Analyzing Sink Mobility in DEEC and its Variantsof the requirement for the degree of MS (Electrical Engineering) By Miss Momena Malik CIIT/FA11-REE-008/ISB Fall, 2012 iii Analyzing

i

Analyzing Sink Mobility in DEEC and its Variants

By Miss Momena Malik

Registration Number: CIIT/FA11-REE-008/ISB MS Thesis

In Electrical Engineering

COMSATS Institute of Information Technology Islamabad – Pakistan

FALL, 2012

ii

Analyzing Sink Mobility in

DEEC and its Variants

A Thesis presented to COMSATS Institute of Information Technology

In partial fulfillment of the requirement for the degree of

MS (Electrical Engineering)

By

Miss Momena Malik

CIIT/FA11-REE-008/ISB

Fall, 2012

iii


A Graduate Thesis submitted to Department of Electrical Engineering as partial fulfillment of the requirement for the award of Degree of M. S.

(Electrical Engineering).

Name Registration Number Miss Momena Malik CIIT/FA11-REE-008/ISB

Co-supervisor: Dr. Nadeem Javaid, Assistant Professor,

Center for Advanced Studies in Telecommunications (CAST), COMSATS Institute of Information Technology (CIIT),

Islamabad Campus, December, 2012

Supervisor: Dr. Mahmood Ashraf Khan,

Director, Center for Advanced Studies in Telecommunications (CAST),

COMSATS Institute of Information Technology (CIIT), Islamabad Campus,

December, 2012

iv

Final Approval

This thesis titled


By Miss Momena Malik

CIIT/FA11-REE-008/ISB

has been approved for the COMSATS Institute of Information Technology, Islamabad

External Examiner: __________________________________ (To be decided)

Co-Supervisor: ________________________ Dr. Nadeem Javaid /Assistant professor, Center for Advanced Studies in Telecommunications (CAST), CIIT, Islamabad.

Supervisor: ________________________ Dr. Mahmood Ashraf Khan/Director, Center for Advanced Studies in Telecommunications (CAST), CIIT, Islamabad. Head of Department:________________________ Dr. Raja Ali Riaz / Associate professor, Department of Electrical Engineering, CIIT, Islamabad.

v

Declaration

I Miss Momena Malik, CIIT/FA11-REE-008/ISB herebyxdeclare that I havexproduced the workxpresented inxthis thesis, duringxthe scheduledxperiod of study. I also declare that I havexnot taken anyxmaterial from anyxsource exceptxreferred toxwherever due that amountxof plagiarism isxwithin acceptablexrange. If a violationxof HEC rulesxon research hasxoccurred in thisxthesis, I shall be liablexto punishablexaction under the plagiarismxrules of the HEC.

Date: ________________ ________________ Miss Momena Malik CIIT/FA11-REE008/ISB

vi

Certificate

It is certified that Miss Momena Malik, CIIT/FA11-REE-008/ISB has carried out all the work related to this thesis under my supervision at the Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad and the work fulfills the requirements for the award of MS degree.

Date: _________________ Co-Supervisor:____________________ Dr. Nadeem Javaid /Assistant professor, Center for Advanced Studies in Telecommunications (CAST), CIIT, Islamabad.

Supervisor: ________________________ Dr. Mahmood Ashraf Khan/Director, Center for Advanced Studies in Telecommunications (CAST), CIIT, Islamabad.

____________________________ Head of Department: Dr. Raja Ali Riaz/Associate Professor, Department of Electrical Engineering, CIIT, Islamabad.

vii

DEDICATION

Dedicated to my parents.

viii

ACKNOWLEDGMENT I am heartily grateful to my supervisor, Dr. Mahmood Ashraf Khan, and co-supervisor Dr. Nadeem Javaid whose patient encouragement, guidance and insightful criticism from the beginning to the final level enabled me have a deep understanding of the thesis. Lastly, I offer my profound regard and blessing to everyone who supported me in any respect during the completion of my thesis especially my friends in every way offered much assistance before, during and at completion stage of this thesis work.

Miss Momena Malik CIIT/FA11-REE-008/ISB

ix

List of Publications

[1] M.Momena, Javaid.N “On Performance Evaluation of Variants of DEEC in WSNs”, published in 7th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2012), Victoria, Canada, 2012.

x

List of Abbreviations WSNs Wireless Sensor Networks

LEACH Low Energy Adaptive Clustering Hierarchy

MEMS Micro Electro Mechanical Sensor

TEEN Threshold Sensitive Energy Efficient Routing Protocol

SEP Stable Election Probability

DEEC Distributed Energy Efficient Clustering

CH Cluster Head

BS Base Station

ATPC Adaptive Transmission Power Control

TSP Travelling Salesman Problem

DDEEC Developed Distributed Energy Efficient Clustering

TDEEC Threshold Distributed Energy Efficient Clustering

EDEEC Enhanced Distributed Energy Efficient Clustering

PEGASIS Power Efficient Gathering in Sensor Information Systems

HEED Hybrid Energy Efficient Distributed Clustering

Table of Contents

1 Abstract 1

2 Introduction 2

3 Background and motivation for thesis 5

3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4 On Performance Evaluation of Variants of DEEC in WSNs 8

4.1 Heterogeneous WSN Model . . . . . . . . . . . . . . . . . . . . . . 8

4.1.1 Two Level Heterogeneous WSNs Model . . . . . . . . . . . . 8

4.1.2 Three Level Heterogeneous WSN Model . . . . . . . . . . . 9

4.2 Radio Dissipation Model . . . . . . . . . . . . . . . . . . . . . . . . 9

4.3 Overview of Distributed Heterogenous Protocols . . . . . . . . . . . 10

4.3.1 DEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.3.2 DDEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.3.3 EDEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.3.4 TDEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4.4 Performance Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . 15

4.5 Simulations And Discussions . . . . . . . . . . . . . . . . . . . . . . 15

5 Analyzing Sink Mobility in DEEC and its Variants 27

5.1 Sink Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.2 Simulations And Discussions . . . . . . . . . . . . . . . . . . . . . . 28

5.3 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6 Conclusions 37

References 38

xi

List of Figures

4.1 Radio Energy Dissipation Model . . . . . . . . . . . . . . . . . . . . 9

4.2 Nodes dead during rounds . . . . . . . . . . . . . . . . . . . . . . . 16

4.3 Nodes alive during rounds . . . . . . . . . . . . . . . . . . . . . . . 17

4.4 Packets to the BS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17



4.7 Packets to the BS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19



4.10 Packets to the BS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21



4.13 Packets to the BS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22



4.16 Packets to the BS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24



4.19 Packets to the BS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.1 Static sink at the center of square field region . . . . . . . . . . . . 29

5.2 Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC and

TDEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.3 Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC and

TDEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.4 Packets sent to the base station during rounds for DEEC, DDEEC,

EDEEC and TDEEC . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.5 Sink moving linearly at the center of the network . . . . . . . . . . 31


