are based on the contents of a routing table that contains

a list of destinations, combined with one or more next-hop neighbors that lead

toward these destinations and costs associated with each next hop option. While

table-driven protocols eliminate the route discovery delays, they may be overly

aggressive in that routes are established that may never be needed. Further, the

time interval between route discovery and actual use of the route can be very

large, potentially leading to out-dated routes (e.g., a link along the route may

have broken in the meantime).

• Hybrid:

The cost of establishing a routing table can be signicant, for example, in some

protocols it involves propagating a nodes local information (such as its list of

neighbors) to all other nodes in the network. Some protocols exhibit characteris-

tics of both reactive and proactive protocols and belong to the category of hybrid

routing protocols.

24

3.4 Clustering in WSNs

Clustering is a technique used in hierarchical based routing. It is a technique to

divide whole network intoIsmall blocks, calledIclusters. With each cluster hav-

ing a managing node, called cluster head (CH) and rest act as members. CH

is responsible to provide communication bridge between members and the base

station. The selectionIof cluster headsI and formation of clusters are the two im-

portant concerns of clustering protocols. The advantage of clustered routing is

the aggregation of data. The data from all member nodes are sent to the cluster

head and then the cluster head forwards these data towards the sink after apply-

ing compression techniques. The aggregated data are easy to handle and simple

to process. One of the major drawbacks of hierarchical routing is the increase in

energyIconsumption of cluster heads due to their additional functions. The nodesI

are selectedIas cluster heads in rotation manner, which overcomes this issue. The

selection of cluster heads and the formation of clusters in each round require more

computations, which also causes more energy consumption. There are many clus-

tering protocols developed by researchers in past few years. Some well known

clustering protocols are discussed in next section.

3.4.1 Energy Saving Schemes in WSN Clustering

As discussed above, CH selection and cluster formation are important constrains

in WSNs clustering. Hence a large amount of energy can be preserved by efficiently

electing cluster head and forming clusters. This is discussed in detail in following

sections.

3.4.1.1 Cluster Formation and Rotation

With the evolving trend in application and management of WSNs, clustering pro-

vides an efficient means of managing sensor nodes in order to prolong its lifetime.

Several clustering formation technique have been develop in the past such as ran-

dom competition based clustering (RCC)[25]. RCC algorithm uses random timer

and node identification for cluster formation is based on First Declaration Wins

Rule. This rule assigns governorship position to any node which declares itself

first as being a CH to other nodes in its radio network.

In direct broadcasting technique, cluster advertisement message is sent to all sen-

sors within a selected region. For instance, two clusters formation requires two

random nodes are selected for broadcasting. This randomly selected node is known

25

as an initiator. All initiators broadcast a cluster advertisement message to all sen-

sor nodes in the network. If any node in the network that is not an initiator

receives an advertisement message within the cluster, it sends a message to the

initiator from which the message was received. It will not only send a reply but

also refrain from accepting any other cluster advertisement message for that sim-

ulation round. Such a sensor node will however become a part of this initiators

cluster [26].

The technique of direct broadcasting is very simple when it comes to its implemen-

tation but it is not cost effective in termsIof energy consumption.I This is due to

the fact that all sensor nodes receive a broadcast from the CH. The sensor nodes

that are very far to the CH will still need to receive broadcast but it does not mean

that the sensor will respond to the message. But, in a situation where a sensor

node receives a broadcast from an initiator, the subsequent broadcast message will

be dropped, and energy which is used in transmission will be underutilized.

Multi-hop broadcasting on the other hand uses specific transmission range to

transmit a cluster advertisement message to the sensor nodes. It is the duty of

the receiving node to proceed in sending the cluster advertised message to all

the sensor nodes in its transmission range. This technique works very closely to

direct broadcasting technique for the fact that it also selects an initiator node

that sends cluster advertisement messages at the start of cluster formation. These

techniques use a concept which is known as minimum communication energy which

means that the sensor node that is easiest to reach will form part of the initiator

cluster. Also, when cluster are formed dynamically, the reorganization is done on

a periodic basis. The initiator is selected at the beginning of every period and

broadcasted messages are sent out using one of the above-mentioned methods for

cluster organization.

The multi-hop broadcasting minimizes the problem of energy usage. This is due

to the fact that there is a limit for transmission because the highest amount of

energy that can be wasted is the minimum transmission energy of neighboring

sensor nodes. This will create no need for the sensor nodes which are far away

from each other to transmit directly. It has a disadvantage in the sense that it

has more delay when compared to the former technique of broadcasting. This

is because, in multi-hop broadcasting, the data are required to be processed by

each sensor node along the multi-hop path, which creates delay in the formation

of cluster. However, the multi-hop is much better than the direct broadcast if the

problem of delay is taken care of.

26

3.4.1.2 Cluster Head Election and Rotation

After cluster formation, CHs are designated which act as a leader in each clus-

ters. ClusterIheads areI provided with the responsibility for data aggregation and

performing routing for its cluster members information to the base station. Also,

the clusters that consist of many nodes have a higher burden than clusters with

fewer nodes as the CHs for those large-sized clusters have to receive, aggregate

and transmit more data.

