i

HSEP and TRP Two New Routing Protocol in WSNs

By Mr. Awais Adil Khan

Registration Number: CIIT/FA11-REE-028/ISB MS Thesis

In Electrical Engineering

COMSATS Institute of Information Technology Islamabad – Pakistan

FALL, 2012

ii

HSEP and TRP Two New Routing Protocol in WSNs

A Thesis presented to COMSATS Institute of Information Technology

In partial fulfillment of the requirement for the degree of

MS (Electrical Engineering)

By

Mr. Awais Adil Khan

CIIT/FA11-REE-028/ISB

Fall, 2012

iii

HSEP and TRP Two New Routing Protocol in WSNs

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 Mr. Awais Adil Khan CIIT/FA11-REE-028/ISB

Supervisor: Dr. Nadeem Javaid, Assistant Professor,

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

Islamabad Campus, December, 2012

iv

Final Approval

This thesis titled

HSEP and TRP Two New Routing Protocol in WSNs

By Mr. Awais Adil Khan

CIIT/FA11-REE-028/ISB

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

External Examiner: __________________________________ (To be decided)

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

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

v

Declaration

I Mr. Awais Adil Khan, CIIT/FA11-REE-028/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: ________________ Mr.Awais Adil Khan ________________ CIIT/FA11-REE-028/ISB

vi

Certificate

It is certified that Mr. Awais Adil Khan, CIIT/FA11-REE-028/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: _________________ Supervisor:____________________ Dr. Nadeem Javaid /Assistant professor, Center for Advanced Studies in Telecommunications (CAST), CIIT, Islamabad.

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

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DEDICATION

Dedicated to my parents.

viii

ACKNOWLEDGMENT I am heartily grateful to my 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.

Mr. Awais Adil Khan CIIT/FA11-REE-028/ISB

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List of Abbreviations

WSNs Wireless Sensor Networks CHs Cluster heads BS Base Station SEP Stable Election Protocol HSEP Hierarchal Stable Election Protocol DT Direct Transmission MTE Minimum Transmission Energy LEACH Low-Energy Adaptive Clustering Hierarchy RSSI Received Signal Strength Indicator ESEP Enhanced Stable Election Protocol DEEC Distributed Energy Efficient clustering

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List of Publications

[1]A. A Khan, N. Javaid, U. Qasim, Z. Lu, Z. A. Khan, “Hierarchal Stable Election Protocol for WSNs”, published in 2012 3rd International Workshop on Advances in Sensor Technologies, Systems and Applications (ASTSA-2012) in conjunction with 7th IEEE International Conference on Broadband and Wireless Computing.

[2]A. A Khan, A. Maraim, N. Javaid, “Tunneled Routing Protocol for WSNs”, submitted in, 10th IEEE International Conference on Wireless On-demand Network Systems and Services (WONS'13), March 18-20, 2013, Banff, Canada.

[3] A. A Khan, A. Maraim, N. Javaid, “Squared Routing Protocol for WSNs”, submitted in 4th IEEE International Conference on Ambient Systems, Networks and Technologies (ANT-13) June 25-28, 2013, Halifax, Nova Scotia, Canada.

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ABSTRACT

Wireless Sensor Networks (WSNs) are increasing to handle complex situations and functions. In these networks some of the nodes become Cluster Heads (CHs), aggregate data of cluster members and transmit it to Base Stations (BS). However, homogeneous networks are not enough efficient in consuming energy. Stable Election Protocol (SEP) introduces heterogeneity in WSNs, consisting of two type of nodes. SEP is based on weighted election probabilities of each node to become CH according to remaining energy of nodes. We propose Heterogeneity-aware Hierarchal Stable Election Protocol (HSEP) having two levels of energies. Simulation results show that HSEP prolongs stability period and network life time when compared to conventional routing protocols and having higher average throughput than current clustering protocols in WSNs.

Energy conservation is one of the most important factors in WSNs for network reliability since nodes have limited resource of energy. We need to design such routing protocols, which efficiently use available energy and prolong network life time and stability period. We implement sink mobility in Cluster Less Stable Election Protocol (CL-SEP) and propose Tunnel Routing Protocol (TRP) for WSNs, which are two levels heterogeneous. From our simulation results we can see that our proposed TRP out performs conventional SEP in stability period, network life time and throughput. TRP efficiently utilizes available energy of the network by using Moving Sink (MS) and prolong network life time and stability period of the network.

Sink Mobility (SM) is getting popular due to excellent load balancing between nodes and ultimately resulting in prolonged network lifetime and throughput. A major challenge is to provide reliable and energy-efficient operation taking into consideration different mobility patterns. Aim of the paper is lifetime maximization of delay tolerant WSN through the manipulation of SM on different trajectories. We jointly optimize the problem by designing a routing protocol for routing of the sensed data in the network and patterns for SM. We proposed Square Routing Protocol (SRP) based on existing SEP (Stable Election Protocol), by making it Cluster Less (CL) and introducing SM.

Key Words: Energy consumption, Heterogeneous environment, Cluster heads, Mobile sink, Trajectories, Mobility pattern.

Table of Contents

1 Introduction 1

2 Related Work 4

3 HSEP: Heterogeneity-aware Hierarchical Stable Election Pro-

tocol for WSNs 9

3.1 Existing Routing Protocols For WSNs . . . . . . . . . . . . . . . . 9

3.1.1 LEACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1.2 SEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1.3 ESEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.4 DEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 HSEP: The Proposed Protocol . . . . . . . . . . . . . . . . . . . . . 13

3.2.1 Comparison of LEACH, SEP, ESEP, DEEC and HSEP . . . 15

4 TRP: An Energy Efficient Approach Incorporating Sink Mobil-

ity in WSNs 22

4.1 Sink Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.2 Network Model and Description . . . . . . . . . . . . . . . . . . . . 23

4.2.1 Heterogeneous Network Model . . . . . . . . . . . . . . . . . 23

4.3 Our Proposed TRP . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.3.0.1 MS along border of field (B-TRP): . . . . . . . . . 25

4.3.0.2 MS along diagonal line of field (D-TRP): . . . . . . 26

4.3.0.3 MS along horizontal line passing through center of

field (C-TRP): . . . . . . . . . . . . . . . . . . . . 26

4.3.0.4 MS in spiral Trajectory of field (S-TRP): . . . . . . 28

4.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5 SRP:An Energy Efficient Approach Incorporating Sink Mobil-

ity in WSNs 31

5.1 Our Proposed Squared Routing Protocol (SRP) . . . . . . . . . . . 31

5.2 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . 32

xii

6 Conclusion 37

xiii

List of Figures

3.1 Network Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 Comparison of HSEP with LEACH, SEP and ESEP with α = 1m =

0.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.3 Comparison of HSEP with LEACH, SEP and ESEP with α = 1m =

0.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.4 Comparison of HSEP with LEACH, SEP and ESEP at α = 1m =

0.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.5 Comparison of HSEP with LEACH, SEP and ESEP at α = 3m =

0.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.6 Comparison of HSEP with LEACH, SEP and ESEP α = 3m = 0.1 . 20

3.7 Comparison of HSEP with LEACH, SEP and ESEP α = 3m = 0.1 . 21

4.1 MS along border of field . . . . . . . . . . . . . . . . . . . . . . . . 25

4.2 MS along diagonal line of field . . . . . . . . . . . . . . . . . . . . . 26

4.3 MS along horizontal line passing through center of field . . . . . . . 27

4.4 MS for S-TRP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.5 Alive Nodes with α = 1m = 0.1 . . . . . . . . . . . . . . . . . . . . 29

4.6 Throughput of TRP with α = 1, m = 0.1 . . . . . . . . . . . . . . . 30

5.1 Alive nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.2 Circular sink mobility in circular field . . . . . . . . . . . . . . . . . 33

5.3 Squared sink mobility in squared field . . . . . . . . . . . . . . . . . 33

5.4 Circular sink mobility in squared field . . . . . . . . . . . . . . . . . 33

5.5 Flow Chart of SRP . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.6 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

xiv

Chapter 1

Introduction

WSNs are being continuously used in new applications in various areas, like, re-

mote and drastic areas. Some WSNs applications are present in health sectors in

which sensors are implanted on human body for diagnosis diseases. To maintain

reliable information delivery, WSNs require some efficient routing and MAC pro-

tocols. Two necessary tasks for reliable communication among nodes area Data

Aggregation and Information combination which are carried-out by CH with in a

cluster. Only effective information is forwarded to BS to decrease communication

energy and prolong network life-time with precise data delivery. WSNs serves

different applications in variety of fields such as military operations, medical mon-

itoring and environmental monitoring. Different bridges, tunnels, underpasses and

flyovers are also being monitored by WSNs now a days. As the nodes in WSNs

have limited amount of energy and have power constrained due to this limited

energy. It is very difficult to replace or recharge these sensor nodes because they

can not be accessed once they are deployed in the field. Direct communication

of sensor nodes to BS consumes more energy so it is not feasible to use direct

communication due to limited energy of the sensor nodes.