TDEEC with linear sink mobility . . . . . . . . . . . . . . . . . . . 32

xii


TDEEC with linear sink mobility . . . . . . . . . . . . . . . . . . . 33


EDEEC and TDEEC with linear sink mobility . . . . . . . . . . . . 33

5.9 Sink moving in zigzag pattern across the network . . . . . . . . . . 34


TDEEC with sink mobility in zigzag pattern . . . . . . . . . . . . . 34


TDEEC with sink mobility in zigzag pattern . . . . . . . . . . . . . 35


EDEEC and TDEEC with sink mobility in zigzag pattern . . . . . 35

xiii

List of Tables

4.1 Value of parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5.1 Value of parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.2 Values of network protocols with n=200 (scenario 1) . . . . . . . . . 36



xiv

Chapter 1

Abstract

Wireless Sensor Networks (WSNs) contain a huge number of sensor nodes having

bounded power energy, which transmit their sensed information to the Base Sta-

tion (BS) that is highly power constrained. The importance of WSN arises from

their capability for detailed monitoring in remote and inaccessible areas where it

is not feasible to install conventional wired infrastructure. Many routing proto-

cols [23-29] have been proposed in this regard achieving energy efficiency in het-

erogeneous scenarios. However, every protocol is not suitable for heterogeneous

WSNs. Efficiency of protocol degrades while changing the heterogeneity parame-

ters. In this thesis, firstly I test Distributed Energy-Efficient Clustering (DEEC),

Developed DEEC (DDEEC), Enhanced DEEC (EDEEC) and Threshold DEEC

(TDEEC) under several different scenarios containing high level heterogeneity to

low level heterogeneity. I thoroughly observe their performance based on stability

period, network life time and throughput. EDEEC and TDEEC perform better in

all heterogeneous scenarios containing variable heterogeneity in terms of life time,

however TDEEC is best of all for the stability period of the network. However,

the performance of DEEC and DDEEC is highly effected by changing the hetero-

geneity parameters of the network.

Then, I have taken four protocols into consideration that are Distributed Energy-

Efficient Clustering (DEEC), Developed DEEC (DDEEC), Enhanced DEEC (EDEEC)

and Threshold DEEC (TDEEC) for comparing their performances in different sce-

narios of sink and network. Former their performance is observed with static sink

and later by keeping sink mobile to various patterns in the network. This thesis

caters for real time situations where throughput rate and network life of wireless

WSNs are always needed to be improved.

1

Chapter 2

Introduction

A Wireless Sensor Network (WSN) consists of spatially distributed autonomous

sensors to monitor physical or environmental conditions, such as temperature,

sound, pressure, etc. at different locations. WSN has become a prominent area of

research in recent years in industry because of its potential to provide applications

that inter relate the physical world to the imperative world. Technological devel-

opments in the field of Micro Electro Mechanical Sensors (MEMS) have enabled to

produce tiny, low power, low cost sensors having limited processing, wireless com-

munication and energy resource capabilities. With the passage of time researchers

have found new applications of WSN. The serving applications of such large-scale

WSNs exist in a number of fields, such as, medical monitoring, environmental

monitoring, industrial machine monitoring, home security, surveillance and mil-

itary operations. Wireless sensors are also used for monitoring data in various

infrastructures like bridges, flyovers, embankments, tunnels etc that benefits the

engineering tasks by online data monitoring which is more accurate rather than

visiting the sites virtually. To achieve fault tolerance, WSN consists of numerous

sensors randomly installed inside the sensing region of interest [1]. All the nodes

have to send their data towards BS often called as sink. Usually nodes in WSN are

power constrained due to limited battery, it is not possible to recharge or replace

the battery of already deployed nodes and nodes might be placed where they can

not be accessed. These nodes may be present far away from BS so direct commu-

nication is not feasible due to limited battery as direct communication requires

high energy.

Clustering is the key technique for decreasing battery consumption in which mem-

bers of the cluster select a Cluster Head (CH). Many clustering protocols are de-

signed in this regard [2,3,21,30]. In clustering, member nodes of a cluster send

2

their sensing data to CH, where CH aggregates this data and transmits it to the

BS [4-6]. Under aggregation, fewer messages are sent to BS and only few nodes

have to transmit over large distance, so high energy is saved and over all lifetime of

the network is prolonged. Energy consumption for aggregation of data is much less

as compared to energy used in data transmission. There are two types of networks

where clustering can be used i.e homogenous and heterogeneous networks. Nodes

having same energy level are called homogenous network and nodes having differ-

ent energy levels called heterogeneous network. Low-Energy Adaptive Clustering

Hierarchy (LEACH) [5], Power Efficient Gathering in Sensor Information Systems

(PEGASIS) [7], Hybrid Energy-Efficient Distributed clustering (HEED) [8] are

algorithms designed for homogenous WSN under consideration so these protocols

do not work efficiently under heterogeneous scenarios because these protocols are

unable to differentiate the nodes in terms of their energy. Whereas, Stable Elec-

tion Protocol (SEP) [9], Distributed Energy-Efficient Clustering (DEEC) [10],

Developed DEEC (DDEEC) [11], Enhanced DEEC (EDEEC) [12] and Threshold

DEEC (TDEEC) [13] are algorithms designed for heterogeneous WSN. SEP is de-

signed for two level heterogeneous networks, so it can not work efficiently in three

or multilevel heterogeneous network. SEP considers only normal and advanced

nodes where normal nodes have low energy level and advanced nodes have high

energy. DEEC, DDEEC, EDEEC and TDEEC are designed for multilevel het-

erogeneous networks and can also perform efficiently in two level heterogeneous

scenarios.

In this thesis, performance of heterogeneous WSN protocols under three and

multi level heterogeneous networks is studied. I compare performance of DEEC,

DDEEC, EDEEC and TDEEC for different scenarios of three level and multi-

level heterogeneous WSNs. Three level heterogeneous networks contain normal,

advanced and super nodes whereas super nodes have highest energy level as com-

pared to normal and advanced nodes. I discriminate each protocol on the basis

of prolonging stability period, network life time of nodes alive during rounds for

numerous three level heterogeneous networks. Each containing different ratio of

normal, advanced and super nodes along with the multilevel heterogeneous WSNs.

It is found that different protocols have different efficiency for three level and mul-

tilevel heterogeneous WSNs in terms of stability period, network life time and

throughput. DEEC and DDEEC perform well under three level heterogeneous

WSNs containing high energy level difference between normal, advanced and super

nodes in terms of stability period. However, it lacks in performance as compared

to EDEEC and TDEEC in terms of network lifetime. Whereas, EDEEC and

3

TDEEC perform well under multi and three level heterogeneous WSNs containing

low energy level difference between normal, advanced and super nodes in terms of

both stability period and network lifetime.

In recent years, unlike static sink, researchers have gained much interest in the field

of sink mobility due to its many real time applications and its ability to enhance

the energy efficiency and throughput of the sensor network. The phenomenon of

sink mobility is commonly considered as one of the most efficient ways of load

balancing in a network, ultimately leading to lesser failed nodes and prolonged

network lifetime. In this thesis, I have introduced sink mobility in different fashions

in the network and observed their results. Firstly, the performance evaluation of

DEEC, DDEEC, EDEEC and TDEEC is estimated in a 100m × 100m network.

Then the shape of the network is changed into a tunnel having dimensions of

100m × 20m where sink is made mobile linearly at the center of network and

its performance results are evaluated for DEEC and its variants. An alteration

to sink trajectory includes its zigzag motion across the network of dimensions

100m × 20m. The network performance of this sink trajectory is observed for

DEEC and its variants. In real time scenarios, the field region may not always

be square but can be of various shapes and dimensions. So, this research work

focuses on a different network region (rectangular) such as tunnel which finds its

applications in coal mines, railways and salt mines.