A CH can be elected randomly or pre-assigned by the designer of the network. A

CH can also be elected byI taking into considerationIthe residual energy of nodes

in the cluster. The CHs are known to have higherIburdens than member nodes;

therefore, the role of CH is rotated to share the burden and thus improving the

useful lifetime of those clusters.

In random selection, a node is selected randomly as CH, based on the probability

that it has never being selected during the entire lifetime of the network. This

reduces the traffic burden on a CH since the role of CH is spread throughout the

sensor nodes. The rotation is done at a periodic interval.

Whereas, in the residual energy selection, the sensor node that hasI the highest

amountI of energy in the cluster is selected as the cluster head. It will continue

to remain the CH until the energy drops below the average energy of the entire

cluster. So, rotation of CH is done at every instance when its energy level drops

below the average cluster energy. This rotation of CHs will lead to the overall

energy of the sensor network being evenly distributed. This technique eventually

improves the lifetime of the network.

Another approach to cluster headIselection isI based on minimizing the distance

to cluster nodes as this offers reduction in energy usage during data transmission

to the BS. With this method of minimizing sum of distances to CH, the cluster

formation is better enhance to reduce energy usage as transmission takes place. It

helps in reducing the unnecessary energy which the sensor node uses in commu-

nicating with the CH by minimizing the transmission distances from sensor node

to any CH [27].

Since communication energy is an important concept to consider in wireless trans-

mission, it is known that energy greatly depends on distance [7]. Therefore, it is

very good idea to minimize the distance in transmitting data from sensor nodes

to base station via the CH, as this helps to reduce the communication energy in

wireless sensor network.

27

3.4.1.3 Efficient Intra-cluster Communication

When addressing the problem of energyIconsumption inIwireless sensorInetwork,

the size of a cluster is an important factor subject to analysis in hierarchical

network. Clusters of small size save power in intra-cluster communication but it

will also increase the complexity of the backbone network. Also, smaller cluster

size means less load in the backbone and thus a less complicated communication,

but the intra-cluster communication consumes more power and reduces the lifetime

of the sensor network. These necessitate a tradeoff in clusters formation [27].

One of the trade-off is mentioned in where a method of limiting the number of

hops which a sensor node takes in communicating with its CH. Bandyopadhyays

and Coyles approach makes a use of the K-tree cluster framework and optimize

the framework and optimize the value K to minimize power consumption within

a cluster. The K-tree clustering algorithm can be described as a technique that

ensures that every sensors node within a cluster can carry out communication

with its corresponding cluster head by using a maximum of K- hops. Therefore, a

sensor node needs to use other sensor nodes to relay its transmission to the base

station via its routing table. And, the data of any sensor node will not be relayed

within a cluster for more than K times.

Moreover, in, Bandyopadhyay and Coyle utilize a method of selecting cluster head

with the probability P. The message will then be forwarded to all nodes which are

K-hops away. The optimal K value is a predetermined number that is set according

to the size of the network. Any sensors that receive the advertisement message

from the elected CH are considered a cluster member of the cluster from which it

received this message. The message sent by the sensor node would be ignored if

it is received by another volunteer cluster head. Also, a node can be mandated

to become a cluster head if it does not receive a CHs advertisement in a specific

time t which is defined as the time required to send K hops data away [27].

With the K-hops approach, communication in wireless sensor network can be

either of the single-hop or multi-hop. Single-hop simply refers to direct commu-

nication from sensor node to cluster head while multi-hop does not require direct

communication from all sensors to the base station. But, it can send data to the

next cluster head which is closer to the base station. Therefore, multi-hop com-

munication has higher energy efficiency than the single hop within the clusters.

When the sensor node is at a far distance from the CH, much energy is expended

thereby reducing the lifetime of sensor network.

For instance, when two sensor nodes are placed at a far distance away from each

28

other in the same cluster but one of them is closer to the base station, it is observed

that the energy consumed by the node closer to the base station is lower compared

to that which is far away. And with the help of multi-hop communication, an

intermediary node is used between a source environment and BS to relay the

data, a great deal of energy is conserved in the network.

Furthermore, taking computational complexity into consideration when designing

cluster with uneven data traffic in each clusters when data are being transmitted,

the size of clusters seem to be random at the time of formation. Although, in some

circumstances, the cluster sizes are equal which denotes equal number of nodes,

and in other scenario, the size is randomly sized.

3.5 Clustering protocols in WSNs

Proper clustering and CH selection largely affects network lifetime and stability

in WSNs. Lots of research have been devoted in this regard and many clustering

protocols have been purposed. This section reviews few of the previously proposed

clustering schemes.

3.5.1 Low-EnergyIAdaptiveIClusteringIHierarchy (LEACH)

The LEACH [28] protocol is the basic clustering-basedIenergy-efficient routing

protocol. The clustering techniques proved to be very useful to reduceItheIenergy

consumptionIand increase the networkIlifetime. The entire network is divided into

clusters in the LEACH routing protocol. One sensor node in eachIcluster must act

as a cluster head and all remaining sensor nodes are member nodes of that cluster.

Communication between the member nodes and sink is only possible via the cluster

head. From each cluster, only the cluster headIcan directlyIcommunicateIwith the

sink. The clusterIheads collect, aggregate,IandIforward theIdata from member

nodes to the sink. The cluster head consumes more energy due to the additional

functions and this node can die quickly if it continuously plays the role of a cluster

head. LEACH resolved this problem by changing dynamically the role of nodes

as cluster heads.