Clustering is major technique to minimize this energy dissipation while trans-

mission there is one CH and associated member nodes in one cluster. Different

clustering protocols are proposed in [1-3]. Associated node of a cluster transmit

their data to CH and CH send the aggregated data to the BS. In aggregation

lesser packets are sent to BS and only few nodes just perform high transmissions

hence maximum part of energy is saved and network life of network is enhanced.

Clustering technique can be used in two different types of networks heterogeneous

and homogeneous networks. In homogeneous networks all the nodes have same

energy while in heterogeneous networks there is some fraction of advance nodes.

SEP [1] is designed for heterogeneous network model, whereas, SEP does not sup-

1

port multilevel heterogeneity. There are two types of node advance and normal

nodes, advance nodes have α times more energy than normal nodes and become

CH more than normal node in the network.

In Direct Transmission (DT) BS receives data directly from sensor nodes so, nodes

die earlier which are far away from BS because they consume more energy in

transmitting data to BS due to large distance between nodes and BS. Another

technique in which data is transmitted over minimum cost paths where minimum

transmission energy consumed is Minimum Transmission Energy (MTE), Using

MTE, nodes near to BS act as relay having higher probability die first than other

nodes which are far from BS. Another routing protocol discussed by authors is

Low-Energy Adaptive Clustering Hierarchy (LEACH) which is used by homoge-

neous networks having same type of nodes. However, it uses clustering technique

which is not used by DT or MTE. CHs are elected probabilistically in LEACH

where each node becomes CH according to a random number compared with de-

fined threshold.

We propose HSEP, which reduces transmission cost from CH to BS. The election

probability of node to become CH is based on original energy of node in HSEP.

It increases stability period (before death of first node) for those applications in

which dependable feedback from sensor is compulsory. However, HSEP minimizes

transmission energy by choosing secondary CHs from existing primary CHs in

each round and these secondary CH are elected by the probability, Ph.

WSNs are based on great number of tiny sensor nodes which have limited energy.

Different applications of WSNs monitor different conditions of particular area.

Sensors usually deployed to specific area and sense data such as pressure, humidity,

temperature and motion. Sensor nodes transmit data to BS, depending on nature

of application. Objective of WSNs is to prolong life time of every member node

and thus, network lifetime. In comparison with other wireless communication

networks i.e. Mobile Ad-hoc Network (MANET) and cellular networks, WSNs

have some unique characteristics and constrains.

Battery operated sensors

Sensor nodes are mostly deployed in those areas where their batteries can not be

change so, sensor nodes are battery operated devices.

Self-configurable

Sensor nodes are organized themselves automatically and deployed in random

manner.

Dense sensor node deployment

2

The numbers of nodes are higher in WSN than MANET and densely deployed.

Energy constraints

Sensors nodes have limited energy, computation, and storage capabilities. Re-

cently networks using sink mobility for data gathering are getting popular. Our

aim is to design more effective data gathering method by using MS as compare to

conventional routing schemes.

As sinks are Proficient machines which are provided with sufficient energy/ fuel

(able to refill). Sensor which are deployed in the field act as sources, as they are

gather data and provided the needed information. They send their sensed data

to the sink for further processing. The data which is transmitted to the sink is

send either in pull model or push model. Sensors send their data actively to the

sink in push model and in pull it will send data only on the sinks request. As the

sink is sometimes out of the range of many sensors and data sending takes much

energy. If transmission is multi hop, the nodes which are located near sink will

deplete their batteries first as they will act as rely for the far most sensors. An

other draw back is if every nodes is in active mode then near the sink bottle neck

will create.

To avoid it we are introducing Sink Mobility (MS) is our proposed scheme. Cana-

dian Traveller Problem (CTP) the variants are use to find the shortest path be-

tween sensors and sink. When sink is moving on the predefined trajectory the

sensor nodes in the In this paper, we work on existing routing protocol Stable

Election Protocol(SEP) [1] and introduced a mobile sink in the field. Study is

based on data collection in WSNs by considering sink mobility and as well as

routing protocol.

In this thesis, I introduce MS schemes in CL-SEP. Here, implemented four different

mobility patterns in the field to efficiently gather data, in my proposed protocol

named as TRP. This thesis is organized as follows: In chapter 2, motivation and

related research works including MS is discussed. Chapter 3 details our proposed

protocols HSEP for efficiently data gathering with MS in WSN environments with

simulation results. TRP is discussed with its simulation results in chapter 4 net-

work model is also descussed in this chapter. Chapter 5 details our proposed

technique Squared Routing Protocol (SRP) for reliable data gathering with MS

in WSNs. Finally, concluding remarks are presented in chapter 6.

3

Chapter 2

Related Work

Authors discuss clustering technique to evenly balance load between nodes and

also discussed node heterogeneity in terms of their energy in [1]. It uses two kind

of nodes: advance nodes and normal nodes. Advance nodes become cluster head

more that than normal nodes to prolong network life and stability period. How-

ever we discuss hierarchal clustering technique to minimize transmission distance

among CHs and BS which is not discussed by authors.

In [2], clustering base routing protocol for WSNs is described by authors. This uses

random rotation of CH to evenly distribute energy load among sensor nodes to

enhance stability period and network life time. Authors used homogeneous routing

protocol having same type of nodes. However, authors do not discuss hierarchal

clustering with node heterogeneity in terms of energy to enhance network life and

stability period.

WSNs are handling more difficult functions in daily life. We desire that WSNs

sense data by consuming minimum energy to prolong network life and stability

period. Authors in [3], use clustering base routing protocol with three level of

node heterogeneity in terms of energy to prolong network life time and stability

period. However, we discuss hierarchal clustering technique which reduces distance

between CHs and BS and prolongs stability period and network life, which is not

discussed by authors.

Energy-efficient clustering protocols are designed by heterogeneous WSNs to max-

imize network lifetime and stability period. Authors use clustering technique to

reduce energy consumption in [4], CHs are elected probabilistically on the basis of

ratio between remaining energy of node and average energy of the network. How-

ever, we introduce hierarchal clustering technique to reduce energy consumption

between CH and BS by data transmission.

4

Routing is use to provide communication among sink and sensor nodes in WSNs.

[2] presents an efficient hierarchical clustering scheme for sensor networks known

as Low Energy Adaptive Clustering Hierarchy (LEACH). It is clustering based

routing protocol having distributed cluster formation process. In LEACH, CH

election is random, and it rotates this clustering process, to efficiently distribute

energy among sensor nodes. However, authors used clustering to evenly balance

load between sensor nodes in [1], every node in heterogeneous hierarchal network

selects itself to be a CH on bases of its initial energy compare to other nodes.

Authors in [3], used clustering with three level of node heterogeneity to enhance

stability period and network lifetime. Authors have discussed hierarchal clustering

technique with in heterogeneous network in [5]. Whereas, clustering technique is

used by authors to reduce energy consumption in [4], CHs are elected probabilis-

tically on the basis of ratio between remaining energy of each node and over all

average energy of network. However, all above mentioned papers they have not

discussed sink mobility and tunneled network for network life time maximization.

Chain formation for data exchange is used in [6]. Over all information of network

is required which make this algorithm hard to apply.

Statistical process of choosing CH with distributed clustering algorithm is dis-

cussed in HEED [7]. CH selection depending on remaining energy of node is

discussed in [8]. In initial rounds advance nodes are selected as CHs but after

some time their energy becomes equal to normal nodes and have same CH elec-

tion process like normal nodes. Three level of node heterogeneity is also discussed

in [9] and have named third type of nodes as super nodes. Proactive and reactive

protocols is presented in [10] they used threshold technique in their idea. LEACH-

SM protocol minimizes the energy consumption is discussed in [11]. This improves

LEACH by efficiently managing of spares and minimizing energy consumption.

The extension of Teen is presented by [12] known as A Hybrid Protocol for Efffi-

cient Routing and Comprehensive Information Retrieval in Wireless Sensor Net-

works (APTEEN). This protocol is used for both periodic data sending and time

critical applications. Both proactive and reactive protocols have best features in

APTEEN. Threshold Distributed Energy Efficient Clustering (TDEEC) an exten-

sion of DEEC is presented by [13] on basis of threshold value node become CH is

discussed in TDEEC. The idea of sink mobility to maximize network lifetime is

discussed [14]. Linear programming formulation for sojourn time and sink mobility

is also presented in this paper. However, they have not discussed specific mobility

patterns in their research work. Sink mobility between two different locations is

presented in [15] and have minimum loss of data in their proposed technique.

5

Four different patterns of sink mobility are discussed in [16] . Data is collected from

over all network by a mobile sink. random walk and passive data collection, partial

random walk with limited multi-hop data propagation, biased random walk with

passive data collection and deterministic walk with multi-hop data propagation

are four mobility patterns used by this protocol.

In conventional clustered routing protocols CHs are elected using distributed al-

gorithm for each round. In every round, nodes elect itself to be a CH, decision to

be a CH is based on pre-defined percentage of CHs for network. Node n chooses

a random number among 1 and 0 while making this decision. If threshold value

becomes greater than random number then that node becomes a CH for current

round. Pnrm and Padv are weighted election probabilities for normal nodes and

advanced nodes to become CHs and are given by using following equations. (1)

given in [2].