4

Chapter 3

Background and motivation for

thesis

3.1 Related Work

T. N. Qureshi, N. Javaid and M. Malik [14] worked on a detailed survey on het-

erogeneous DEEC and its variants under varying radio parameters and evaluated

their performance in terms of networks nodes alive, network nodes dead and pack-

ets sent to the BS.

Heinzeman, et al. [5] developed a clustering protocol for homogeneous WSNs

called as LEACH in which nodes randomly select themselves to be CHs and pass

on this selection criteria over the entire network to distribute energy load.

G. Smaragdakis, et al. [9] proposed a two level hierarchical network protocol called

as SEP in which CH selection is based on initial energy of the node with respect

to other nodes.

L .Qing, et al. [10] worked on heterogeneous WSN and proposed a protocol named

as DEEC in which CH selection is based on the probability of the ratio of (Eres)

(Eres) and Average Energy (Eavg) of the network.

Brahim Elbhiri, et al. [11] worked on heterogeneous WSN and proposed a proto-

col named as DDEEC in which CH selection is based on (Eres) to balance it over

the entire network. As the advanced nodes energy level is higher as compared to

the energy level of normal nodes so they will firstly be elected as CHs for first

5

transmission rounds. So, a point will come when their energy will drop down to

the level that their CH election probability will become equal to normal nodes.

P. Saini et al. [12] proposed a protocol EDEEC which is extended to three level

heterogeneity by adding an extra amount of energy level known as super nodes.

Parul Saini and Ajay K Sharma [13] proposed a protocol TDEEC scheme selects

the CH from the high energy nodes improving energy efficiency and lifetime of the

network.

Zhengjie Wang, et al. [15] have introduced the concept of linear WSN and dis-

cussed the classification of topology and the main issue in this network by pre-

senting applications of the linear WSN such as road, bridge, tunnel and pipeline.

Sudarmani.R, et al. [16] have evaluated a Load Balanced Heterogeneous Sen-

sor networks with Adaptive Transmission Power Control (ATPC) and mobile

sink which when compared to stationary sink depicted less energy consumption

amount.

Yan Zhao, et al. [17] proposed a protocol for coal mine which uses mobile sink

and works on an agent based routing protocol having four specific features in it.

Yamaimaiti Nizhamudong, et al. [18] proposed Mobile Sink node control method

for WSN which evaluates the performance of route cost for a mobile sink node.

The fixed nodes form the clusters and Mobile Sink node by using the Nearest

Addition Method of TSP (Traveling Salesman Problem) decides the best rotation

of the communication among the clusters, after, it decides the best fixed node to

make the shortest distance of communication. The chosen best fixed nodes will

transfer its data to the Mobile Sink node when it reached to them.

3.2 Motivation

Many algorithms are recently proposed to increase stability and lifetime of het-

erogeneous WSNs. However, heterogeneous networks are of different types having

different parameters. Every algorithm does not work efficiently for different net-

works having different heterogeneity levels and fails to maintain the same stability

period and lifetime as in previous heterogeneous WSNs. Some algorithms work

efficiently in heterogeneous WSNs containing low energy difference between nor-

mal, advanced and super nodes and some algorithms work efficiently in networks

6

containing high energy difference between normal, advanced and super nodes. So

I interpret each algorithm in this thesis, on basis of types of heterogeneous net-

works containing different heterogeneity level and parameters on basis of stability

period, lifetime of network and packets sent to the BS.

In recent years of research, many routing algorithms such as LEACH, SEP or

DEEC have implemented square field regions to improve the energy efficiency and

stability of WSN. These algorithms use clustering for transmitting their data to

BS which is static in the sensing field. Since the network nodes deployed do not

change their position with non-mobile sink so throughput results are not favorable.

Moreover, heterogeneous WSNs have not discussed their techniques with mobile

sink and for different dimensional areas such as tunnel(rectangular). And we know

that there is always a need to enhance the parameters of energy and stability in

heterogeneous environmental conditions to tackle with real time scenarios. There-

fore, in my research, these requirements are fulfilled by introducing sink mobility

in heterogeneous DEEC, DDEEC, EDEEC and TDEEC. With sink being mobile,

throughput rate in DEEC and its variants is improved and network performance

is greatly enhanced.

7

Chapter 4

On Performance Evaluation of

Variants of DEEC in WSNs

4.1 Heterogeneous WSN Model

In this section, N number of nodes are assumed that are placed in a square region

of dimension M ×M . Heterogeneous WSNs contain two, three or multi types of

nodes with respect to their energy levels and are termed as two, three and multi

level heterogeneous WSNs respectively.

4.1.1 Two Level Heterogeneous WSNs Model

Two level heterogeneous WSNs contain two energy level of nodes, normal and

advanced nodes. Where, E0 is the energy level of normal node and E0(1 + a) is

the energy level of advanced nodes containing a times more energy as compared

to normal nodes. If N is the total number of nodes then Nm is the number of

advanced nodes where m refers to the fraction of advanced nodes and N(1 −m)

is the number of normal nodes. The total initial energy of the network is the sum

of energies of normal and advanced nodes.

Etotal = N(1−m)E0 +Nm(1 + a)E0

= NE0(1−m+m+ am)

= NE0(1 + am)

(4.1)

The two level heterogeneous WSNs contain am times more energy as compared

8

to homogeneous WSNs.

4.1.2 Three Level Heterogeneous WSN Model

Three level heterogeneous WSNs contain three different energy levels of nodes

i.e normal, advanced and super nodes. Normal nodes contain energy of E0, the

advanced nodes of fraction m are having a times extra energy than normal nodes

equal to E0(1 + a) whereas, super nodes of fraction m0 are having a factor of b

times more energy than normal nodes so their energy is equal to E0(1 + b). As

N is the total number of nodes in the network, then Nmm0 is total number of

super nodes and Nm(1−m0) is total number of advanced nodes. The total initial

energy of three level heterogeneous WSN is therefore given by:

Etotal = N(1−m)E0 +Nm(1−m0)(1 + a)E0 +Nm0E0(1 + b) (4.2)

Etotal = NE0(1 +m(a+m0b)) (4.3)

The three level heterogeneous WSNs contain (a + m0b) times more energy as

compared to homogeneous WSNs.