LEACH works in rounds. The operations that are carried out inIeach round

consist of two phases known as setup and steady state phases. The organization

ofIclusters andIselectionIof clusterIheadsI(CHs) are done in the setup phase of the

LEACH. The data are sent to the sink during the second or steady state phase.

29

InItheIsetupIphase, the formation of clustersIand the election process of cluster

heads are performed. First of all, the whole network is divided into clusters. Now

the cluster head election process starts in each cluster. There are many ways to

elect the CH. Some of the wireless nodes in the network ignore the negotiation

process with other nodes and elect themselves autonomously as CHs. The CH

selection criteria of a member node are the recommended percentageIP and the

earlier recordIas a CH. If a node is not a CH in preceding 1/P rounds, it produces

a numberIbetween zero and one (0-1). Only nodes with a generated number less

than threshold T(n) are eligible to become CHs. The formula used to calculate

the value of threshold is given in eq. (3.1).

T (n) =

p1−p(r∗mod(1/p))

if n ∈ G

0 otherwise(3.1)

where

G = Group ofInodesInot selectedIas CHs in preceding 1/pIrounds;

P = Recommended percentageIofICH;

r = Current round.

Figure 3.3: Architecture of LEACH

The basic architecture of LEACH isIshown in fig. 3.3.IA node cannot be selected as

a CH if it has already performed a CH role in the lastI1/p rounds but all nodes that

were CHs before 1/p rounds will again be candidates for the selection of CHs [28].

30

The uniform service of each node as a CH prevents the uneven energy consumption

of the member nodes. The CSMA/CA protocol is used by the CH to broadcast

its status. After receiving the broadcast messages from CHs, the non-cluster head

nodes useIthe ReceivedISignal Strength Indication (RSSI) as a parameter to select

their CH. Each CH creates a TimeIDivisionIMultiple AccessI(TDMA) schedule for

their cluster members. The CH and member nodes communicate with each other

during their assigned time slots in the steady state phase. The member node is

only in active mode during its communication with a CH. Otherwise the mem-

ber node goes to sleep mode during an unallocated time slot. The management

of the member node in this way reduces theIenergy consumptionIandIincreases

theIbattery life of the node. The CH collects the data from all the cluster member

nodes. The CH transmits that dataItoIthe baseIstation after compression. The

time duration of setup phase is lower than the steady state phase.

3.5.2 Centralized LEACH (C-LEACH)

C-LEACH [29] is proposed by Heinzelman et al. The conventional LEACH proto-

col does not guarantee the best possible number of CHs and their effectual loca-

tions. The problem is due to the clusters formation method used by the LEACH

algorithm. LEACH-C is therefore proposed to enhance the cluster creation part

of the LEACH protocol.

All nodes are required to send their ID, location, and energy information to the

base station during the setup phase of C-LEACH [29]. The base station is re-

sponsible for assigning the role of CH to any member node by using its central

control algorithm. The central control algorithm first specifies the average energy

level and then compares that energy level to the energy level of the received signal

energy. The base station picks the optimal number of CHs from the nodes with an

energy level greater than the average energy level. A list of IDs of these selected

nodes is transmitted by the base station to all nodes. From this list, a node having

minimum distance from its member nodes is elected as CH of that cluster. The

approach used in C-LEACH reduces the energy consumption of CH and member

nodes. The following assumptions are taken in this protocol:

• Every node can calculate its energy.

• Every node knows its location.

• Every node can communicate to the base station.

The successful data transmission during the steady state phase of LEACH-C in-

31

creases. Some of the disadvantages of using the LEACH-C protocols are given

below.

• During the setup phase, all the nodes in the network are required to send

their information to the base station. This process causes additional energy

consumption from each node.

• The central control algorithm runs on the base station to select the CHs. The

IDs of selected CHs are passed to all nodes. Every node needs to compare

its ID with the IDs of CHs to determine its role as a CH. These additional

computations consume more energy from the nodes.

At the end of this chapter we present the detailed comparison of some well known

routing protocols in table 3.2 on next page.

32

Table 3.2: Detailed Comparison of WSN Clustering Protocols [30]

Protocol EnergyHomo-geneity

Scalability EnergyEffi-ciency

NetworkLifetime

DataDeliveryModel

CH Selection based on

InitialEnergy

ResidualEnergy

AverageEnergy ofNetwork

LEACH Yes Verylimitedscalability

Lower Poor Clusterhead

YesI NoI NoI

TEEN Yes Verylimitedscalability

Very high Best ActiveThresh-old

YesI NoI NoI

SEP No (Twolevelsheteroge-neous)

MoreIscalableLowI Good ClusterIhead

No Yes No

DEEC No(Multilevelsheteroge-neous)

Most Scal-able

High Better Clusterhead

No Yes Yes

33

Chapter 4

Proposed Clustering Scheme

34

Chapter 4

Proposed Clustering Scheme

Various techniques can be found in clustering protocols. The basic idea of these

techniquesIisIto efficiently utilize the energy in orderIto prolongInetwork lifetime

and stability. In this chapter we discuss the details of my proposed clustering

scheme, which is based on MultipleICriteria DecisionIMaking (MCDM).