Pnrm =Popt

1 + am(2.1)

Where,

Pnrm is election probability of normal nodes to become CH.

Popt is optimal probability.

m fraction of advance nodes.

α is an additional energy factor between normal and advance nodes.

Padv =Popt

1 + am∗ (1 + α) (2.2)

Where Padv is probability of advance nodes to become CH. α is an additional

energy factor.

These sensor nodes become CHs by comparing random number between 1 and 0

with given threshold calculated by following equation (2) given in [2].

Ti =

Pi

1−Pi[r.mod 1Pi

]if SiεG

0 otherwise(2.3)

Where,

G is set of those nodes which have not become CH yet.

6

Our TRP heterogeneous, cluster less, with mobile sink, to grantee reliable and

energy efficient communication for tunneled WSN.

SEP, LEACH, Threshold sensitive Energy Efficient sensor Network protocol (TEEN),

and Distributed Energy Efficient Clustering (DEEC) worked on clustering tech-

nique. All sensor nodes send their data to CHs and then CH forward data to the

BS. As BS is static in sensing field, maximum energy is consumed in transmitting

data to BS. In order to ensure energy-efficient and reliable communication, this

work focuses on sink mobility in striped areas. We have implemented sink mobility

in tunneled network in our proposed protocol TRP. In this scheme, sink is mobile

in middle line of the field and gathers data from all sensors. From residual energy

aspect, sink is mobile and nodes switch off their transmitters when they get out of

range of sink. Hence, save energy and outperform than other conventional routing

protocols. Our current aim is to achieve a self-configured and robust WSN that

maximizes lifetime.

Focus of the research in WSNs is on MS, as it is improving the lifetime of the net-

work. Sink mobility can be consider in two categories, controlled (MS moves along

pre-defined trajectory) [17], [18] and un controlled (MS has random motion) [19].

Random motion of sink if it stays on all the given set of location has polynomial

solution for maximization of network life. However, controlling the sink motion in

a specific trajectories is more challenging. Also, in [20] mobile relay approach is

discussed, in which MS receive data from nodes through direct transmissions and

data transmission is with mechanical movement. Tolerable delay is introduced to

avoid over flows and ques [21]. In [14] MS lowers the saturation from the nodes

which are close to sink and resulting increase the network life. Authors in [22]

surveyed variants of Distributed Energy Efficient Clustering (DEEC) on basis of

multi level heterogeneous network to two level heterogeneous network. However ,

hierarchal clustering is discussed in [5] by authors. They have used primary and

secondary CHs in their clustering hierarchy. In [23] the uniform distribution of

nodes is used in planed region, hence take full advantage of three level of node

heterogeneity.

Communication between sink and the wireless sensor nodes is performed through

routing. Their are many techniques for routing, through clustering, multi-hop or

direct. If a node has to send the data to sink which is very far then maximum

energy is consume in transmission. To save energy and prolong lifetime of the

network clustered base techniques are used in which nodes and sink both are sta-

tionary. In [2] introduced a hierarchical clustering algorithm for sensor networks

known as Low Energy Adaptive Clustering Hierarchy (LEACH). LEACH is clus-

tered based routing protocol and cluster formation is distributed. CH election

7

is random, and it rotates this clustering process, to efficiently distribute energy

among sensor nodes. However, authors used clustering to evenly balance load

between sensor nodes in [1], every node in two level heterogeneous hierarchal net-

work selects itself to be a CH on bases of its initial energy compare to other nodes.

Authors in [24], used clustering with three level of node heterogeneity in terms

of energy to prolong network lifetime and stability period. Whereas, clustering

technique is used by authors to reduce energy consumption in [4], CHs are elected

probabilistically on the basis of ratio between residual energy of each node and

average energy of network. However, all above mentioned papers they have not

discussed sink mobility for prolonging network lifetime.

SEP, LEACH, Threshold sensitive Energy Efficient sensor Network protocol (TEEN)

[10], and Distributed Energy Efficient Clustering (DEEC) [4] worked on clustering

technique. All sensor nodes send their data to CHs and then CHs forward data to

the BS. As BS is static in sensing field, maximum energy is consumed in transmit-

ting data to BS. In order to ensure energy-efficient and reliable communication,

this work focuses on MS. We have implemented sink mobility in squared network

in our proposed protocol SRP. From residual energy aspect, sink is mobile and

nodes switch off their transmitters when they get out of range of sink. Hence, save

energy and performance enhanced than other conventional routing protocols. Our

current goal is to achieve a robust self-configured WSN that maximizes lifetime.

In above mentioned schemes maximum energy of the sensors is consume in elect-

ing CH. Here, in our proposed scheme we introduced MS instead of clustering.

Now, sink moves on predefined trajectory and collect data directly. Nodes after

transmission go to sleep mode and sink moves on.

8

Chapter 3

HSEP: Heterogeneity-aware

Hierarchical Stable Election

Protocol for WSNs

3.1 Existing Routing Protocols For WSNs

Routing protocols are use to route data between networks, sensed data is trans-

mitted to CHs, CHs further transmit aggregated data to BS. There are many

routing protocols such as DT routing protocol in which there is no use of cluster-

ing technique to minimize energy consumption in network. Nodes sense data and

directly transmit data to BS so nodes far from BS dies first. Whereas, in MTE,

nodes near to the sink dies earlier in MTE. As a result, some part of area which

is to be monitored cannot be observed for a maximum of the lifetime of the over

all network. We propose Hierarchal Stable Election Protocol (HSEP) which en-

hance network life and stability period than other conventional routing protocols,

so solution proposed for these type of problem is discussed in next section.

3.1.1 LEACH

LEACH is self-organized, adaptive clustering protocol that uses random distribu-

tion of sensor nodes in area, to evenly distribute energy between nodes in sensor

network. In LEACH, sensor nodes are organize in a way that some of nodes

become CHs transmit data to BS. In this process CHs are elected on basis of

probability. CHs election criteria of sensors nodes at any given time with a cer-

tain probability depends upon a random selection of a number between, 0 and

9

1. This random number is then compare with given threshold value, if value of

threshold is greater than random number, a sensor node becomes a CH and trans-

mit data to BS. Nodes which become CHs broadcast their status in network. Each

sensor node join CH on basis of Received Signal Strength Indicator (RSSI). After

organizing a network into clusters, each CH allocates a TDMA slot for node in

its cluster. So except the transmission times, all non CH sensor nodes switch off

their transceivers, thus minimizing energy dissipation by individual sensor nodes.

Once all aggregated data is received by CH from its associated nodes, then this

aggregated data transmits to BS after compression. As in the scenario which we

are observing, BS is far away and high transmission energy is required. However,

there are only few CHs this only affect small number of nodes. Being CH for a

long time drains out battery of sensor node, so to avoid this unnecessary draining

of energy of single node. CH does not remain same they keep on changing, they

are self configured. Thus, clustering seems to be an energy-efficient technique in

routing protocols.

3.1.2 SEP

SEP is routing protocol, which uses clustering based routing technique with node

heterogeneity in a sense that it has fraction of advance nodes. SEP used to select

CHs in a distributed fashion in WSNs, SEP is heterogeneity-aware protocol and

initial energy of each node relative to that of other nodes in a network is used for

weighted election probabilities of each node. This enhances the stability period.

SEP performs better than LEACH in evenly consuming additional energy of ad-

vanced nodes.LEACH has stability period than SEP which improves stability of

clustering hierarchy, using parameters of heterogeneity, advanced nodes, m. In or-

der to enhance stability, SEP tries to maintain balanced energy consumption and

normal nodes become CHs lesser than advance nodes. Initial energy of normal

nodes is equal to E0, and advance nodes have (1 + a)E0 of initial energy. Where,

(α) is percentage of energy higher than normal nodes. In SEP, every node has

some probability to become CH. A random number between 0 and 1 is selected by

each node, if this selected random number is less than given threshold T (s) then

that node become CH in current round to evenly distribute energy in network.

T (s) increases with number of rounds within each epoch and becomes equal to 1

only in last round, i.e, remaining nodes in last round become CH with probability

1. Pnrm and Padv are weighted election probabilities for normal and advance nodes.

For each node to become a CH an optimal probability is divided on the basis of

energy and can be calculated by using following formulas:

10

pnrm =popt

1 + am(3.1)

padv =popt

1 + am∗ (1 + a) (3.2)

Padv is probability of advance nodes to become CH. Popt is optimal probability . m

fraction of the advanced nodes. α is an additional energy factor among advanced

and normal nodes.

Now for the assuring of same CHs selection criteria as authors assume, they take

threshold level as another parameter into consideration. Each node selects a ran-

dom number between 0 and 1, if value of T (s) becomes greater than that selected

random number then this node becomes CH. Threshold calculating formula for

both type of nodes depend upon their probabilities, which are given below:

T (si) =

pi1−pi(rmod 1

Pi)

if siεG

0 otherwise(3.3)

Tnrm =

{

pnrm

1−pnrm[r.mod 1pnrm

]if nnrmεG

′

0 otherwise(3.4)

G′ is set of nodes which have not become cluster heads in current round. Tnrm is

threshold for normal nodes to become CH. Pnrm is probability of normal nodes to

become CH.