4.2 Radio Dissipation Model

L bit packet Transmit

ElectronicsTx Amlifier

Receiver

Electronics

L bit packet

ETx(d)

EeleTX *L Eamp *L*d2

EeleRX *L

d

Figure 4.1: Radio Energy Dissipation Model

9

The radio energy model presented in figure 1 describes that an l bit message is

transmitted over a distance d, the energy expended is then given by:

ETx(l, d) =

lEelec + lεfsd2, d < d0

lEelec + lεmpd4, d ≥ d0

(4.4)

Where, Eelec is the electronics energy of the transmitter or receiver circuit. d is the

distance between node and BS. If this distance is less than threshold, it will use

the free space(fs) model else multi path(mp) model is used. Now, in one round

the energy dissipated in the network is given by:

Eround = L(2NEelec +NEDA + kεmpd4toBS +Nεfsd

2toCH) (4.5)

Where, K= number of clusters

EDA= Data aggregation cost expended in CH

dtoBS= Average distance between the CH and BS

dtoCH= Average distance between the cluster members and the CH

dtoCH =M√2πk

, dtoBS = 0.765M

2(4.6)

kopt =

√N√2π

√εfsεmp

M

d2toBS

(4.7)

4.3 Overview of Distributed Heterogenous Pro-

tocols

4.3.1 DEEC

DEEC is designed to deal with nodes of heterogeneous WSNs. For CH selection

in DEEC is based on initial and (Eres) level of nodes. Let ni denote the number

of rounds to be a CH for node si. poptN is the optimum number of CHs in our

network during each round. CH selection criteria in DEEC is based on energy

level of nodes. As in homogenous network, when the amount of energy of nodes

10

is same during each epoch then choosing pi = popt assures poptN CHs during each

round. In WSNs, nodes with high energy have high chances to be elected as CH

than nodes with low energy but the net value of CHs during each round is equal

to poptN . pi is the probability for each node si to become CH, so the node with

high energy has larger value of pi as compared to the popt. E(r) denotes average

energy of network during round r which can be given as defined in reference[10]:

E(r) =1

N

N∑i=1

Ei(r) (4.8)

Probability for CH selection in DEEC is given as defined in reference[10]:

pi = popt[1−E(r)− Ei(r)

E(r)] = popt

Ei(r)

E(r)(4.9)

In DEEC the average total number of CH during each round is given as defined

in reference [10]:

N∑i=1

pi =N∑i=1

poptEi(r)

E(r)= popt

N∑i=1

Ei(r)

E(r)= Npopt (4.10)

Probability for a node to become CH is written as:

T (si) =

pi

1−pi(rmod 1Pi

)if siϵG

0 otherwise(4.11)

Where G is the set of nodes eligible to become CH at round r. If node becomes CH

in recent rounds then it belongs to G. All nodes are supposed to select an arbitrary

number between 0 and 1. The nodes with Random Number (RN) lower than the

threshold value becomes CH. [10] As popt is reference value of average probability

pi. In homogenous networks, initial energy of the nodes is same so they use popt

to be the reference energy for probability pi. However in heterogeneous networks,

the initial energy of nodes is different and hence different value of popt. In two level

heterogenous network the value of popt is given by as defined in reference [10]:

11

padv =popt

1 + am, pnrm =

popt(1 + a)

(1 + am)(4.12)

Then use the above padv and pnrm instead of popt in equation 4.10 for two level

heterogeneous network as defined in reference [10]:

pi =

poptEi(r)

(1+am)E(r)if si is the normal node

popt(1+a)Ei(r)

(1+am)E(r)if si is the advanced node

(4.13)

Above model can also be expanded to multi level heterogenous network given

below as defined in reference [10]:

pmulti =poptN(1 + ai)

(N +∑N

i=1 ai)(4.14)

Putting above pmulti in equation 4.10 instead of popt to get pi for heterogeneous

node. pi for the multilevel heterogeneous network is given by as defined in reference

[10]:

pi =poptN(1 + a)Ei(r)

(N +∑N

i=1 ai)E(r)(4.15)

In DEEC, average energy E(r) of the network for any round r is estimated as

defined in reference [10]:

E(r) =1

NEtotal(1−

r

R) (4.16)

R denotes total rounds of network lifetime and is estimated as follows:

R =Etotal

Eround

(4.17)

Etotal is total energy of the network where Eround is energy expenditure during

12

each round.

4.3.2 DDEEC

DDEEC uses same method for estimation of average energy in the network and

CHs are selected on the basis of (Eres) as implemented in DEEC. Difference be-

tween DDEEC and DEEC is centered in expression that defines probability for

normal and advanced nodes to be a CH [11] as given in equation 4.14.

It is found that nodes with having (Eres) at round r have more chances to become

CH, so in this way nodes having higher energy values or advanced nodes will be-

come CH more often as compared to the nodes with lower energy or normal nodes.

A point comes in a network where advanced nodes having same (Eres) like normal

nodes. Although, after this point DEEC continues to punish the advanced nodes

so this is not an exact way for energy distribution because by doing so, advanced

nodes are continuously becoming a CH and they die more quickly than normal

nodes. In order to maintain the energy balance, DDEEC makes some changes in

equation 14 to save advanced nodes from being punished over and again. DEEC

introduces threshold (Eres) as in reference [11] and given below:

ThREV = E0(1 +aEdisNN

EdisNN − EdisAN

) (4.18)

When energy level of advanced and normal nodes falls down to the limit of thresh-

old (Eres) then both type of nodes use same probability to become cluster head.

Therefore, CH selection is balanced and more efficient. Threshold (Eres) Th is

given as in reference [11] and given below:

ThREV ≃ (7/10)E0 (4.19)

Average probability pi for CH selection used in DDEEC is as follows as in reference

[11]:

pi =

poptEi(r)

(1+am)E(r)for Nml nodes, Ei(r) > ThREV

(1+a)poptEi(r)

(1+am)E(r)for Adv nodes, Ei(r) > ThREV

c(1+a)poptEi(r)

(1+am)E(r)for Adv, Nml nodes, Ei(r) ≤ ThREV

(4.20)

13

4.3.3 EDEEC

EDEEC uses concept of three level heterogeneous network as described above. It

contains three types of nodes normal, advanced and super nodes based on initial

energy. pi is probability used for CH selection and popt is reference for pi. EDEEC

uses different popt values for normal, advanced and super nodes, so, value of pi in

EDEEC is as follows as in reference [12]:

pi =

poptEi(r)

(1+m(a+m0b))E(r)if si is the normal node

popt(1+a)Ei(r)

(1+m(a+m0b))E(r)if si is the advanced node

popt(1+b)Ei(r)

(1+m(a+m0b))E(r)if si is the super node

(4.21)

Threshold for CH selection for all three types of node is as follows as in reference

[12]:

T (si) =

pi1−p

i(rmod 1pi

)

ifpiϵG′

pi1−pi(rmod 1

pi)

ifpiϵG′′

pi1−pi(rmod 1

pi)

ifpiϵG′′′

0 otherwise

(4.22)

4.3.4 TDEEC

TDEEC uses same mechanism for CH selection and average energy estimation as

proposed in DEEC. At each round, all nodes are supposed to select a RN between 0

and 1. The nodes with RN lower than the threshold value defined in equation 4.24

becomes CH. In TDEEC, threshold value is adjusted and based upon that value

a node decides whether to become a CH or not by introducing (Eres) and average

energy of that round with relative to optimum number of CHs [13]. Threshold

value proposed by TDEEC is given as follows as in reference [13]:

T (s) = { p

1− p(rmod1p)

∗ residual energy of a node ∗ koptaverage energy of the network

(4.23)

14

4.4 Performance Criteria

Performance parameters used for evaluation of clustering protocols for heteroge-

neous WSNs are lifetime of heterogeneous WSNs, number of nodes alive during

rounds and data packets sent to BS.

Lifetime is a parameter which shows that node of each type has not yet consumed

all of its energy.

Number of nodes alive is a parameter that describes number of alive nodes dur-

ing each round.

