ThesisPDF Available

Analyzing Sink Mobility in DEEC and its Variants

Authors:

Abstract and Figures

Wireless Sensor Networks (WSNs) contain a huge number of sensor nodes having bounded power energy, which transmit their sensed information to the Base Station (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 protocols [23-29] have been proposed in this regard achieving energy efficiency in heterogeneous scenarios. However, every protocol is not suitable for heterogeneous WSNs. Efficiency of protocol degrades while changing the heterogeneity parameters. 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 heterogeneity parameters of the network. Then, I have taken four protocols into consideration that are Distributed EnergyEfficient Clustering (DEEC), Developed DEEC (DDEEC), Enhanced DEEC (EDEEC) and Threshold DEEC (TDEEC) for comparing their performances in different scenarios 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.
Content may be subject to copyright.
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
Analyzing Sink Mobility in
DEEC and its Variants
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
Analyzing Sink Mobility in
DEEC and its Variants
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
LEACH
MEMS
TEEN
SEP
DEEC
CH
BS
ATPC
TSP
DDEEC
TDEEC
EDEEC
PEGASIS
HEED
Table of Contents
1 Abstract 1
2 Introduction 2
3 Background and motivation for thesis 5
3.1 RelatedWork .............................. 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 SinkMobility .............................. 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 PacketstotheBS............................ 17
4.5 Nodes dead during rounds . . . . . . . . . . . . . . . . . . . . . . . 18
4.6 Nodes alive during rounds . . . . . . . . . . . . . . . . . . . . . . . 19
4.7 PacketstotheBS............................ 19
4.8 Nodes dead during rounds . . . . . . . . . . . . . . . . . . . . . . . 20
4.9 Nodes alive during rounds . . . . . . . . . . . . . . . . . . . . . . . 20
4.10PacketstotheBS............................ 21
4.11 Nodes dead during rounds . . . . . . . . . . . . . . . . . . . . . . . 21
4.12 Nodes alive during rounds . . . . . . . . . . . . . . . . . . . . . . . 22
4.13PacketstotheBS............................ 22
4.14 Nodes dead during rounds . . . . . . . . . . . . . . . . . . . . . . . 23
4.15 Nodes alive during rounds . . . . . . . . . . . . . . . . . . . . . . . 23
4.16PacketstotheBS............................ 24
4.17 Nodes dead during rounds . . . . . . . . . . . . . . . . . . . . . . . 24
4.18 Nodes alive during rounds . . . . . . . . . . . . . . . . . . . . . . . 25
4.19PacketstotheBS............................ 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,
EDEECandTDEEC.......................... 31
5.5 Sink moving linearly at the center of the network . . . . . . . . . . 31
5.6 Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC and
TDEEC with linear sink mobility . . . . . . . . . . . . . . . . . . . 32
xii
5.7 Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC and
TDEEC with linear sink mobility . . . . . . . . . . . . . . . . . . . 33
5.8 Packets sent to the base station during rounds for DEEC, DDEEC,
EDEEC and TDEEC with linear sink mobility . . . . . . . . . . . . 33
5.9 Sink moving in zigzag pattern across the network . . . . . . . . . . 34
5.10 Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC and
TDEEC with sink mobility in zigzag pattern . . . . . . . . . . . . . 34
5.11 Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC and
TDEEC with sink mobility in zigzag pattern . . . . . . . . . . . . . 35
5.12 Packets sent to the base station during rounds for DEEC, DDEEC,
EDEEC and TDEEC with sink mobility in zigzag pattern . . . . . 35
xiii
List of Tables
4.1 Valueofparameters........................... 15
5.1 Valueofparameters........................... 28
5.2 Values of network protocols with n=200 (scenario 1) . . . . . . . . . 36
5.3 Values of network protocols with n=200 (scenario 2) . . . . . . . . . 36
5.4 Values of network protocols with n=200 (scenario 3) . . . . . . . . . 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×100mnetwork.