Tadv =

padv1−padv[r.mod 1

padv]

if nadjεG′

0 otherwise(3.5)

G′ is set of nodes which have not become CHs in current round. Padv is probability

of advance nodes to become CH. Tadv is threshold for advance nodes to become

CH.

11

3.1.3 ESEP

ESEP is heterogeneity aware routing protocol. An efficient manner must be used

to evenly balance available energy for maximizing network life and stability period

in WSNs. Authors present an easy approach which is an extension of SEP called

as ESEP. In ESEP by considering three type of nodes: normal, advance and

intermediate nodes. Actual goal of ESEP is to have a self configured WSN that

prolongs lifetime and stability period. Major aim is to minimize communication

cost and maximizing network resources to ensure correct information. Each node

in network transmit sensed data to associated CH performs data aggregation to

reduce redundancy and send that data to BS. In this protocol, each sensor node

chooses a random number between, 0 and 1. If T (s) becomes greater than random

number value which is given in equation 3, then node becomes a CH in current

round. Intermediate nodes can be choose by a relative distance of normal nodes

positions to advance nodes position in network or by a threshold of energy level

between normal nodes and advance nodes.

3.1.4 DEEC

DEEC is a protocol that has been designed to deal with nodes of heterogeneous

energy level in a WSN. For the CH selection, DEEC uses residual and initial

energy level of the nodes. Let ni is number of rounds to be a CH for node si. We

want to attain PoptN number of CHs in our network during each round. The CH

selection criteria in DEEC is based on energy level of the nodes. As in homogenous

network when nodes have same amount of energy during each epoch then choosing

Pi = Popt will assure that PoptN CHs during each round. In heterogeneous network

the nodes with high energy are more probable to become 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 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 by:

E(r) =1

N

N∑

i=1

Ei(r) (3.6)

pi, probability for the CH selection in DEEC is given by:

12

pi = popt[1−E(r)−Ei(r)

E(r)] = popt

Ei(r)

E(r)(3.7)

In DEEC the average total number of CH during each round is given by:

N∑

i=1

pi =

N∑

i=1

poptEi(r)

E(r)= popt

N∑

i=1

Ei(r)

E(r)= Npopt (3.8)

Pi is probability that is used by each node to become CH in a round. Where,

G is set of nodes eligible to become CH at round. If node has not become CH

in recent rounds then it belongs to G. During each round each node chooses a

random number between 0 and 1. If the number is less than threshold, it will be

become a CH else not.

As, popt is reference value of average probability pi. In homogenous networks,

all nodes have same initial energy so they use popt to be the reference energy for

probability pi. However in heterogeneous networks, the value of popt should be

different according to the initial energy of the node. In two level heterogenous

network the value of popt is given by:

padv =popt

1 + am, pnrm =

popt(1 + a)

(1 + am)(3.9)

padv and pnrm are used instead of popt in equation (6) for two level heterogeneous

network as given below:

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

(3.10)

3.2 HSEP: The Proposed Protocol

HSEP is hierarchal based clustering routing protocol, use to reduce transmission

energy between CH and BS, as distance between CH and BS increases it increase

its transmission energy, because maximum energy consumed in process of data

13

BASE STATION

CLUSTER

SECODARY

CLUSTER

HEAD

PRIMARY

CLUSTER

HEAD

SENSOR

NODE

ab

c

d

Figure 3.1: Network Topology

transmission from CH to BS. HSEP is heterogeneous-aware protocol in a sense

having two types of nodes i.e. advance nodes and normal nodes taking part in

sensing an area M = 100x100, election probabilities of nodes to become CHs are

weighted by initial energy of a node relative to other nodes in network. This

prolongs time interval before death of first node (stability period), stability is

important for feedback applications. So we propose HSEP to minimize this trans-

mission cost by proposing clustering hierarchy, we use two type of CHs, primary

CHs and secondary CHs. Secondary CHs can be from existing primary CHs, and

elect on basis of probability (Ph) from those nodes which already become primary

CHs and only primary CHs can take part in process of electing secondary CHs.

Primary CHs check distance between each others and transmit their data to those

CHs which are at minimum distance from them. However these minimum distance

CHs are secondary CHs. HSEP uses two types of nodes normal and advance nodes,

advance nodes have higher probability to become CH than normal nodes. Nodes

select a random number between 0 and 1, compare it with defined threshold if

random number value is less than threshold then a node become primary CH,

aggregate data, send it to secondary CHs which further transmit aggregated data

to BS. Topology use in HSEP is that two level of clustering hierarchy, where, sen-

sor nodes first sense desired data, transmit it to primary CH using TDMA slots

allocated by primary CHs to their associated nodes. However(Ph)is probability of

primary CHs to become a secondary CHs in every round. Primary CHS transmit

their aggregated data to secondary CHS by associating with them using again

TDMA slots allocated by secondary CHS, then secondary CHS further transmit

aggregated data to BS and thus minimizing transmission distance between sec-

14

ondary CHs and BS consume less energy. however whole process is define in three

phases,in first phase sensor nodes sense data according to requirement this can

be a temperature and motion of some body. In second phase nodes take part in

becoming primary CHs by comparing random number with threshold if node be-

come primary CH it broad cast head message in network and nodes get associate

with them using receive signal strength indicator (RSSI) and send their sensed

data to their CHs which we call as primary CHs. In second phase these primary

CHs again get associate to their secondary CHs according to shortest distance

between them as shown in fig 3.1 with a, b, c and d according to this distance sec-

ondary CHs are selected as these are the only short distances so only these short

distance primary CHs only become secondary CHshown in fig7, these secondary

CHs aggregate data receive from primary CHs and send aggregated data to BS.

3.2.1 Comparison of LEACH, SEP, ESEP, DEEC and HSEP

Table 1 shows parameters used for simulation. For analysis of our simulation

results, we consider following performance matrices which shows results for case

when m=0.1, α = 1 and β = 0.3. However, beta factor is only used in ESEP,

where intermediate nodes are between. It can be easily seen from fig 3.2, that

stability period of HSEP is extended as compared to LEACH, SEP, ESEP and

DEEC. First node dies at 1900 rounds, whereas stability period of LEACH dies

at 52.3 percent less than HSEP, however stability period of SEP is 47.3 percent

less than HSEP and 10 percent larger than LEACH, however stability period os

ESEP is 42.1 percent less stable than HSEP, 9 percent larger than SEP and 18

percent larger than LEACH.

Values used for simulations are Eelect = 50nJ/bit, EDA = 5nJ/bit/message, εfs =

10pJ/bit/m2, εmp = 0.0013pJ/bit/m4, E0 = 0.5J , K = 4000, Popt = 0.1, n =

100, α = 1, m = 0.1, Eelec = transmitter/receiverelectronicsenergy. EDA =

dataaggregation,

Stability period of HSEP is 23.6 percent larger than DEEC. however if we talk

about DEEC its stability period is 24.1 percent larger than ESEP, 31 percent

larger than SEP and 37 percent larger than LEACH. DEEC has higher stability

period than LEACH, SEP, and ESEP because it uses residual energy of nodes in

electing CHs, node having higher residual energy has greater chances to be a CH,

thus enhances stability period of DEEC. While ESEP, a flavor of SEP out per-

forms SEP and LEACH in terms of stability because ESEP is getting benefit from

three level of heterogeneity and have three kind of nodes, i.e. normal,intermediate

and advance nodes. However, α additional energy factor between advance and

15

0 2000 4000 6000 8000 10000 120000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Dead

nod

es

Nodes dead during rounds

SEPLEACHDEECESEPHSEP

Figure 3.2: Comparison of HSEP with LEACH, SEP and ESEP with α = 1m = 0.1

normal nodes and β is additional energy feature between normal and intermediate

nodes. due to three types of nodes in ESEP it has three different energy levels. If

we compare ESEP and DEEC with our proposed protocol HSEP we can see that

HSEP out performs LEACH, SEP, DEEC and ESEP interms of stability period

and also beats SEP, ESEP, LEACH and DEEC in term of network life. HSEP is

out performing than others because it is hierarchal based stable election protocol

in which cluster are of two level of hierarchy, in this process once primary CHs

elected then secondary CHs elected according to defined probability and differ-

ence of distance between primary and secondary CHs. Due to which they reduce

transmission energy and have large stability period and network life time.

In fig 3.3 there is a comparison of throughput of DEEC, HSEP, SEP, LEACH and

ESEP with same parameters as discussed above. Throughput is total number of

packets send to BS from CHs in whole network life and we can see that DEEC

has highest throughput . Its throughput increased in first 2500 rounds that is

it reaches 7kbps and then become constant after 2500 rounds. Whereas SEP

has 1.2kbps throughput which is 82 percent less than DEEC and LEACH has

1.17kbps throughput which is 83 percent less than DEEC. SEP has a little bit

higher throughput than LEACH because SEP is for heterogeneous networks having

two types of nodes which take a part in clustering where as in LEACH same nodes

which take a part in clustering. In ESEP it has 2kbps throughput which is 71

percent less than throughput of DEEC however, its throughput is higher than SEP

16

0 2000 4000 6000 8000 10000 120000

1

2

3

4

5

6

7

8x 10

4

Number of rounds

Thro

ughp

ut

Packets sent to the base station

SEPLEACHDEECESEPHSEP

Figure 3.3: Comparison of HSEP with LEACH, SEP and ESEP with α = 1m = 0.1

and LEACH because of three types of node heterogeneity. Our proposed protocol

HSEP has 57 percent higher throughput than SEP, 28.5 higher than ESEP and

58.21 higher than LEACH. Throughput of HSEP is 2.8 kbps in 4000 rounds and

become constant after 4000 rounds, so our simulation results show that HSEP

beats ESP ,ESEP, and LEACH in throughput and DEEC out performs from all

of these protocols.