Data packets sent to the BS is the measure that how many packets are received

by BS for each round.

These parameters depict stability period, instability period, energy consumption,

data sent to the BS, and data received by BS and lifetime of WSNs. Stabil-

ity period is period from start of network until the death of first node whereas,

instability period is period from the death of first node until last one.

Table 4.1: Value of parameters

Parameters ValuesNetwork field 100 m,100 mNumber of nodes 100E0(initial energy of normal nodes) 0.5JMessage size 4000 bitsEelec 50nJ/bitEfs 10nJ/bit/m2

Eamp 0.0013pJ/bit/m4

EDA 5nJ/bit/signald0(threshold distance) 70mPopt 0.1

4.5 Simulations And Discussions

In this section, different clustering protocols in heterogeneous WSN are simulated

using MATLAB and for simulations 100 nodes are randomly deployed in a sensing

region of dimension 100m×100m. For simplicity, consider all nodes either fixed or

15

micro-mobile as supposed in [14] and ignore energy loss due to signal collision and

interference between signals of different nodes that are due to dynamic random

channel conditions. In this scenario, I am considering that, BS is placed at center

of the network field. I simulate DEEC, DDEEC, EDEEC and TDEEC for three-

level and multi-level heterogeneous WSNs. Scenarios describe values for number

of nodes dead in first, tenth and last rounds as well as values for the packets sent

to BS by CH at different values of parameters m, m0, a and b. These values are

examined for DEEC, DDEEC, EDEEC and TDEEC.

In heterogeneous WSN, radio parameters mentioned in Table 4.1 are used for

different protocols deployed in WSN and the performance for three level hetero-

geneous WSNs is estimated. Parameter m refers to fraction of advanced nodes

containing extra amount of energy a in network whereas, m0 is a factor that refers

to fraction of super nodes containing extra amount of energy b in the network.

Figure 4.2: Nodes dead during rounds

For the case of a network containing m = 0.5 fraction of advanced nodes having

a = 1.5 times more energy and m0=0.4 fraction of super nodes containing b = 3

times more energy than normal nodes. From Fig. 4.2 and 4.3, it is examined that

first node for DEEC, DDEEC, EDEEC and TDEEC dies at 1117, 1470, 1583 and

1719 rounds respectively. Tenth node dies at 1909, 1863, 1726 and 1297 rounds

respectively. All nodes are dead at 5588, 6180, 9873 and 9873 rounds respectively.

It is obvious from the results of all protocols that in terms of stability period,

TDEEC performs best of all, EDEEC performs better than DEEC and DDEEC

16

Figure 4.3: Nodes alive during rounds

Figure 4.4: Packets to the BS

but has less performance than TDEEC. DDEEC only performs well as compared

to DEEC and DEEC has least performance than all the protocols. Stability period

of DEEC and DDEEC is lower than EDEEC and TDEEC because the probabil-

ities in TDEEC and EDEEC are defined separately for normal, advanced and

super nodes whereas, DEEC and DDEEC do not use different probabilities for

normal, advanced and super nodes so their performance is lower than EDEEC

17

and TDEEC. However, instability period of EDEEC and TDEEC is much larger

than DEEC and DDEEC. The number of nodes alive in TDEEC is quite larger

than EDEEC because in TDEEC the formula of threshold used by nodes for CH

election is modified by including residual and average energy of that round. So

nodes having high energy will become CHs. Similarly, by examining results of

Fig. 4.4, packets sent to the BS by DEEC, DDEEC, EDEEC and TDEEC have

their values at 125316, 139314, 391946 and 470248. Now it is seen that packets

sent to BS for DEEC and DDEEC is almost same whereas, the packets sent to BS

for EDEEC and TDEEC are almost the same because the probability equations

for normal, advanced and super nodes is same in both of them. Now coming to

the CHs, the packets sent to CHs increase during the start of the network and

gradually decrease down towards the end due to the nodes dying simultaneously.


Now considering second case in which the parameters change to a = 1.3, b = 2.5,

m = 0.4 and m0 = 0.3. Fig. 4.5 shows that first node for DEEC, DDEEC,

EDEEC and TDEEC dies of each protocol at 1291, 1355, 1367 and 1694 rounds

respectively. Tenth node dies at 1531, 1547, 1574 and 1946 rounds respectively.

All nodes are dead at 4870, 4779, 7291, 7291 rounds. Graph for number of nodes

alive in first, tenth and all rounds is exactly the flip to the graph for number of

nodes dead and is shown in Fig. 4.6. Results of Fig. 4.7 show that packets sent

to BS by DEEC, DDEEC, EDEEC and TDEEC are 135650, 107891, 300735 and

365628 respectively. As it is seen, that by decreasing the values of parameters,

18



TDEEC still performs best among the four protocols. EDEEC performs bet-

ter than TDEEC. DDEEC performs better than TDEEC and EDEEC whereas,

DEEC performs worst.

Now considering third case, parameter values further decrease to a = 1.2, b = 2,

19



m = 0.3, m0 = 0.2 in which first node for DEEC, DDEEC, EDEEC and TDEEC

dies at 963, 1158, 1309, and 1753 rounds respectively. Tenth node dies at 1290,

1573, 1556 and 2026 rounds respectively. All nodes are dead at 6533, 4386, 7467

and 7467 rounds respectively. Similarly, the packets to BS sent in DEEC, DDEEC,

EDEEC and TDEEC are 132378, 91269, 259370 and 339406 respectively as shown

in Fig. 4.8, 4.9 and 4.10.

20



Now considering fourth case, parameters are increased to a = 1.6, b = 3.2,

m = 0.6, m0 = 0.5. Results show that for DEEC, DDEEC, EDEEC and TDEEC

first node dies at 1576, 1495, 1382 and 1863 round respectively. Tenth node

dies at 2245, 2213, 1691 and 2574 round respectively and all nodes are dead at

21



5498, 6092, 9331 and 9331 round respectively. Packets sent to the BS in DEEC,

DDEEC, EDEEC and TDEEC are 116181, 162506, 455423 and 521450 respec-

tively as shown in Fig. 4.11, 4.12 and 4.13.

Now considering the fifth case and further more increasing the parameters to

22



a = 1.7, b = 3.4, m = 0.7, m0 = 0.6 it is observed that for DEEC, DDEEC,

EDEEC and TDEEC first node dies at 1763, 1584, 1551, 1897 rounds respectively.

Tenth node dies at 2711, 2308, 1735, 2725 rounds respectively. All nodes dead for

DEEC and DDEEC are 8414, 6786 rounds and for EDEEC ,TDEEC still some

nodes are alive after 10000 rounds. Packets sent to the BS in DEEC, DDEEC,

EDEEC and TDEEC are 224095, 193931, 562819, 620606 respectively as shown

23


in Fig. 4.14, 4.15 and 4.16.