4.1 Energy Model for WirelessISensor Node

In WSNs most of the energy is consumed in communication process. Therefore

for estimation of WSNs energy utilization, it is necessary to calculate energy cost

for transmission and reception. This cost can be evaluated using simple model for

radio hardware consumption. Figure 4.1 shows energy model for wireless sensor

node.

Transmitter Electronics Transmitter Amplifier

Receiver Electronics

K-bit packet

K-bit packet

Eelect *k Eamp*d2*k

Eelect *k

Tx Antenna

Rx Antenna

d

Figure 4.1: Wireless Sensor Node Energy Model

35

DependingIonIthe distance d betweenItransmitterIand receiver, the required trans-

mitting and receiving energy for a k-bitIpacket can beIexpressed as following equa-

tions. Eq. (4.1) for transmitting energy, where as eq. (4.2) for receiving. Both

free space and multi-path fading channel models are used in this energy model.

ETx(k, d) = kEelect + Eamp(k, d)

=

kEelect +Kefsd2, d ≤ d0

kEelect + kEmpd4, d > d0

(4.1)

ERx = kEelect (4.2)

where:

ETx ⇒ energyIdissipated per bitIatItransmitter

ERx ⇒ energyIdissipatedIper bit atIreceiver

Eamp ⇒ amplification factor

Eelect ⇒ costIofIcircuitenergyIwhen transmittingI or receivingIone bit of data

efs ⇒ free space coefficient

emp ⇒ multi path coefficient

k ⇒ number ofItransmittedIdata bits

d ⇒ distanceIbetween a sensor node and its respective cluster head or between a

CH to another CH nearer to the BS or between CH and BS

d0 ⇒ distance threshold value obtained by d0 =√

efsemp

For scalability purpose, we assume that the intra-cluster transmission range must

satisfy d > d0 and inter-cluster transmission range must satisfy the bound d ≤ d0.

An error free communication and an ideal MAC layer are also assumed so that

transmission is perfect and there is no collision and retransmission.

4.2 Proposed Scheme

We propose a multi-criteria based distributed CH selection technique based on

fuzzy-TOPSIS method. We improve deficiencies in previous fuzzy based CH se-

lection technique. Due to using distributed algorithm, nodes themselves take

decision to be selected as CH, hence nodes join CH with maximum resources be-

cause all nodes have index value of their neighbouring nodes (which is a rank value

obtained using multi-criteria, final CH selection is based on this value).

36

We define a threshold value for change of CH, so in our proposed scheme CHs

are not changing in every round, due to this, control overhead is much reduce as

compared to previous scheme. We consider four criteria includingIresidual energy,

numberIof neighbor nodes, distance fromIBS and average distance form neighbor

nodes. Our proposed scheme consists of four phases, i.e. network deployment,

neighbor discovery, CH selection and cluster formation and last one is communi-

cation. These are three phases are describedIin detailIin this section.

4.2.1 Network Deployment Phase

The basic architecture of Wireless Sensor Networks used in our protocol is shown in

Fig. 4.2. We assume that the sensor nodes are deployed randomly and uniformly,

are not movable, and are homogenous initially with respect to their antenna gain.

It is also assumed that the dimensions of the sensor field are given and the coor-

dinates of the base station are known. The base station is capable of receiving,

aggregating, and then forwarding the data from the cluster heads to the desired

destinations.

Figure 4.2: Sensor Nodes Deployed in Field

37

4.2.2 Neighbor Discovery Phase

The initial step of our proposed distributed clustering scheme is to perform neigh-

bor discovery. Initially, all nodes broadcast a Hello packet, which contains node’s

ID, residual energy, C1, node density C2, distance to the BS, C3, average distance

between this node and its neighbors, C4 and location information. Initially, C2 and

C4 fields in the Hello packet will be empty because each node has no information

about its neighbors. However, after sharing Node ID and location information

with its neighbors, each node can easily compute C2 and C4 and exchange it in

the next Hello packet. All the other nodes in the transmission range Tr of that

node, receive Hello packet. After receiving hello packet from all neighbors, a node

updates its neighborhood table (T ) with neighboring node’s ID, C1, C2, C3, C4

as well as its own information. Suppose, there are n neighbors of node k, then Tk

will be an (n+ 1)× 4 matrix, as shown below:

Tk =

C1 C2 C3 C4

a1 v1,1 v1,2 v1,3 v1,4

a2 v2,1 v2,2 v2,3 v2,4

a3 v3,1 v3,2 v3,3 v3,4

: : : : :

an+1 vn+1,1 vn+1,2 vn+1,3 vn+1,4

(4.3)

After updating packet form all neighbors, the nodes perform multi-criteria tech-

nique to calculate their respective rank index, and share it with all neighbor nodes

through hello packet. Following are steps to calculate rank index value based on

fuzzy-TOPSIS.

4.2.3 CH selection and Cluster Formation Phase

Comprehensive explanation of CH selection process of our proposed scheme is

given in this section. Following are steps to calculate rank index value based on

fuzzy-TOPSIS:

Step 1: It is evident that the values of all criteria, Ci, do not lie in the same

range, e.g. range of C1 is not similar to the C2. Therefore, these criteria must be

normalized to the similar range [0 − 1] to fairly select a CH. Note that there are

some criteria whose larger value is suitable for a node to be selected as a CH e.g.