Then the shape of the network is changed into a tunnel having dimensions of
100m×20mwhere 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, Nnumber 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, E0is the energy level of normal node and E0(1 + a) is
the energy level of advanced nodes containing atimes more energy as compared
to normal nodes. If Nis the total number of nodes then N m is the number of
advanced nodes where mrefers 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 atimes extra energy than normal nodes
equal to E0(1 + a) whereas, super nodes of fraction m0are having a factor of b
times more energy than normal nodes so their energy is equal to E0(1 + b). As
Nis the total number of nodes in the network, then Nmm0is total number of
super nodes and N m(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+N m0E0(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
Electronics Tx 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:
ET x(l, d) =
lEelec +f s d2, d < d0
lEelec +mp d4, d d0
(4.4)
Where, Eelec is the electronics energy of the transmitter or receiver circuit. dis 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(2N Eelec +N EDA +mpd4
toBS +N εfsd2
toCH ) (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
2πεfs
εmp
M
d2
toBS
(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 nidenote the number
of rounds to be a CH for node si.popt Nis 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 poptNCHs 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.piis the probability for each node sito become CH, so the node with
high energy has larger value of pias compared to the popt.¯
E(r) denotes average
energy of network during round rwhich 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
popt
Ei(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
1pi(rmod 1
Pi)if siϵG
0otherwise
(4.11)
Where Gis 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 siis the normal node
popt(1+a)Ei(r)
(1+am)¯
E(r)if siis 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 pifor heterogeneous
node. pifor 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 ris 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 rhave 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:
T hREV =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)T h is
given as in reference [11] and given below:
T hREV (7/10)E0(4.19)
Average probability pifor CH selection used in DDEEC is as follows as in reference
[11]:
pi=
poptEi(r)
(1+am)¯
E(r)for N ml nodes, Ei(r)> T hREV
(1+a)poptEi(r)
(1+am)¯
E(r)for Adv nodes, Ei(r)> T hREV
c(1+a)poptEi(r)
(1+am)˜¯
E(r)for Adv, N ml nodes, Ei(r)T hREV
(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. piis 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 piin
EDEEC is as follows as in reference [12]:
pi=
poptEi(r)
(1+m(a+m0b)) ¯
E(r)if siis the normal node
popt(1+a)Ei(r)
(1+m(a+m0b)) ¯
E(r)if siis the advanced node
popt(1+b)Ei(r)
(1+m(a+m0b)) ¯
E(r)if siis the super node
(4.21)
Threshold for CH selection for all three types of node is as follows as in reference
[12]:
T(si) =
pi
1pi(rmod 1
pi)
ifpiϵG
pi
1pi(rmod 1
pi)ifpiϵG′′
pi
1pi(rmod 1
pi)ifpiϵG′′′
0otherwise
(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
1p(rmod 1
p)residual energy of a node kopt
average 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 Values
Network field 100 m,100 m
Number of nodes 100
E0(initial energy of normal nodes) 0.5J
Message size 4000 bits
Eelec 50nJ/bit
Efs 10nJ/bit/m2
Eamp 0.0013pJ/bit/m4
EDA 5nJ/bit/signal
d0(threshold distance) 70m
Popt 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,aand 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 mrefers to fraction of advanced nodes
containing extra amount of energy ain network whereas, m0is a factor that refers
to fraction of super nodes containing extra amount of energy bin 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.
Figure 4.5: Nodes dead during rounds
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
Figure 4.6: Nodes alive during rounds
Figure 4.7: Packets to the BS
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
Figure 4.8: Nodes dead during rounds
Figure 4.9: Nodes alive during rounds
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
Figure 4.10: Packets to the BS
Figure 4.11: Nodes dead during rounds
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
Figure 4.12: Nodes alive during rounds
Figure 4.13: Packets to the BS
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
Figure 4.14: Nodes dead during rounds
Figure 4.15: Nodes alive during rounds
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
Figure 4.16: Packets to the BS
in Fig. 4.14, 4.15 and 4.16.