Now in last case considering multilevel heterogeneous network we see that for

DEEC, DDEEC, EDEEC and TDEEC first node dies at 1196,1262,1349,1688

rounds respectively. Tenth node dies at 1389, 1511, 1593, 2045 rounds respec-

tively and all nodes are dead at 5547, 3999, 6734, 6734 rounds. Packets sent

24



to the BS in DEEC, DDEEC, EDEEC and TDEEC are 106514, 79368, 236380,

314848 respectively as shown in Fig. 4.17, 4.18 and 4.19. It is observed from all

the above scenarios that for first case of three level heterogeneous WSN consider-

ing a = 1.5,b = 3,m = 0.5 and m0 = 0.4 TDEEC performs best of all, EDEEC

performs better than DDEEC and DEEC where DDEEC performs better than

DEEC in terms of stability period. For EDEEC and TDEEC instability period

25

is higher as compared to DDEEC and DEEC. When values of a, b, m, m0 are

decreased linearly further in second and third scenario, same results as in first

scenario are found for all protocols. In fourth and fifth scenarios when a, b, m,

m0 are increased linearly it is found after larger number of simulations that in

some scenarios DEEC performs better than DDEEC, EDEEC in terms of stabil-

ity period, TDEEC performs best and stability period of DDEEC and EDEEC is

almost the same. Whereas instability period of TDEEC and EDEEC is also larger

than DEEC and DDEEC even some nodes are not dead in EDEEC and TDEEC

after 10,000 rounds. In last case considering multilevel heterogeneous network in

which all nodes have random energy it is observed that TDEEC performs best of

all, EDEEC performs better than DDEEC and DEEC and DDEEC performs bet-

ter than DEEC in terms of stability period. For EDEEC and TDEEC instability

period is higher as compared to DDEEC.

26

Chapter 5

Analyzing Sink Mobility in

DEEC and its Variants

5.1 Sink Mobility

Data gathering is one of the basic tasks in WSN. It aims at gathering data from

sensor nodes in the network field with some static (non-mobile) sink for analysis

and processing. Sinks are capable machines with rich (often considered unlimited)

resources. The responsibilities of sink node include training the sensor network, its

maintenance and repair operations. The location of BS in the network has a huge

impact on the energy consumption and lifetime of WSNs. The energy of sensor

nodes near the BS exhausts very quickly in WSN when the BS is fixed, since they

do not only sense data of the nodes nearer to it but also sense and collect data

of the nodes placed at larger sensing ranges from it. Due to this unbalanced traf-

fic load, the sensor nodes in the network face the problem of non uniform power

dissipation. As a result of this, the sensor nodes power out earlier and network

gets disconnected. To cater this problem, the concept of sink mobility [19], [20]

is introduced to balance the energy dissipation among sensor nodes. The use of

sink mobility is considered as one of the most effective means of load balancing,

ultimately leading to lesser failed nodes and prolonged network lifetime [22]. A

mobile sink can adopt various trajectory patterns across the network such as sink

moving on the top of a square network region, sink moving at the center of the

network, across the borders, sink moving diagonally, zigzag and many other mo-

tion patterns.

In this thesis, sink mobility is introduced in a heterogeneous WSN and its per-

formance is estimated in different scenarios of sink location. Following section

elaborates the simulation results of these scenarios individually and plots their

27

graphs accordingly.

5.2 Simulations And Discussions

In this section, three different scenarios of heterogeneous wireless sensor networks

are created by deploying 100 nodes in the network region using the tool of MAT-

LAB. For simplicity, nodes are considered either fixed or micro-mobile in the field

where they are randomly dispersed. The BS is assumed to be located at the center

of the sensing region. I consider the following scenarios and observe their perfor-

mance measures.

In the first scenario, base station is located at the center of the 100m× 100m field

as shown in figure 5.1 and simulations of DEEC and its variants that are TDEEC,

DDEEC, EDEEC are plotted in this defined shape of the network. DEEC con-

tains two types of heterogeneous nodes, that are normal nodes and m fraction

of advanced nodes having α times more energy than the normal ones. Table 5.1

shows the radio parameters used for the simulations of all the three scenarios.

Table 5.1: Value of parameters

Parameters ValuesNumber of nodes 100E0(initial energy of normal nodes) 0.5JMessage size 4000 bitsEelec 50nJ/bitEfs 10nJ/bit/m2

Eamp 0.0013pJ/bit/m4

EDA 5nJ/bit/signald0(threshold distance) 70mPopt 0.1

Where, Eelec is transmitter/receiver electronics energy. EDA is the data aggrega-

tion energy expended in the cluster-heads. εfs or εmp is the amplifier energy that

depends on the transmitter amplifier model.

Results from figures 5.2 and 5.3 show the number of rounds where first node and

all the nodes die in DEEC, DDEEC, EDEEC and TDEEC. The values come out to

be 1473, 1309, 1332 and 1285 for number of rounds of first node dead respectively.

The values of rounds for all nodes dead come out be 2754, 3497, 9779 and 9832

respectively. These results show that of all protocols in terms of stability period,

TDEEC performs best, EDEEC performs better than DEEC and DDEEC but

has less performance than TDEEC. DDEEC only performs well as compared to

28

DEEC and DEEC has least performance than all the protocols. Stability period of

DEEC and DDEEC is lower than EDEEC and TDEEC because the probabilities

in TDEEC and EDEEC are defined separately for normal, advanced and super

nodes whereas, DEEC and DDEEC do not use different probabilities for normal,

advanced and super nodes. However, instability period of EDEEC and TDEEC

is much larger than DEEC and DDEEC. The number of nodes alive in TDEEC

is quite larger than EDEEC because in TDEEC the formula of threshold used by

nodes for CH election is modified by including (Eres) and (Eavg) of that round

relative to the optimum number of CHs. So nodes having high energy will become

CHs.

Figure 5.1: Static sink at the center of square field region

Similarly, figure 5.4 shows the packets sent to the base station per round for

DEEC, DDEEC, EDEEC and TDEEC are 72449, 93415, 342687 and 458591 re-

spectively. It can be seen the graph increases linearly for DEEC and DDEEC

up to 2000 rounds after that the difference is observed. Whereas, for EDEEC

and TDEEC the graph increases linearly up to almost 1700 rounds and after that

the shape of the graph gradually changes for both of them. So, TDEEC sends

the highest number of packets to base station because of the different probability

equations defined for normal, super and advanced nodes.

In the second scenario, length of network field is changed in the form of a tun-

nel by varying its vertical dimensions from 100m to 20m as shown in figure 5.5.

BS is assumed to be placed at the center of the sensing area. The hollow circles

29

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

10

20

30

40

50

60

70

80

90

100

no of rounds

no o

f dea

d no

des

DEECDDEECEDEECTDEEC

Figure 5.2: Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC andTDEEC

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

10

20

30

40

50

60

70

80

90

100

No of rounds

No

of a

live

node

s

DEECDDEECEDEECTDEEC

Figure 5.3: Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC andTDEEC

represent normal nodes and filled dark circles represent the advanced nodes in

the network. When the network is compressed in the shape of a tunnel, distance

between the nodes and the base station decreases also decreasing the distances

between the cluster head nodes and BS. This reasons out the minimum energy

30

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5x 10

5

no of rounds

pack

ets

to b

ase

stat

ion

DEECDDEECEDEECTDEEC

Figure 5.4: Packets sent to the base station during rounds for DEEC, DDEEC, EDEECand TDEEC

consumption in data transmission to the base station. But as the nodes use clus-

tering technique for transmitting their aggregated data to sink so energy is not

saved in this scenario. Member nodes send their respective data to CHs which

then sends the aggregated data to BS in spite of the fact that energy consumption

for direct transmission to BS is lesser as compared to the transmission of data to

BS through clustering technique.