C1 and C2. These criteria are called Positive criteria, Benefit criteria or Positive

Ideal Solution (PIS) and are normalized as in eq. (4.4). On the other hand, the

38

criteria with smaller values are appropriate for a node to be selected as a CH,

e.g. C3 and C4. This type of criteria are called Negative criteria, Cost criteria or

Negative Ideal Solution (NIS) and are normalized as in eq. (4.5).

Ni,j =vi,j −min∀i(vi,j)

[max∀i(vi,j)−min∀i(vi,j)](4.4)

Ni,j =max∀i(vj)− vi,j

[max∀i(vi,j)−min∀i(vi,j)](4.5)

Each element of the Tk, is normalized using eq.(4.4) and (4.5) and the normalized

matrix at node k, Nk, will be:

Nk =

N1,1 N1,2 N1,3 N1,4

N2,1 N2,2 N2,3 N2,4

N3,1 N3,2 N3,3 N3,4

: : : :

Nn+1,1 Nn+1,2 Nn+1,3 Nn+1,4

(4.6)

The preference or weight ’wi’ are assigned to each criterion. These, weights are

application specific, however, for our proposed scheme, the weights assigned to the

selected criteria are shown in Table 4.1.

Table 4.1: Criteria Weights

Criteria Weight

Residual Energy (w1) 0.4Node density (w2) 0.2Distance form BS (w3) 0.2Avg. Distance between Neighbors (w4) 0.2

After normalization, Fuzzy membership function is used to categorize these nor-

malized value of each criteria and their respective weights for every node. For our

proposed scheme Fuzzy member function is given in Table 4.2.

Table 4.2: Fuzzy Membership Functions

Very LowI(VL) (0.00, 0.08, 0.15, 0.25)LowI(L) (0.15, 0.28, 0.35, 0.45)MediumI(M) (0.35, 0.48, 0.55, 0.65)HighI(H) (0.55, 0.68, 0.75, 0.85)Very HighI(VH) (0.75, 0.88, 0.95, 1.00)

39

Step 2 : On the basis of these Fuzzy membership functions, weighted decision ma-

trix is determined for each criteria and their respective weight. Weighted decision

matric Vk is given as:

Vk =

V1,1 V,12 V1,3 V1,4

V2,1 V2,2 V2,3 V2,4

V3,1 V3,2 V3,3 V3,4

: : ... :

Vn+1,1 Vn+1,2 Vn+1,3 Vn+1,4

(4.7)

Step 3 : After that, PIS and NIS are determined from Vk matrix, using following

equations:

PIS = (V +1 , ..., V +

n ) = [(maxiVij |i = 1, ..m), j = 1, ...., n] (4.8)

NIS = (V −

1 , ..., V −

n ) = [(miniVij|i = 1, ..m), j = 1, ...., n] (4.9)

Step 4 : Separation measure is also determined form Vk matrix, using the n-

dimensional Euclidean distance. This distance can be calculated as:

D+j =

√

√

√

√

n∑

i=1

(Vij − V +i )2, j = 1, 2, ...m (4.10)

D−

j =

√

√

√

√

n∑

i=1

(Vij − V −

i )2, j = 1, 2, ...m (4.11)

Step 5 : Finally Rank Index (R.I) is determined according to following formula:

R.I =D−

j

D+j +D−

j

(4.12)

The node with highest value in this rank index announces itself as CH in that

region. Other nodes in that region, send join request to associate with the CH

and act as member nodes. After successful reception of the join request message,

the CH a acknowledges to all of its members. In this manner, wholeInetwork is

dividedIinto clustersIand inside each cluster, a potential node is selected as a CH.

40

R = 0

Diff. b/w CH and any

other node index value

> 0.1

Send data to BS

End

Yes

No

Start

R= R + 1

Re-election

All nodes die ?

Yes

No

R= Round

Figure 4.3: Procedure for CH Change

After successful clustering round, all member nodes in clusters, start normal com-

munication through their respective CHs. Along with the normal communication,

they will also compare their index value in their neighborhood table. If the index

value of any node is greater than index value of CH plus specific threshold (in our

proposed scheme it is 0.1), then the CH will no more be eligible to act as CH,

and nodes will perform re-election process within the cluster only by following

the steps discussed above. The significance of using threshold value is to avoid

CH change in every round. This process will continue till last node dies in the

network. The flow diagram of CH change is shown in fig. 4.3

41

4.2.4 Communication Phase

After CH selection and cluster formation, communication phase starts. The multi-

hoping communication model is considered by our proposed scheme because it is

the more realistic and practical one. The nodes within five meters range of CH,

send their data directly to CH, however, the nodes that are at a distance of

greater than five meter from CH, perform multi-hoping with other nodes coming

in their way to communicate with the CH. Same condition is also applied on CHs

when they communicate with the BS. The CHs within twenty meters range of BS,

communicates directly to BS, where as the remaining CHs perform multi-hoping

via other CHs. The purpose of using multi-hoping is to increase network stability

and life time. Figure 4.4 shows intra and inter-cluster communication.