Figure 4.17: Nodes dead during rounds
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
Figure 4.18: Nodes alive during rounds
Figure 4.19: Packets to the BS
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, m0are
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,
m0are 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×100mfield
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 mfraction
of advanced nodes having αtimes 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 Values
Number of nodes 100
E0(initial energy of normal nodes) 0.5J
Message size 4000 bits
Eelec 50nJ/bit
Efs 10nJ/bit/m2
Eamp 0.0013pJ/bit/m4
EDA 5nJ/bit/signal
d0(threshold distance) 70m
Popt 0.1
Where, Eelec is transmitter/receiver electronics energy. EDA is the data aggrega-
tion energy expended in the cluster-heads. εf s 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 100mto 20mas 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 10000
0
10
20
30
40
50
60
70
80
90
100
no of rounds
no of dead nodes
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.2: Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC and
TDEEC
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
0
10
20
30
40
50
60
70
80
90
100
No of rounds
No of alive nodes
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.3: Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC and
TDEEC
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 10000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5x 105
no of rounds
packets to base station
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.4: Packets sent to the base station during rounds for DEEC, DDEEC, EDEEC
and 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 10000
0
10
20
30
40
50
60
70
80
90
100
no of rounds
no of dead nodes
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.6: Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC and
TDEEC 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 10000
0
10
20
30
40
50
60
70
80
90
100
no of rounds
no of alive nodes
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.7: Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC and
TDEEC with linear sink mobility
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5 x 105
no of rounds
packets to base station
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.8: Packets sent to the base station during rounds for DEEC, DDEEC, EDEEC
and 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 10000
0
10
20
30
40
50
60
70
80
90
100
no of rounds
number of dead nodes
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.10: Rate of nodes dead during rounds for DEEC, DDEEC, EDEEC and
TDEEC with sink mobility in zigzag pattern
34
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
0
10
20
30
40
50
60
70
80
90
100
no of rounds
number of alive nodes
DEEC
DDEEC
EDEEC
TDEEC
Figure 5.11: Rate of nodes alive during rounds for DEEC, DDEEC, EDEEC and
TDEEC with sink mobility in zigzag pattern
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5 x 105
no of rounds
packets to the base station
DEEC
DDEEC
EDEEC
TDEEC
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 Throughput
DEEC 1210 2730 79765
DDEEC 987 2831 84597
EDEEC 1259 9865 657402
TDEEC 1247 9815 918492
Table 5.3: Values of network protocols with n=200 (scenario 2)
Protocols First node dead All nodes dead Throughput
DEEC 1357 2997 80446
DDEEC 1116 2926 84109
EDEEC 1209 8511 650709
TDEEC 1409 8578 903399
Table 5.4: Values of network protocols with n=200 (scenario 3)
Protocols First node dead All nodes dead Throughput
DEEC 1297 2999 65408
DDEEC 1017 2930 67919
EDEEC 1209 8522 632199
TDEEC 1393 8543 893253
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
ResearchGate has not been able to resolve any citations for this publication.
Conference Paper
Full-text available
Typically, a wireless sensor network contains an important number of inexpensive power constrained sensors, which collect data from the environment and transmit them towards the base station in a cooperative way. Saving energy and therefore, extending the wireless sensor networks lifetime, imposes a great challenge. Clustering techniques are largely used for these purposes. In this paper, we propose and evaluate a clustering technique called a Developed Distributed Energy-Efficient Clustering scheme for heterogeneous wireless sensor networks. This technique is based on changing dynamically and with more efficiency the cluster head election probability. Simulation results show that our protocol performs better than the Stable Election Protocol (SEP) by about 30% and than the Distributed Energy-Efficient Clustering (DEEC) by about 15% in terms of network lifetime and first node dies.
Conference Paper
Several recent works propose mobile sink based data gathering approaches to distribute energy consumption in Wireless Sensor Networks. However, mobility based algorithms require the sink to periodically update its location to the network to ensure multi-hop connectivity. This additional information increases the control traffic overhead, and thus energy consumption also increases. In this paper, we propose a distributive energy efficient sink location update mechanism for mobile sink WSNs, along with a preemptive buffering data collection technique. This sink location update mechanism combines MPR based broadcast algorithm with a distributed local repair mechanism, whereby, sensor nodes dynamically control the scope of sink location update messages. In preemptive buffering mechanism, sink's neighbor nodes act as temporary data collection points, referred as roots, to prevent data loss when sink moves out of their vicinity. We show by simulations that our proposal effectively reduces the energy consumption and data losses of the network.
Conference Paper
Nowadays the Wireless Sensor Network (WNS) technology has been developing remarkably, and it's expected to be applied to many applications such as plant management, crime prevention, and disaster prevention, and medical treatment, traffic system, so on. In this article, we propose a Mobile Sink node control method for Wireless Sensor Networks. These Networks are composed from two types of sensor node, one is “fixed node” which is immovable and the other one is “mobile sink node” which is movable. The fixed nodes will form the clusters, and then the Mobile Sink node by using the Nearest Addition Method of TSP (Traveling Salesman Problem) which moves around the cluster center, 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. Moreover the mobile Sink node is also capable to sense the possibility of communication of more than few clusters in between, from then it would identify the best path of collecting the data. This method has proved it efficiency by the result of simulation and experiment that we have conducted and it would evaluate the performance of route cost for a mobile sink node.