Figure 5.5: Sink moving linearly at the center of the network

The concept of sink mobility is introduced where sink carries data through sensor

nodes while continuously moving in the middle line of network. Sink mobility

shortens length of the route and hence reduces the energy consumption of sen-

sor nodes. Starting from the location of sink motion, it has some nodes closer

31

to it and some nodes far away from it. As the sink moves in the network, its

distance increases from nodes closer to it and decreases from the nodes farther

from it. With sink being mobile, the performance of DEEC is compared with its

variants that are TDEEC, DDEEC and EDEEC. Figures 5.6 and 5.7 show the

rate of dead nodes with the number of rounds. It is examined that the number of

rounds where first node in DEEC and its variants die come out to be 1406, 1338,

1387 and 1405 respectively. Similarly, the number of rounds for all nodes dead in

DEEC, DDEEC, EDEEC and TDEEC come out to be 2766, 3255, 8506 and 8560

respectively. Figure 5.8 displays the amount of throughput or the packets sent to

the base station as 60400, 85159, 336603 and 447058 respectively. It can be seen

the graph increases linearly for DEEC and DDEEC up to 2000 rounds after that

the difference is observed. Whereas, for EDEEC and TDEEC the graph increases

linearly up to almost 1700 rounds and after that the shape of the graph gradually

changes for both of them. In this case, TDEEC performs best, EDEEC performs

better after TDEEC, and DDEEC shows good results than DEEC where DEEC

performs worst in terms of nodes dead in rounds.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

10

20

30

40

50

60

70

80

90

100

no of rounds

no o

f dea

d no

des

DEECDDEECEDEECTDEEC

Figure 5.6: Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC andTDEEC with linear sink mobility

In third scenario, the position for sink mobility is changed from linear to zigzag

motion as shown in figure 5.9 and its results are observed. All nodes are station-

ary except for the sink in the network. Mobile sink travels in zigzag trajectory to

32

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

10

20

30

40

50

60

70

80

90

100

no of rounds

no o

f aliv

e no

des

DEECDDEECEDEECTDEEC

Figure 5.7: Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC andTDEEC with linear sink mobility

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

5

no of rounds

pack

ets

to b

ase

stat

ion

DEECDDEECEDEECTDEEC

Figure 5.8: Packets sent to the base station during rounds for DEEC, DDEEC, EDEECand TDEEC with linear sink mobility

collect the sensor data. The distance of the sensor nodes to BS varies continuously

as the sink traverses across the network. It is observed from figure 5.10 that for

DEEC, DDEEC, EDEEC and TDEEC first node dies at 1464, 1258, 1318 and

1389. From figure 5.11 it is seen that all nodes die at 2939, 3095, 8554 and 8563.

33

Packets sent to the base station are 49085, 75950, 319597 and 448324 is depicted

in figure 5.12. It can be seen the graph increases linearly for DEEC and DDEEC

up to 2000 rounds after that the difference is observed. Whereas, for EDEEC and

TDEEC the graph increases linearly up to almost 1700 rounds and after that the

shape of the graph gradually changes for both of them. TDEEC sends the highest

number of packets to base station because of the different probability equations

defined for normal, super and advanced nodes.

Figure 5.9: Sink moving in zigzag pattern across the network

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

10

20

30

40

50

60

70

80

90

100

no of rounds

num

ber

of d

ead

node

s

DEECDDEECEDEECTDEEC

Figure 5.10: Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC andTDEEC with sink mobility in zigzag pattern

34

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

10

20

30

40

50

60

70

80

90

100

no of rounds

num

ber

of a

live

node

s

DEECDDEECEDEECTDEEC

Figure 5.11: Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC andTDEEC with sink mobility in zigzag pattern

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

5

no of rounds

pack

ets

to th

e ba

se s

tatio

n

DEECDDEECEDEECTDEEC

Figure 5.12: Packets sent to the base station during rounds for DEEC, DDEEC,EDEEC and TDEEC with sink mobility in zigzag pattern

5.3 Scalability

In this section, scalability is introduced in three scenarios mentioned in the pre-

vious section by varying the number of nodes in the network from 100 to 200 and

35

their performance values are estimated.

Table 5.2: Values of network protocols with n=200 (scenario 1)

Protocols First node dead All nodes dead ThroughputDEEC 1210 2730 79765DDEEC 987 2831 84597EDEEC 1259 9865 657402TDEEC 1247 9815 918492





36

Chapter 6

Conclusions

I have examined DEEC, E-DEEC, T-DEEC and D-DEEC for heterogeneous WSNs

containing different level of heterogeneity. Simulations prove that DEEC and

DDEEC perform well in the networks containing high energy difference between

normal, advanced and super nodes. Whereas, I find out that EDEEC and TDEEC

perform well in all scenarios. TDEEC has best performance in terms of stability

period and life time but instability period of EDEEC and TDEEC is very large. So,

EDEEC and TDEEC is improved in terms of stability period while compromising

on lifetime.

Then, I have compared the performance of DEEC, DDEEC, EDEEC and TDEEC

in a square field region by keeping sink static. After that I have implemented

linear sink mobility and compared the performance of heterogeneous DEEC and

its variants with single sink in the network field. These scenarios are considered

with clustering of nodes in the network. Further the sink mobility is enhanced

by varying its motion from linear to zigzag with single sink that shows different

values of throughput and network lifetime.

Future contribution to this work can be more variations in sink trajectories with

static nodes i.e sink can be placed across the borders of the network, in spiral

movement in a network, or moving diagonally in a network and energy consump-

tion can be evaluated. Network performance can be observed with nodes being

mobile and sink being static or nodes and sink both being mobile. Different re-

sults on stability period, network lifetime and throughput can be achieved by

introducing scalability in the network by further increasing the number of nodes.

Another future contribution to this work can be introducing joint sink mobility by

adding more than one mobile sinks in a network and their results can be measured.

As this research is focused on a sink mobility in a clustered network, so future

37

contribution can involve sink mobility in cluster less protocols and their network

performance can be estimated.

38

References

[1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor

networks: a survey, Computer Networks”, 38 (4) (2002) 393-422.

[2] P. Krishna, N.H. Vaidya, M. Chatterjee, D. Pradhan, “A cluster-based ap-

proach for routing in dynamic networks, ACM SIGCOMM Computer Com-

munication Review“, 27 (2) (1997) 49-65.

[3] B. McDonald, T. Znati, “Design and performance of a distributed dynamic

clustering algorithm for Ad-Hoc networks”, in: Proceedings of the Annual

Simulation Symposium, 2001.

[4] V. Mhatre, C. Rosenberg, D. Kofman, R. Mazumdar, N. Shroff, “Design of

surveillance sensor grids with a lifetime constraint”, in: 1st European Work-

shop on Wireless Sensor Networks (EWSN), Berlin, January 2004.

[5] W.R. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, “Energy efficient

communication protocol for wireless micro sensor networks”, in: Proceedings

of the 33rd Hawaii International Conference on System Sciences (HICSS-33),

January 2000.

[6] W.R. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, “An application- spe-

cific protocol architecture for wireless microsensor networks”, IEEE Transac-

tions on Wireless Communications 1 (4) (2002) 660-670.