Figure 4.4: Inter and Intra-cluster Communication

Complete flow diagram of our proposed scheme, including neighbor discovery, CH

selection and both inter and intra-cluster communication, is shown in fig. 4.5 on

next page.

42

Node Deployment

Neighbor Discovery

Sharing of Information

(four criteria values

among neighbors)

CH

Send Join Request

Cluster Formation

Node within

5m of CH

Broadcast CH

advertisement

Receive CH

advertisement

Receive Join

Request

Send Data to CH

End

CH selection based on

MCDM

Yes No

No

Perform Multi-hoping with

Neighbor nodes

Start

Yes

CH within

20m of BS

Send aggregated

data to BS

Perform Multi-hoping with

Neighbor CHs

No

Yes

(Normal Node)

Intra-cluster

communication

Inter-cluster

communication

Aggregate & compress

data

Figure 4.5: Flow Diagram of Proposed Scheme

43

Chapter 5

Simulation and Results

44

Chapter 5

Simulation and Results

In this chapter, we compare the performance of our proposed scheme with LEACH

[28] and previous fuzzy based CH selection scheme [31], through simulation using

MATLAB.

5.1 Simulation Environment

In our simulation n number of sensor nodes is randomly dispersed in a field of

100m × 100m. The BS is located at corner of the field. In our simulation we

made some assumptions. First is that sensor nodes are continuously monitoring

the environment and always have data to be sent to the BS. Second is that wireless

channel is free of signal collision and interference. Simulation parameters are given

in Table 5.1.

Table 5.1: Simulation Parameters

Parameter Value

Network Area 100m x 100mNumber of Nodes (n) 100BS Position (50,100)Initial Energy 0.5 JData Aggregation Energy 50pj/bit/reportData Packet Size 4000 bitsHello Packet Size 200 bitsTransmitter Electronics (EelectTx) 50 nJ/bitReceiver Electronics (EelecRx) 50 nJ/bitTransmit Amplifier (Eamp) 100 pJ/bit/m2Transmission Frequency Band 2.4 GHzMAC Protocol CSMA/CA

45

5.2 Simulation Results

We evaluate the stability of the network by examining the numbers of rounds until

first node dies. Following graphs show simulation results of our purposed scheme

compared with previous schemes.

5.2.1 Number of Dead Nodes

Number of dead nodes per round in the network are shown in fig.5.1 .It is clear

from graph that in LEACH, first node dies around 170 rounds and previous fuzzy

model first node dies around 530 rounds where as our proposed scheme first node

dies 1600 rounds It shows that network lifetime and stability in our purposed

scheme is much better than previous clustering techniques. Reason for is that

LEACH is a single criteria based technique, where as previous fuzzy-TOPSIS

method , BS is performing CH selection process, which does not depends on

geographical conditions of nodes, where as in our purposed scheme every node

itself take decision for CH, considering the knowledge of neighbor nodes.

0 500 1000 1500 2000 25000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Nu

mb

er

of

de

ad

no

de

s

Network Stability

Proposedprevious FuzzyLEACH

Figure 5.1: Number of Dead Nodes

46

5.2.2 Number of Alive Nodes

Fig. 5.2 shows network lifetime. from graph, in LEACH last nodes dies around

1000 rounds, in previous fuzzy model it dies 1100 rounds, where is in our proposed

scheme network dies after around 2400 rounds. Network lifetime in our proposed

scheme is much better than previous schemes, due to proper CH selection and

using multi-hop communication.

0 500 1000 1500 2000 25000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Nm

be

r o

f a

live

no

de

s

Network Lifetime

LEACHPrevious FuzzyProposed

Figure 5.2: Number of Alive Nodes

5.2.3 Energy Consumption

Energy consumption of network per round is shown in Fig. 5.3. It is observed

that our proposed scheme consumes less energy than previous schemes. A major

constituent of energy consumption is communication process. Almost 70 percent

of whole network’s energy is consumed in communication. So the proper commu-

nication model is very much necessary for any energy efficient clustering protocol.

In our proposed scheme we use multi-hop communication in both inter and intra-

cluster communication, this is the main reason of lower energy consumption in

our proposed scheme is much less than LEACH and previous fuzzy based scheme.

In previous schemes, energy variation at different points is observed and it due to

re-selection of CHs in each round. This CH re-selection is avoided in our proposed

47

scheme by introducing the CH changing threshold and that is also the main reason

of less energy consumption in our proposed scheme.

0 500 1000 1500 2000 25000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Number of rounds

En

erg

y c

on

su

me

d p

er

rou

nd

Network Energy Consumption

LEACHPrevious FuzzyProposed

Figure 5.3: Energy Consumption of Network

5.2.4 Change of Cluster Head

One major drawback of LEACH is that it performs CH election process in every

round or communication cycle. Hence in every round, all nodes send their complete

information to BS, if it is distributed. In centralized algorithm, like C-LEACH,

nodes share their information with all neighbor nodes in every communication

cycle. This shearing of information or sending information to BS in every round

require control overhead packets. Hence protocols based on re-election of CHs in

every round have large number of control overhead packets. Resulting in more

energy consumption.

In our proposed scheme we define a threshold, CH will only change if difference

between index value of CH and any other node in that cluster exceed that thresh-

old. Due to small number of variations in CH, there are very small number of

control overhead packets. Figure 5.4 shows the number of CH change during each

round.