Fig 3.4 shows rate of nodes in network which are alive with number of rounds.

Results for case of same parameters as used above. From fig 3.4 we see that

HSEP out performs DEEC, SEP, LEACH and ESEP in stability period. There is

very little difference between stability period of LEACH, SEP and ESEP. However,

DEEC has larger stability period than SEP, LEACH and ESEP. Now if we compare

ESEP with SEP and LEACH, we see that ESEP has higher stability period than

SEP and LEACH because ESEP has three level of node heterogeneity, whereas

SEP has two level of heterogeneity having two types level of heterogeneity and

LEACH is homogeneous routing protocol having same type of nodes, so due to

three level of heterogeneity ESEP has higher stability period its first node dies

at 1900 which is 5.2percent more than SEP and 10 percent more than LEACH.

However our protocol HSEP has highest network life than , ESEP, DEEC, LEACH

and SEP so by changing value of α and m there is a significant improvement on

network life of HSEP we can see it from fig3.3. Whereas network life of HSEP is

40 percent more than ESEP , 75 percent more than SEP network life time, and

17

0 2000 4000 6000 8000 10000 120000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Alive

nod

es

Nodes alive during rounds

SEPLEACHDEECESEPHSEP

Figure 3.4: Comparison of HSEP with LEACH, SEP and ESEP at α = 1m = 0.1

61 percent more than LEACH and 52 percent more network life than DEEC. So

from our simulation we clearly see that HSEP has largest network life and stability

period at α = 1 and m = 0.1.

In fig 3.5 comparison of throughput of DEEC, HSEP, SEP, LEACH and ESEP

are discussed values of parameters m = 0.1 and α = 3. We can see that DEEC

has highest throughput its throughput is 14 kbps then become constant till end

of network life and if we look at HSEP its throughput increase slowly and goes

up to 5 kbps which is 64.2 less than DEEC and then become constant after that,

whereas HSEP beats SEP, LEACH and ESEP in throughput because it is hierar-

chal based clustered routing protocol which consume energy more efficiently than

SEP, LEACH and ESEP . Throughput of ESEP is 4kbps which is 71 percent less

than DEEC. However, ESEP has higher throughput than SEP and LEACH be-

cause it is heterogeneous protocol having three types of nodes in it so have high

throughput than SEP and LEACH. Whereas, if we talk about LEACH and SEP.

Both have 2 kbps which is 85.71 less than DEEC. SEP has higher throughput

because its heterogeneous protocol and have two level of heterogeneity in energy.

Whereas, LEACH has same type of nodes known as homogeneous network so

have less throughput than SEP. So from our simulation results we clearly see that

DEEC outperforms HSEP, ESEP, SEP and LEACH in throughput and stability

period.

Characteristic parameters used in fig 3.6 shows rate of nodes in network which

18

0 2000 4000 6000 8000 10000 120000

5

10

15x 10

4

Number of rounds

Thro

ughp

ut

Packets sent to the base station

SEPLEACHDEECESEPHSEP

Figure 3.5: Comparison of HSEP with LEACH, SEP and ESEP at α = 3m = 0.1

are alive with number of rounds. Beta factor is only used in ESEP. From fig 3.6

we see that HSEP and DEEC out performs SEP, LEACH and ESEP in stability

period however there is very less difference between first node dead round of HSEP

and DEEC. HSEP has 2.6 percent more stable than DEEC. If we talk about

LEACH, SEP and ESEP we can see that ESEP has higher stability period than

LEACH and SEP because it is heterogeneous protocol, so if we compare SEP with

LEACH we see that SEP has higher stability period than LEACH because SEP

is heterogeneous routing protocol having two level of heterogeneity and two types

of nodes advance nodes and normal nodes which take a part in clustering process

however LEACH is homogeneous WSN protocol it has same type of nodes which

become CH in every round and become dead early than SEP , Now if we compare

ESEP has5.5 percent higher stability period than SEP and 11.1 percent higher

than LEACH. However LEACH has highest network life than HSEP, ESEP and

SEP. LEACH, HSEP and ESEP has 40 percent larger than SEP and DEEC last

node dies at 6000 rounds. So from our simulation we clearly see that LEACH,

HSEP and ESP has largest network life and HSEP has highest stability period at

α = 3 and m = 0.1. Fig 3.7 shows rate of nodes in network which are going to be

dead with number of rounds. It can be seen easily from fig 3.7 that stable region

of HSEP and DEEC are larger as compared to that of LEACH, SEP and ESEP.

However, there is very less difference between stable period of HSEP and DEEC,

however HSEP has 2.6 more stable region than DEEC because HSEP is hierarchal

based clustering that’s why energy consumption is more efficient than DEEC.

19

0 2000 4000 6000 8000 10000 120000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Alive

nod

es

Nodes alive during rounds

SEPLEACHDEECESEPHSEP

Figure 3.6: Comparison of HSEP with LEACH, SEP and ESEP α = 3m = 0.1

ESEP has 5.5 percent higher stability region than SEP and 11.1 percent LEACH.

However LEACH has highest network life than HSEP, and DEEC. LEACH, HSEP

and ESEP has 40 percent more network life than SEP and DEEC. So from our

simulation we clearly see that leach, HSEP and ESP have largest network life

and HSEP has highest stability period at α = 3 and m = 0.1. We can see that

stability period of SEP is 32 percent less than ESEP. Which is larger than SEP and

LEACH, first node dies at 1500 rounds because it is also heterogeneity awareness

protocol. As three types of nodes take apart in clustering, so, it increases stability

period of network and HSEP outperforms ESEP, SEP and LEACH in stability

period because it uses hierarchal technique in clustering. It use two level hierarchy

in cluster formation and then transmit sensed data and efficiently utilize energy

consumption in network. So from our simulation it is clearly seen that HSEP has

largest throughput among DEEC, SEP, LEACH and ESP however ESEP, LEACH

and HSEP have largest network life at given value of α and m.

20

0 2000 4000 6000 8000 10000 120000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Dead

nod

es

Nodes dead during rounds

SEPLEACHDEECESEPHSEP

Figure 3.7: Comparison of HSEP with LEACH, SEP and ESEP α = 3m = 0.1

21

Chapter 4

TRP: An Energy Efficient

Approach Incorporating Sink

Mobility in WSNs

4.1 Sink Mobility

One of the basic tasks in WSNs is gathering of data from sensor nodes. Sinks are

the nodes or machines having unlimited energy resources. Repairing and mainte-

nance of the network is responsibility of the sink node. The energy consumption

and overall network lifetime depends upon the position of sink node in the net-

work. In static sink WSNs, where sink is static at the center of the field, nodes

near to the BS exhausts their energy very quickly because these nodes act as relay

nodes and die early. because of this unbalanced load management sensor nodes

have problem of non uniform power dissipation in the network. As a result of this

sensor nodes dies quickly and the network gets unstable. To handle this problem

the idea of sink mobility is introduced to evenly distribute energy among the sen-

sor nodes in the network. One of the most efficient ways of load balancing is using

sink mobility in WSNs. Sink mobility can be in variety of patterns across the field

such as sink mobility across the border, at center of the field, moving diagonally

in the field and spiral movement of the sink within the sensing field.

Sink mobility in the two level heterogeneous WSNs is introduced in this thesis and

measured its performance in different locations of sink. Following section describes

the network model and its parameters.

22

4.2 Network Model and Description

This paper is about data gathering for WSN. Generally network model has follow-

ing characteristics. A WSN is composed of a BS and N sensor nodes randomly

distributed in M × M region. Nodes have unique identity, sink is moving and

all sensor nodes are static in field. There are two different kind of sensor nodes,

normal and advance nodes. Here m is fraction of advance nodes from total N

nodes. Normal nodes have α times lesser energy than advance nodes. In our

network model we implement sink mobility in 200m × 6m and meters regions.

Sink can mobile according to the situation and has no energy constraint. Sen-

sor nodes, have limited energy E0. We have defined transmission range of 10m

in our model. Sensor nodes only switch on their transmitters when they are in

defined range of sink. Simulation parameters are given in Table ?? where Eelec is

Table-1 Simulation parameters labeltabEelect 50nJ/bitEDA 5nJ/bit/messageεfs 10pJ/bit/m2

εmp 0.0013pJ/bit/m4

Eo 0.5JK 4000Popt 0.1n 100α 1m 0.1

transmitter/receiver electronics energy.

EDA is data aggregation.

εfs transmit amplifier if dmaxtoBS≤do.