[7] S. Lindsey, C.S. Raghavenda, PEGASIS: “power efficient gathering in sensor

information systems”, in: Proceeding of the IEEE Aerospace Conference, Big

Sky, Montana, March 2002.

[8] O. Younis, S. Fahmy, HEED: “A hybrid, energy-efficient, distributed clus-

tering approach for ad hoc sensor networks”, IEEE Transactions on Mobile

Computing 3 (4) (2004) 660-669.

[9] G. Smaragdakis, I. Matta, A. Bestavros,“SEP: A Stable Election Protocol

for clustered heterogeneous wireless sensor network”, in: Second International

39

Workshop on Sensor and Actor Network Protocols and Applications (SANPA

2004), 2004.

[10] L. Qing, Q. Zhu, M. Wang, “Design of a distributed energy-efficient clustering

algorithm for heterogeneous wireless sensor network”, ELSEVIER, Computer

Communications 29, 2006, pp 2230- 2237.

[11] Elbhiri, B. , Saadane, R. , El Fkihi, S. , Aboutajdine, D. “Developed Dis-

tributed Energy-Efficient Clustering (DDEEC) for heterogeneous wireless sen-

sor networks”, in: 5th International Symposium on I/V Communications and

Mobile Network (ISVC), 2010.

[12] Parul Saini, Ajay.K.Sharma, “E-DEEC- Enhanced Distributed Energy Effi-

cient Clustering Scheme for heterogeneous WSN”, in: 2010 1st International

Conference on Parallel, Distributed and Grid Computing (PDGC - 2010).

[13] Parul Saini, Ajay.K.Sharma, “Energy Efficient Scheme for Clustering Pro-

tocol Prolonging the Lifetime of Heterogeneous Wireless Sensor Networks”,

International Journal of Computer Applications (0975 8887), Volume 6 No.2,

September 2010.

[14] Qureshi. T. N, Javaid. N, Malik. M, Qasim. U, Khan. Z. A, On Perfor-

mance Evaluation of Variants of DEEC in WSNs, 7th International Confer-

ence on Broadband and Wireless Computing, Communication and Applica-

tions (BWCCA-2012), Victoria, Canada, 2012.

[15] Zhengjie Wang, Xiaoguang Zhao, Xu Qian, “The Application and Issuse of

Linear Wireless Sensor Networks”, International Conference on System Sci-

ence, Engineering Design and Manufacturing Informatization (ICSEM), Vol-

ume 2, Oct. 2011.

[16] Sudarmani.R, Dr. K.R. Shankar Kumar, “Analysis of Energy Consumption

in Heterogeneous Sensor Networks with Mobile sink”, TENCON 2011, IEEE

Region 10 Conference, Nov. 2011.

[17] Yan Zhao, Qianping Wang, Dong Jiang, Wanrong Wu, Li Hao, Ke Wang, “An

Agent-based Routing Protocol with Mobile Sink for WSN in Coal Mine”, Third

International Conference on Pervasive Computing and Applications (ICPCA),

Volume-2, Oct. 2008.

[18] Yamaimaiti Nizhamudong, Naoshi Nakaya, Yukari Hagihara, Yuji Koui, “Per-

formance Evaluation of Route Cost for Wireless Sensor Networks with a Mobile

Sink”, SICE Annual Conference 2011, Sept. 2011.

40

[19] Yasir Faheem, Saadi Boudjit, Ken Chen, “Dynamic Sink Location Update

Scope Control Mechanism for Mobile Sink Wireless Sensor Networks”, Eighth

International Conference on Wireless On-Demand Network Systems and Ser-

vices (WONS), France, 2011.

[20] Babar Nazir, Halabi Hasbullah, “Mobile Sink based Routing Protocol

(MSRP) for Prolonging Network Lifetime in Clustered Wireless Sensor Net-

work”, International Conference on Computer Applications and Industrial

Electronics (ICCAIE 2010), Kuala Lumpur, Malaysia, December 5-7, 2010.

[21] Ma Chaw Mon Thein, Thandar Thein, “An Energy Efficient Cluster-Head Se-

lection for Wireless Sensor Networks”, International Conference on Intelligent

Systems, Modelling and Simulation, (ISMS), 2010.

[22] YoungSang Yun, Ye Xia, “Maximizing the Lifetime of Wireless Sensor Net-

works with Mobile Sink in Delay-Tolerant Applications”. IEEE TRANSAC-

TIONS ON MOBILE COMPUTING, VOL. 9, NO. 9, SEPTEMBER 2010.

[23] Tauseef Shah, Nadeem Javaid, Talha Naeem Qureshi, “Energy Efficient Sleep

Awake Aware (EESAA) Intelligent Sensor Network Routing Protocol”, 15th

IEEE International Multi Topic Conference (INMIC’12), 2012, Pakistan.

[24] Aasia Kashaf, Nadeem Javaid, Zahoor Ali Khan and Imran Ali Khan, “TSEP:

Threshold-sensitive Stable Election Protocol for WSNs”, 10th IEEE Interna-

tional Conference on Frontiers of Information Technology (FIT 12), 2012, Pak-

istan.

[25] Khan. A. A, Javaid. N, Qasim. U, Lu. Z, Khan. Z. A, “HSEP: Heterogeneity-

aware Hierarchical Stable Election Protocol for WSNs”, 3rd International

Workshop on Advances in Sensor Technologies, Systems and Applica-

tions (ASTSA-2012) in conjunction with 7th IEEE International Confer-

ence on Broadband and ireless Computing, Communication and Applications

(BWCCA 2012), Victoria, Canada, 2012.

[26] Aslam. M, Javaid. N, Rahim. A, Nazir. U, Bibi. A, Khan. Z. A, “Survey

of Extended LEACH-Based Clustering Routing Protocols for Wireless Sensor

Networks”, 5th International Symposium on Advances of High Performance

Computing and Networking (AHPCN-2012)in conjunction with 14th IEEE In-

ternational Conference on High Performance Computing and Communications

(HPCC-2012), 25-27 June, Liverpool, UK, 2012.

[27] Aslam. M, Shah. T, Javaid. N, Rahim. A, Rahman. Z, Khan. Z. A, “CEEC:

Centralized Energy Efficient Clustering A New Routing Protocol for WSNs”,

Poster Session of 9th Annual IEEE Communications Society Conference on

41

Sensor, Mesh and Ad Hoc Communications and Networks (SECON2012),

Seoul, Korea, 2012.

[28] 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 Mo-

bile Networks (NGWMN 2012) in conjunction with 7th IEEE International

Conference on Broadband and Wireless Computing, Communication and Ap-

plications (BWCCA 2012), Victoria, Canada, 2012.

[29] M. Aslam, N. Javaid, A. Rahim, U. Nazir, A. Bibi, Z. A. Khan ”Survey

of Extended LEACH-Based Clustering Routing Protocols for Wireless Sensor

Networks”, 5th International Symposium on Advances of High Performance

Computing and Networking (AHPCN-2012)in conjunction with 14th IEEE In-

ternational Conference on High Performance Computing and Communications

(HPCC-2012), 25-27 June, Liverpool, UK, 2012.

[30] M. Younis, M. Youssef, K. Arisha, “Energy-aware management in cluster-

based sensor networks”, Computer Networks 43 (5) (2003), 649668.

42

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