48

0 500 1000 1500 2000 25000

2

4

6

8

10

12

14

16

18

20

Number of rounds

Ch

an

ge

of

CH

s p

er

rou

nd

Cluster Head Stability Ratio

LEACH Previous FuzzyProposed

Figure 5.4: Change of CHs

5.2.5 Control Overhead (Hello) Packets

The control overhead or Hello packets are the control signals required for any type

of data processing in WSNs. Lager the control overhead packets, greater will be

the energy consumption, Hence for any energy efficient clustering protocol it is

necessary that these packets should be minimized. In our proposed scheme CH

change is very rare and we use distributed algorithm for selection of CH. These

are main reasons for minimization of control overhead packets. Figure 5.5 shows

comparison of our proposed scheme with LEACH and previous fuzzy model. It

can clearly observed that control overhead packets in our proposed scheme are

very small as compared to other two schemes.

5.2.6 Packets Sent to Base Station

Number of packets to BS is are shown in fig. 5.6. Due to proper CH selection

and communication model, network throughput of our proposed scheme is much

greater than previous schemes.

49

0 500 1000 1500 2000 25000

1

2

3

4

5

6

7

8

9x 10

4

Number of rounds

Nu

mb

er

of

He

llo

pa

cke

ts

Network Control Overheads

LEACHPrevious FuzzyProposed

Figure 5.5: Control Overhead Packets

0 500 1000 1500 2000 25000

0.5

1

1.5

2

2.5

3

3.5

4x 10

4

Number of rounds

Nu

mb

er

of

pa

cke

ts t

o B

S

Network Throughput

LEACHPrevious FuzzyProposed

Figure 5.6: Packets sent to BS

50

Chapter 6

Conclusion

51

Chapter 6

Conclusion

In this thesis, we present a new clustering technique based on multiple criteria.

We consider four criteria, including residual energy, number of neighbor nodes,

distance form BS and average distance between a node and its neighbors. We use

distributed algorithm for selection of CHs, means nodes themselves take decision

to become CHs or not. We control frequent change of CH in our proposed scheme.

If the index value of any node is greater than index value of CH plus specific

threshold (in our proposed scheme it is 0.1), then the CH will no more be eligible

to act as CH, and nodes will perform re-election process within the cluster. The

significance of using threshold value is to avoid CH change in every round. This

process will continue till last node dies in the network. We also improve both

intra and inter-cluster communication by using multi-hop communication. The

nodes within five meters range of CH, send their data directly to CH, however,

the nodes that are at a distance of greater than five meter from CH, perform

multi-hoping with other nodes coming in their way to communicate with the CH.

Same condition is also applied on CHs when they communicate with the BS. The

CHs within twenty meters range of BS, communicates directly to BS, where as

the remaining CHs perform multi-hoping via other CHs. The purpose of using

multi-hoping is to increase network stability and life time.

We perform MATLAB simulation to compare the results of our proposed scheme

with LEACH and previously proposed fuzzy based centralized clustering model.

All results show that the network performance of our proposed scheme is much bet-

ter than perviously proposed clustering techniques. This improvement is achieved

using distributed algorithm, using multi criteria for CH selection and using multi-

hop communication model in both intra and inter-cluster communication.

52

References

[1] Chaurasiya, S.K.; Pal, T.; Bit, S.D.; ”An Enhanced Energy-Efficient Proto-

col with Static Clustering for WS”. International Conference on Information

Networking (ICOIN), 2011 Kuala Lumpur, Malaysia, on page(s): 58 63.

[2] Ismail Olalekan Eleburuike; Soetan Simeon Adekunle, ”EnergyEfficient Wire-

less Sensor Network Using Hierarchical Routing Technique”. Blekinge Institute

of Technology, Karlskrona, Blekinge, Sweden, 2010.

[3] Opricovic, Seram, and Gwo-Hshiung Tzeng. ”Compromise solution by MCDM

methods: A comparative analysis of VIKOR and TOPSIS.” European Journal

of Operational Research 156.2 (2004): 445-455.

[4] Shah, T., N. Javaid, and T. N. Qureshi. ”Energy Efcient Sleep Awake

Aware (EESAA) Intelligent Sensor Network Routing Protocol.” arXiv preprint

arXiv:1212.4106 (2012).

[5] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, ”Energy-efcient rout-

ing protocols for wireless microsensor networks,” in Proc. 33rdHawaii Int. Conf.

SystemSciences(HICSS), Maui, HI,Jan. 2000.

[6] Ding Yong and Qin Xiao-Ming, ”EERM: Energy Efficient Routing Metric for

Wireless Sensor Networks,” in First International Conference on Pervasive

Computing Signal Processing and Applications (PCSPA), 2010, pp. 171174.

[7] K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie, ”Protocols for self-

organization of a wireless sensor network,” in IEEE Personal Communications,

vol. 7, no. 5, pp. 1627, Oct. 2000.

[8] M. N. Elshakankiri, M. N. Moustafa, and Y. H. Dakroury, ”Energy Efficient

Routing protocol For Wireless Sensor Networks,” in International Conference

on Intelligent Sensors, Sensor Networks and Information Processing, 2008,

pp.393 398.