εmp transmit amplifier if dmaxtoBS≥do.

4.2.1 Heterogeneous Network Model

Heterogeneous network model is defined here, N randomly distributed sensors

within M × M area. Deployed sensors send their data to BS in the field. We

assume that sink is mobile in sensing field. All nodes send their data directly to

mobile sink when sink comes in defined range of 10m. To avoid changes in network

topology we assume that sensor nodes are static.

We are using two level heterogeneous model in our scenario. In two level heteroge-

neous networks we have two kinds of sensor nodes i.e advance and normal nodes.

23

However, initial amount of energy of normal nodes is E0 and (1 + α)E0 is the

energy of advance sensor nodes, which is α times greater than normal nodes and

m is the fraction of advance nodes in network, thus initial energy of mN advance

nodes is equal to (1+α)E0 and initial energy of (1−m)N normal nodes is equal to

E0. So, the two level heterogeneous network model has total initial energy given

[1]:

Etotal = N(1−m)E0 +NmE0(1 + α) = NE0(1 + αm) (4.1)

So, two level heterogeneous network model has αm time extra energy and nodes.

We have implemented three different network topologies with sink mobility.

4.3 Our Proposed TRP

Our proposed protocol is composed of heterogeneous network having two kind of

nodes i.e advance and normal nodes. We consider MS in different patterns in our

proposed protocol TRP. We have implemented it in rectangular region and consid-

ering it tunnel. In TRP we have implemented MS with some defined transmission

range for sensor nodes. Whenever sensor nodes become in defined range of 10m

then they send their data to MS other wise they switch of their transmitters and

stay in sleep mode and keep on sensing. Our protocol is clusterless routing proto-

col, mobile sink receives data directly from all sensor nodes. If we use clustering

then nodes require two transmission of data which use more energy, first is to send

data to associated CHs and then from CHs to BS. However, in cluster less process

nodes on require single transmission of data to MS, thus consume less energy and

resulting prolonging network life time. This problem can be define in terms of

energy.

Object function: Minimization of energy consumption

Minimize E (4.2)

subject to:

qixij ≥ 1 ∀i, j (4.3a)∑

k:i∈n

pkij.ti ≤ E ∀i, j, k (4.3b)

24

∑

i,j

∑

k

pk,lij ≤ Pi, ∀i, k (4.3c)

fk,lij ≤ h(pk,lij ) ∀i, j, k, l (4.3d)

pki is the power consumption of node i during kth epoch.

Equation (6a), shows that the rate of information generation multiply with the

data flow coming on the node, during any epoch is at least 1. Equation (6b), sum

of the energy consumed during every epoch is less than equal to initial energy,

energy conservation. pk,lij denotes the transmission power during kth epoch on

sink location l. Equation (6c) is peak transmission power constraint Pi to node i

for all sink locations and time intervals. Equation (6d) ensures capacity related

upper bound h(pk,lij ), where h is non decreasing concave function, on the achievable

link rate fk,lij on link (i, j) using power pk,lij .

dmax

NUC

MS

L

Figure 4.1: MS along border of field

4.3.0.1 MS along border of field (B-TRP):

Fig 4.1 shows network topology for sink mobility at border of field. There we are

considering a Node Under Consideration (NUC) at a maximum distance from the

Moving Sink (MS), dMSNUC = l = dmax. The transmissions are between 0 ≤ l ≤

dmax. In this scheme network life time is prolonging approximately 3,000 rounds,

as compare to the scheme in which sink is moving diagonally in the tunnel.

25

dmax=l/2

NUC

MS

L

NUCNUC

Figure 4.2: MS along diagonal line of field

4.3.0.2 MS along diagonal line of field (D-TRP):

Fig 4.2 shows diagonal mobility pattern of sink in the field. As sink moves distance

between nodes and mobile sink changes hence effecting transmission energies. All

nodes send their sensed data to mobile sink directly when sink come in the prede-

fined range of the nodes, otherwise nodes will stay in sleep mode. However, sink

mobility along diagonal line is consuming more energy as compare to the border

line, because the distance is varying.

As, diagonal length =√

(w2 + l2). In Fig 4.2 there is NUC at 3 different points,

at one point the distance between MS and NUC is zero the sink is located exactly

at the node position. In the mean time the nodes located on the opposite direction

need maximum transmission energy because nodes are directly transmitting their

data. In the next location distance between MS and NUC greater than zero but

less than Rmax. As sink is moving, after some time distance between MS and NUC

is Rmax. Length of the distance covered by MS in this case is greater than along

boarder and in the center. Due to this reason nodes have to wait and buffer the

data until MS come in the transmission range.

4.3.0.3 MS along horizontal line passing through center of field (C-

TRP):

Sink mobility horizontally through center of field is shown in fig 4.3. Due to

tunneled area and random distribution, nodes are close to each other. AS MS is

passing through center distance with sensing nodes minimum 0 ≤ l ≤ l/2. We

26

dmax=l/2

NUC

MS

L

Figure 4.3: MS along horizontal line passing through center of field

have defined a transmission range of 10m, with in which sensor nodes send their

data to MS whenever it comes in predefined range. Other wise all sensing nodes

turn off their transmitters and just do sensing and go into sleep mode to conserve

energy and stay alive for long period of time. We can see from our simulation

results that in this scheme TRP out performs all conventional routing protocols

in stability period, network life time and throughput.

NUC

MS

L

Figure 4.4: MS for S-TRP

27

4.3.0.4 MS in spiral Trajectory of field (S-TRP):

Spiral trajectory of MS is shown in fig 4.4. When MS passes through spiral

trajectory it covers maximum area and the distance between nodes and MS is

minimal. So, MS coves maximum area and as its motion is spiral. S-TRP is

performing excellent as compare to the C-TRP, B-TRP, and D-TRP, as well as

T-SEP

4.4 Simulation Results

Simulation results show comparison of 4 given flavors of CL-SEP in tunnel and

its comparison with conventional clustered tunnel SEP (T-SEP). Conventional T-

SEP has static sink in middle of the filed and sensors are sending their data to

the sink through CHs. The energy utilization is almost double. First sensor nodes

will elect their CH vis sending messages. Then CHs will receive data from their

respective cluster members, and the after aggregation they send the data to the

sink. Clusters which are far from the sink will use double transmission energy.

The first node of T-SEP dies in 950th round and last at 2150. In given scenarios

sink carries data from senor nodes while continuously moving in field. When sink

comes in defined range of sensor nodes then nodes send their data, other wise they

switch off their transmitters and move to sleep mode which help in saving energy.

However, distance for transmission range defined is 10m, in this way energy can

be conserved and we get good results.

Fig. 4.5 shows rate of alive nodes with number of rounds. We defined m =

0.1 percent advance nodes having α = 1 time higher energy than normal nodes.

Comparing 4 flavours of TRP with T-SEP. Where, SEP is modified in the tunnel

by changing the dimensions of SEP. It is working on the clustering technique,

as the field is now tunnel shape, which is like rectangle. The distance between

sink and the clusters which are located at far ends is increased. Due to distance

transmission and formation of clusters more energy is consumed in T-SEP. First

node will be dead in 950th round and last node will be dead on 2150th round.

Here, we are discussing and comparing T-SEP with our proposed TRP’s flavors.

TRP is cluster less protocol with MS, which is collecting data from nodes directly.

We have discussed C-TRP, where, sensor nodes are continually sending their data

to sink. From fig 4.5 it is seen that all network will be dead in 52, 000 rounds.

Comparing it with B-TRP, last node will be dead in 70, 000 rounds. B-TRP and

D-TRP have same life time, only difference between them is stability period. B-

TRP is more stable than D-TRP. Now looking at the graph we can see that S-TRP

28

0 2 4 6 8 10 12

x 104

0

10

20

30

40

50

60

70

80

90

100

Number of rounds

Nu

mb

er

of a

live

no

de

s

C−tunnelD−tunnelB−tunnelS−tunnelT−SEP

Figure 4.5: Alive Nodes with α = 1m = 0.1

is out performing than B-TRP, C-TRP, D-TRP and conventional T-SEP, in terms

of stability period and network life time, first node dies at 8, 000 rounds and last

node dies at 1, 10, 000 rounds. As S-TRP is clusterless routing protocol, each node

is responsible for its own transmission to sink. Main reason of out performing of

this protocol is sink mobility in spiral trajectory and tunnel like area in which

distance is minimum between nodes and MS and nodes require minimum energy

to transmit their sensed data to BS. Each node switch off its transmitter when

ever it goes out of the range and save energy.

Fig 4.6 shows results for throughput of our proposed protocol TRP variants and T-

SEP. From simulation results we can see that C-TRP is out performing among rest

flavours of TRP and T-SEP in terms of throughput. We can see that throughput of

C-TRP gradually increases up to 25000 rounds with 2,50,000 bps and then slowly

increases up to 70,000 rounds with up to 2,70,000 bps. Due to sink mobility

at middle line of field, this protocol out perform s because sensor nodes switch

off their transmitters, when they get out of the defined range. Defined range is

distance of 10m from mobile sink. When ever sensing nodes are in range of sink

they send their packets to sink other wise they go into sleep mode and save energy.