Conference Paper
Wireless sensor networks consist of a large number of sensor nodes that have low power and limited transmission range and can be used in various scenario. The nodes can be deployed in the long and narrow region, such as road, bridge, tunnel and pipeline, to get some interesting information. The linear topology of these network application is different other application and have special feature, such as multi-hop, long delay, long distance and low reliability. This paper introduces the concept of linear wireless sensor networks and discusses the classification of topology and key issue of this network. The application of the linear wireless sensor network, such as road, bridge, tunnel and pipeline is presented. The research challenges are discussed at last in this paper.
Conference Paper
Wireless sensor networks are energy constraint battery powered sensing, computing and communication infrastructure. Sensor nodes are randomly deployed and organized as clusters, and each node is responsible for transmitting the data to its cluster head. Most of the existing sensor networks focus on homogeneous in which the cluster heads are changed periodically. In order to reduce power consumption of Sensor Networks, we have investigated Load Balanced Heterogeneous Sensor networks with Adaptive Transmission Power Control (ATPC), and mobile sink. The simulation results show that the consumed energy is less in the case of ATPC and Mobile Sink (MS) when compared to stationary sink.
Conference Paper
Wireless sensor network is applied widely in monitoring system of coal mine. An application environment of WSN in coal mine is analyzed to satisfy the data acquirement of mine monitoring. The result of analysis shows that routing protocols applied in coal mine should have four characteristics: data-centric routing mechanism, supporting query of geographic position information, low delay and high reliability of data transmission, solid robustness of topology. An agent-based routing protocol with mobile sink is proposed in this paper according to above-mentioned features. The routing protocol improve the performance of high delay and low reliability in traditional coal mine monitoring system by applying agent nodes, mobile sink and grid topology.
Conference Paper
In WSN, sensors near the sink have to relay the data of the nodes away from the sink and as a result they drain their energy very quickly. It result in network partitioning and can significantly limit the network lifetime. This problem is termed as hotspot problem. Recently, formation of hot spot or energy hole near the sink has emerged as a critical issue for data gathering in WSN. In this paper, we address hotspot problem and purposed Mobile Sink based Routing Protocol (MSRP) for Prolonging Network Lifetime in Clustered Wireless Sensor Network. In MSRP, mobile sink moves in the clustered WSN to collect sensed data from the CHs within its vicinity. During data gathering mobile sink also maintains information about the residual energy of the CHs. Mobile sink based on the residual energy of CHs move to the CHs having higher energy. Consequently, the hotspot problem is minimized as the immediate neighbor of the sink is high energy node and it changes because of regular sink movement. It results in a balanced use of WSN energy and improves network life time. To evaluate the performance of the proposed strategy intensive simulation are carried out using OMNet-4.0. Performance of the proposed strategy is compared to the static sink and multiple sinks strategies, using metrics such as energy per packet, and throughput. The simulation results demonstrate that MSRP is effective in prolonging the network lifetime as well as in improving throughput than static sink and multiple sink strategies.
Conference Paper
Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
Article
The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.
Article
Networking unattended sensors is expected to have a significant impact on the efficiency of many military and civil applications. Sensors in such systems are typically disposable and expected to last until their energy drains. Therefore, energy is a very scarce resource for such sensor systems and has to be managed wisely in order to extend the life of the sensors for the duration of a particular mission. In this paper, we present a novel approach for energy-aware management of sensor networks that maximizes the lifetime of the sensors while achieving acceptable performance for sensed data delivery. The approach is to dynamically set routes and arbitrate medium access in order to minimize energy consumption and maximize sensor life. The approach calls for network clustering and assigns a less-energy-constrained gateway node that acts as a cluster manager. Based on energy usage at every sensor node and changes in the mission and the environment, the gateway sets routes for sensor data, monitors latency throughout the cluster, and arbitrates medium access among sensors. We also describe a time-based medium access control (MAC) protocol and discuss algorithms for assigning time slots for the communicating sensor nodes. Simulation results show an order of magnitude enhancement in the time to network partitioning, 11% enhancement in network lifetime predictability, and 14% enhancement in average energy consumed per packet.