[9] J. Feng, F. Koushanfar, and M. Potkonjak, ”Sensor Network Architecture,”

Handbook of Sensor Networks, 2004.

53

[10] ”Evaluation of Routing Protocols in Wireless Sensor Networks” - Arkiv EX

- Blekinge Tekniska Hgskola.

[11] L. B. Ruiz, J. M. S. Nogueira, and A. A. F. Loureiro, ”Sensor Network Man-

agement,” Handbook of Sensor Networks: Compact Wireless and Wired Sens-

ing Systems, M. Ilyas and I. Mahgoub, Eds., Ch. 3. CRC Press, 2005.

[12] H. K. Kalita and K. Avijit, ”A New Algorithm of Self Organization in Wireless

Sensor Network,” algorithms, vol. 3, p. 4, 2010.

[13] Martin Haenggi, ”Sensor Network Management,” Handbook of Sensor Net-

works: Compact Wireless and Wired Sensing Systems, M. Ilyas and I. Mah-

goub, Eds., Ch.1.CRC Press, 2005.

[14] K. Akkaya and M. Younis, ”A survey on routing protocols for wireless sensor

networks,” Ad Hoc Networks., vol. 3, no. 3, pp. 325349, May 2005.

[15] H. Hassanein, K.Xu and Q.Wang, ”Sensor Network Management,” Handbook

of Sensor Networks: Compact Wireless and Wired Sensing Systems, M. Ilyas

and I. Mahgoub, Eds., Ch. 9. CRC Press, 2005

[16] C. S. Raghavendra, K. M. Sivalingam, and T. F. Znati, ”Wireless sensor

networks” Boston: Kluwer Academic Publishers, 2004.

[17] R. Szewczyk, E. Osterweil, J. Polastre, M. Hamilton, A. Mainwaring, and D.

Estrin, ”Habitat monitoring with sensor networks,” Commun. ACM, vol. 47,

no. 6, pp. 3440, Jun. 2004.

[18] Y. Miao, ”Applications of sensor networks,” in Seminar Wireless Self-

Organization Networks Published, 2005, pp. 0502.

[19] W. Dargie, C. Poellabauer, ”Fundamentals of Wireless Sensor Networks”, A

John Wiley and Sons, Ltd., Publication, 2010.

[20] J. N. Al-Karaki and A. E. Kamal, ”Routing Techniques in Wireless Sensor

Networks: A Survey,” IEEE Wireless Communications, vol. 11, no. 6, pp. 6-28,

2004.

[21] R. C. Shah and J. Rabaey, ”Energy Aware Routing for Low Energy Ad Hoc

Sensor Networks,” in Proceedings of the IEEE Wireless Communications and

Networking Conference, Orlando, USA, 2002.

[22] C. Intanagonwiwat, R. Govindan and D. Estrin, ”Directed diffusion: A scal-

able and robust communication paradigm for sensor networks,” in Proceedings

of the 6th International Conference on Mobile Computing and Networking

(Mobicom), Boston, USA, 2000.

54

[23] D. Braginsky and D. Estrin, ”Rumor Routing Algorithm for Sensor Net-

works,” in Proceedings of the first Workshop on Sensor Networks and Appli-

cations, Atlanta, USA, 2002

[24] K. Sohrabi and J. Pottie, ”Protocols for Self-Organization of aWireless Sensor

Network,” IEEE Pers. Commun., vol. 7, no. 5, pp. 16-27, 2000.

[25] A. A. Abbasi and M.Younis. (2007,Oct.), ”A survey on clustering algorithms

for wireless sensor networks”, in Computer Communications[online]. Vol.30(14-

15), pp. 2861- 2841

[26] D. Wei and A. Chan, ”Clustering A Hoc Networks: Schemes and Classifica-

tions” Proc. of IEEE International Workshop on Wireless Ad Hoc and Sensor

Networks (IEEE IWWAN 2006), New York, June 28-30, 2006.

[27] S.Ghiasi, A. Srivatava, X.Yang, and M.Sanafzadeh, ”Optimal Energy Aware

Clustering in Clustering in Sensor Networks”, in Sensors, volume 2, pp. 258-

269, Feb. 2002.

[28] W. Heinzelman, A. Chandrakasan and H. Balakrishnan, ”Energy-efficient

routing protocols for wireless microsensor networks,” in Proc. 33rd Hawaii

International Conference System Sciences (HICSS), Maui, HI, 2000.

[29] B. P. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, ”An applica-

tion specific protocol architecture for wireless microsensor networks,” IEEE

Transactions on Wireless Communications, vol. 1, no. 4, pp. 660-670, 2002.

[30] Latif. K, Jaffar. M, Javaid. N, Saqib. M. N, Qasim. U, Khan. Z. A, Per-

formance Analysis of Hierar- chical Routing Protocols in Wireless Sensor Net-

works, 5th International Workshop on Next Generation of Wireless and Mobile

Networks (NGWMN 2012).

[31] Azada, Puneet, and Vidushi Sharmab. “Cluster Head Selection in Wireless

Sensor Networks under Fuzzy Environment.” ISRN Sensor Networks, vol. 2013,

Article ID 909086, 8 pages, 2013. doi:10.1155/2013/909086.

55