Similarly, D-TRP and B-TRP are performing almost in a same manner gradually

increases up to 21500 rounds with 2,60,000 bps. S-TRP gradually increases upto

43,000 rounds with upto 4,25,000 bps. Now compare these TRP variants with

conventional T-SEP. It is noticed that conventional T-SEP increases up to 1600

rounds with up to 10,000 bps.

29

0 1 2 3 4 5 6 7 8

x 104

0

0.5

1

1.5

2

2.5

3x 10

5

Number of rounds

Pa

cke

ts to

BS

bits/s

ec

C−tunnelD−tunnelB−tunnelS−tunnelT−SEP

Figure 4.6: Throughput of TRP with α = 1,m = 0.1

30

Chapter 5

SRP:An Energy Efficient

Approach Incorporating Sink

Mobility in WSNs

5.1 Our Proposed Squared Routing Protocol (SRP)

This research work is about data collection from WSNs. Our proposed protocol

consists of heterogeneous network with two different kind of nodes i.e, advance and

normal nodes. We consider four different mobility patterns of sink mobility in the

field. These four mobility patterns are 1) Movement of sink at border of square

field, 2) Movement of sink at center of the field, 3) Diagonal movement of sink in

the square field and 4) Spiral movement of sink in the sensing field. Sink nodes

moves in the field and carries data from sensor nodes. SRP is clusterless routing

protocol and all sensor nodes send their data directly to a mobile sink in the field.

Simulation result shows that this protocol performs betters than conventional

SEP in WSN. There are N number of sensor nodes deployed randomly in the area

of 100 × 100 and sink is moving at predefined path within the sensing field. We

propose SRP and its variants. We introduced sink mobility in Squared path within

the Squared region (SS-SRP) shown in fig. 5.2. We also have implemented sink

mobility in Circular path within the Squared field (SC-SRP) with three different

radii shown in fig. 5.3. However, in third variant we have implemented sink

mobility in Circular path within the Circular (CC-SRP) sensing field shown in

fig. 5.4. Strive is to improve the lifetime of the network by introducing two types

of nodes normal and advance, and making the sink mobile. In this model sink is

mechanically driven and can be recharged, so energy is not a constraint on moving

sink, in short we can say that sink as a small vehicle, which is unmanned, and

31

transceiver is attached with it. Mobile sink collects data, not randomly but on the

defined path. To avoid buffering over flow of the information packets received at

nodes, the tour of the sink and its sojourn locations have specific time, so that all

nodes in the network can easily transfer their data without any loss of packets. To

make it cost-effective sojourn tour is predefined and the distance between the two

locations is bounded by rmax. By exploiting the trajectories of sink, we explored

different results. If sink is moving in the circular pattern within the network it

is giving best results as compare squared mobility pattern. Observed that sum of

the sojourn locations is actually the network lifetime.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 104

0

10

20

30

40

50

60

70

80

90

100

Number of Rounds

Num

ber

of a

live

node

s

SS−SRPSC40−SRPSC20−SRPSC10−SRPCC−SRPCL−SEPSEP

Figure 5.1: Alive nodes

5.2 Simulation Experiments

For simulation results we presented a square field with randomly (uniformly) de-

ployed nodes, the trajectories for the SM will be discussing here. Following few

patterns of SM, we are studying the behaviour of throughput and ratio of nodes

to be dead. Applied a circular pattern of SM in the square field with different

radii, 10m, 20m and 40m. Then square inside a square field and circle inside

the circular field. Finally we compare these results with SEP [1], and CL-SEP.

network parameters are defined in table 1.

Comparing the graphs of variants of SRP, SM in SS-SRP and CC-SRP in fig. 5.1.

First node of SS-SRP dies around 3000 round and in CC-SRP at 4100 node. Last

node of CC-SRP dies at 45000 rounds and in SS-SRP at 35000 rounds. Dimensions

of the square field are 100m×100m SM trajectory is also square and its is moving

along the perimeter of 50m× 50m square which is exactly inside the WSN square

field. Sensing range of a sink is 35.35m because of the far most nodes on the

corner of the field. SM is gathering data efficiently in this way. The nodes placed

near to the Square trajectory of sink are maximum time exposed to the sensing

32

range of the sink and stay in awake mode for the longer time as compare to the

nodes placed outside. Nodes far from square trajectory get sufficient time to stay

in sleep mode because the come in sensing range of MS for very short period of

time.

MS

Figure 5.2: Circular sink mobility in circular field

25 m

25 m

50 m 100 m

100 m

MS

Figure 5.3: Squared sink mobility in squared field

100m

100m

MS

MS

MS

Figure 5.4: Circular sink mobility in squared field

If the awake mode is unnecessary long, it causes an increase in maximum energy

consumption per node. Thats why initially node placed near the path of sink die

earlier. Mobile sink collects data directly from sensor nodes by one hop commu-

nication known as direct contact data collection. Data may be retransmitted by

the mobile sink if needed. This technique minimizes energy consumption between

sensors for communication since sensors do not need to forward messages for each

other. These two variants are performing well as compare to others because the

SM is receiving maximum data due to the well balanced trajectories. Every sensor

node in both fields is directly transmitting sensed data to SM. An other simula-

tion experiment is done by using SM in a circular trajectory with in a square field.

33

Three variants are compared in terms of alive nodes and results are shown in fig.

5.1. and their trajectories are shown in fig. 5.2, 5.3 and 5.4 SM is varied in a

circle with 3 different radii (40m, 20m, 10m). Observe that, death of first node of

SC40-SRP lies in range of 3700th round. This technique beats SC20-SRP, SC10-

SRP, CL-SEP and SEP in stability period and network lifetime. This is because

in SC40-SRP sink is moving at radius of 40 at circular path inside the square field

and having sensing range defined as to be 40 because maximum distance from

trajectory to corner of the field is 31.35 and distance from trajectory to the center

point of the field is 40. So, even if sensor is at corner or at the center of the field

would come in sensing range of mobile sink and whole network would be covered.

However, if we talk about SC20-SRP it performs poor than SC40-SRP, because

in this sink is moving at radius of 20 in circular trajectory inside the square field

with sensing range of 51.35. So, nodes inside the trajectory always exposed to mo-

bile sink because of always coming in the sensing range of mobile sink and drains

their energy earlier. Nodes outer to the circular trajectory also feels maximum

transmission distance when comes in sensing range hence consume more energy

as compares to SC40-SRP.

Parameters

for energy

model

Fraction

and energy

of advance

nodes

Transmission distance

defined by energy model

Random deployment of nodes

No

Rounds start

NoInitialization of dead ,

advance dead, and normal

dead nodes

No

Transmission of

packets to mobile

BS

distance

<=range

Sleep mode

Begin

Initialization counter

for first dead node

Increment in

advance dead

If energy <0

If dead node is

normal

If dead node is

advance

Increment in dead

Increment in

normal dead

Yes

Yes

No

Yes

Rounds=rounds+1

Yes

Rounds<Rmax End

No

Checking energy

of every node

Yes

No

Field

Dimension

Figure 5.5: Flow Chart of SRP

If we compare SC10-SRP with SC20-SRP whose topology is shown in fig. 5.4.

Simulation results in fig. 5.1 shows that SC20-SRP beats SC10-SRP and in sta-

bility period and network lifetime because in SC10-SRP sink is moving in circular

trajectory with radius 10. Sensing range of the mobile sink here in this scenario

34

is defined as 61.71, as sensing range is very large and nodes within the circu-

lar trajectory remain alive and do not go in sleeping mode hence consume their

available energy rapidly and die earlier. However, nodes placed outer the circular

trajectory will feel maximum transmission distance because here sink is moving

at radius of 10. As maximum energy is used for long range transmissions because

all nodes send their sensed data directly to mobile sink and hence network nodes

placed outer the sink trajectory consumes larger amount of energy in their data

transmission session and die earlier. Hot spot problem arises in multi-hop com-

munication with static sink. This results in making the network disconnected,

though most of the sensors are still alive and working. From our simulations we

can see that CL-SEP outperforms SEP, as SEP is clustered routing protocol and

CL-SEP is clusterless routing protocol. CL-SEP performs better because there

is no issue of relay nodes and nodes just do single transmission whereas in SEP

nodes first send their data to CHs and then CHs send the aggregated data to sink.

Hence consuming more energy in double transmission. We can see the flow of our

proposed SRP from above flowchart shown in fig. 5.5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

x 104

0

0.5

1

1.5

2

2.5

3x 10

5

Number of Rounds

Thr

ough

put (

bits

)

SS−SRPSC40−SRPSC20−SRPSC10−SRPCC−SRPCL−SEPSEP

Figure 5.6: Throughput

Fig. 5.6 shows the throughput comparison of above discussed techniques, we can

see that CC-SRP has highest throughput among all techniques. Its throughput

increases up to 2.6 × 105 at 20,000 rounds and then remain constant. Whereas

SS-SRP has second highest throughput in above discussed techniques its through-

put increases up to 2.5× 105 at 20,000 rounds and then goes constant. However,

throughput of SC40-SRP, SC20-SRP and SC10-SRP we can see from fig that

SC40-SRP has highest throughput of 2.4× 105 till 15,000 rounds. Throughput of

SC20-SRP is 2.1× 105 till 8,000 rounds. Whereas, SC10-SRP has little bit higher

throughput of 2.2×105 at 5000 rounds than SC20-SRP. CL-SEP has higher stabil-

ity period and network life than conventional SEP because of direct transmission

of data to static sink at center of the field. CL-SEP uses just single transmission

whereas in conventional SEP a clustering technique is used in which the sensor

nodes first send their data to associated CHs and then data is further forwarded

35

to static sink via CHs hence consume more energy in double transmission. So,

throughput of CL-SEP is 1.3×105 at 5000 rounds whereas 0.2×105 packet received

by sink in SEP is at 5000 rounds and become constant.

36

Chapter 6

Conclusion

SEP introduces heterogeneous WSNs in which nodes have different energy levels.

Weighted election probabilities of each node to become CH in SEP is based on

residual energy. We proposed Hierarchal SEP which is also heterogenous proto-

col with two levels of clustering hierarchy to minimize the transmission distance

between CH and sink to prolong the effective network life-time. It is also based

on weighted election probabilities of each node to become CH. Simulation re-

sult shows that HSEP maximizes the stability period compared to (and that the

average throughput is greater than) the one obtained using current clustering pro-

tocols. From our simulations we clearly see that HSEP outperforms DEEC, SEP,

ESEP and LEACH in stability period and network life.

In this paper we have implemented sink mobility in four different scenario in tunnel

(rectangular area) and compare it with T-SEP. We see that sink mobility effects

network life time and stability period in TRP variants. We have discussed four

different patterns of sink mobility in TRP and got improved results then conven-

tional T-SEP. We have also implemented SRP with four different sink mobility

patterns and got good results then conventional SEP. As sink is mobile in different

trajectories, so, it is collecting data directly and nodes after transmission go to

sleep mode and save energy till the sink again come in the range after completing

a round. Hence experimental results show that MS can enhance network life time

and stability period of routing protocol. We have also implemented same four

sink mobility patterns in squared region and observed that S-SEP performs better

than C-SEP, B-SEP, D-SEP and conventional SEP in network lifetime. Spiral

mobility pattern of sink in sensing field improves network lifetime because sink

moves almost in complete region and the transmission distance between nodes and

sink reduces hence save energy. Advance nodes stay alive for longer time and take

extra energy than normal nodes and also taking advantage of sink mobility.

37

The use of mobile sink in larger networks is necessary in order to cover large areas

and minimize the energy consumption in large transmission distances. In this

paper we proposed energy efficient sink mobility technique in squared regions to

prolong the network lifetime and stability period. Our approach uses different

mobility patterns and compared their results in maximization of network life and

stability period and we observed that CC-SRP out performs all discussed sink

mobility techniques. Our proposed scheme is only applicable for delay tolerant

networks and applications (DTN). simulation results have shown that CC-SRP

significantly prolongs the network life time and stability period when the sink or

MS is moving in the circular path in side the circular field at an optimized radius.

Moreover the plan of our future work is to investigate further on more elaborated

approaches for optimal multiple sink placement in WSN.

38

References

[1] G. Smaragdakis, I. Matta, and A. Bestavros, “Sep: A stable election proto-

col for clustered heterogeneous wireless sensor networks,” tech. rep., Boston

University Computer Science Department, 2004.

[2] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient

communication protocol for wireless microsensor networks,” in System Sci-

ences, 2000. Proceedings of the 33rd Annual Hawaii International Conference

on, pp. 10–pp, IEEE, 2000.

[3] F. Aderohunmu and J. Deng, “An enhanced stable election protocol (sep) for

clustered heterogeneous wsn.,” tech. rep., Discussion Paper Series, 2009.

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

clustering algorithm for heterogeneous wireless sensor networks,” Computer

communications, vol. 29, no. 12, pp. 2230–2237, 2006.

[5] A. Khan, N. Javaid, U. Qasim, Z. Lu, and Z. Khan, “Hsep: Heterogeneity-

aware hierarchical stable election protocol for wsns,” arXiv preprint

arXiv:1208.2335, 2012.

[6] W. Guo, W. Zhang, and G. Lu, “Pegasis protocol in wireless sensor network

based on an improved ant colony algorithm,” in Education Technology and

Computer Science (ETCS), 2010 Second International Workshop on, vol. 3,

pp. 64–67, IEEE, 2010.

[7] O. Younis and S. Fahmy, “Heed: a hybrid, energy-efficient, distributed clus-

tering approach for ad hoc sensor networks,” Mobile Computing, IEEE Trans-

actions on, vol. 3, no. 4, pp. 366–379, 2004.

[8] B. Elbhiri, R. Saadane, S. El Fkihi, and D. Aboutajdine, “Developed dis-

tributed energy-efficient clustering (ddeec) for heterogeneous wireless sensor

networks,” in I/V Communications and Mobile Network (ISVC), 2010 5th

International Symposium on, pp. 1–4, IEEE, 2010.

39

[9] P. Saini and A. Sharma, “E-deec-enhanced distributed energy efficient clus-

tering scheme for heterogeneous wsn,” in Parallel Distributed and Grid Com-

puting (PDGC), 2010 1st International Conference on, pp. 205–210, IEEE,

2010.

[10] A. Manjeshwar and D. Agrawal, “Teen: a routing protocol for enhanced effi-

ciency in wireless sensor networks,” in 1st International Workshop on Parallel

and Distributed Computing Issues in Wireless Networks and Mobile Comput-

ing, vol. 22, 2001.

[11] B. Bakr and L. Lilien, “Extending wireless sensor network lifetime in the

leach-sm protocol by spare selection,” in Innovative Mobile and Internet Ser-

vices in Ubiquitous Computing (IMIS), 2011 Fifth International Conference

on, pp. 277–282, IEEE, 2011.

[12] A. Manjeshwar and D. Agrawal, “Apteen: A hybrid protocol for efficient

routing and comprehensive information retrieval in wireless sensor networks,”

in Proceedings of the 16th International Parallel and Distributed Processing

Symposium, p. 48, 2002.

[13] P. Saini and A. Sharma, “Energy efficient scheme for clustering protocol pro-

longing the lifetime of heterogeneous wireless sensor networks,” International

Journal of Computer Applications IJCA, vol. 6, no. 2, pp. 30–36, 2010.

[14] Z. Wang, S. Basagni, E. Melachrinoudis, and C. Petrioli, “Exploiting sink

mobility for maximizing sensor networks lifetime,” in System Sciences, 2005.

HICSS’05. Proceedings of the 38th Annual Hawaii International Conference

on, pp. 287a–287a, IEEE, 2005.

[15] W. Heinzelman, A. Murphy, H. Carvalho, and M. Perillo, “Middleware to

support sensor network applications,” Network, IEEE, vol. 18, no. 1, pp. 6–

14, 2004.

[16] I. Chatzigiannakis, A. Kinalis, and S. Nikoletseas, “Sink mobility protocols

for data collection in wireless sensor networks,” in Proceedings of the 4th

ACM international workshop on Mobility management and wireless access,

pp. 52–59, ACM, 2006.

[17] S. Basagni, A. Carosi, E. Melachrinoudis, C. Petrioli, and Z. Wang, “Con-

trolled sink mobility for prolonging wireless sensor networks lifetime,” Wire-

less Networks, vol. 14, no. 6, pp. 831–858, 2008.

40

[18] J. Luo, J. Panchard, M. Piorkowski, M. Grossglauser, and J. Hubaux, “Mo-

biroute: Routing towards a mobile sink for improving lifetime in sensor net-

works,” Distributed Computing in Sensor Systems, pp. 480–497, 2006.

[19] Y. Shi and Y. Hou, “Theoretical results on base station movement problem

for sensor network,” in INFOCOM 2008. The 27th Conference on Computer

Communications. IEEE, pp. 1–5, IEEE, 2008.

[20] A. Chakrabarti, A. Sabharwal, and B. Aazhang, “Using predictable observer

mobility for power efficient design of sensor networks,” in Information Pro-

cessing in Sensor Networks, pp. 552–552, Springer, 2003.

[21] Y. Yun and Y. Xia, “Maximizing the lifetime of wireless sensor networks

with mobile sink in delay-tolerant applications,” Mobile Computing, IEEE

Transactions on, vol. 9, no. 9, pp. 1308–1318, 2010.

[22] T. Qureshi, N. Javaid, M. Malik, U. Qasim, and Z. Khan, “On performance

evaluation of variants of deec in wsns,” arXiv preprint arXiv:1208.2401, 2012.

[23] M. Aslam, T. Shah, N. Javaid, A. Rahim, Z. Rahman, and Z. Khan, “Ceec:

Centralized energy efficient clustering a new routing protocol for wsns,” in

Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2012 9th

Annual IEEE Communications Society Conference on, pp. 103–105, IEEE,

2012.

[24] Q. Xie, E. Perez-Cordero, and L. Echegoyen, “Electrochemical detection of

c606-and c706-: Enhanced stability of fullerides in solution,” Journal of the

American Chemical Society, vol. 114, no. 10, pp. 3978–3980, 1992.

41