geneous network and heterogeneous networks [1-2], [3-6]. homogeneous networks contain sensor nodes...

CEEC: Centralized Energy Efficient Clustering

Routing Protocol for Wireless Sensor Networks

By

Mr. Muhammad Aslam

CIIT/FA10-REE-008/ISB

MS Thesis

In

Electrical Engineering

COMSATS Institute of Information Technology

Islamabad – PakistanSpring, 2012

CEEC: Centeralized Energy Efficient Clustering


A Thesis presented to


In partial fulfillment

of the requirement for the degree of

MS (Electrical Engineering)

By

Mr. Muhammad Aslam


Spring, 2012


ii

Engineering).

A post Graduate Thesis submitted to Department of Electrical Engineering as

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

(Electrical

Supervisor:

Dr. Nadeem Javaid,

Assistant Professor,

Department of Electrical Engineering,

COMSATS Institute of Information Technology (CIIT)

Islamabad Campus

June, 2012

Name Registeration Number

Mr. Muhammad Aslam CIIT/FA10-REE-008/ISB


Routing Protocol For Wireless Sensor Network

iii

Final Approval

This thesis titled



By

Mr. Muhammad Aslam


Has been approved

For the COMSATS Institute of Information Technology, Islamabad

External Examiner: __________________________________

Supervisor: ________________________

Dr. Nadeem Javaid /Assistant professor

Department of Electrical Engineering

Islamabad Campus

Co-supervisor: ________________________

Dr. Safdar H.Bouk / Assistant professor


Islamabad Campus

HoD: ________________________

Dr. Shafayat Abrar / Associate professor


Islamabad Campus

iv

Declaration

I Mr. Muhammad Asam, CIIT/FA10-REE-008/ISB hereby declare that I have

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

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

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

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

under the plagiarism rules of the HEC.

Date: ________________

________________

Mr. Muhammad Aslam


v

Certificate

It is certified that Mr. Muhammad Aslam, CIIT/FA10-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 award of MS degree.

Date: _________________

Supervisor: ____________________

Dr. Nadeem Javaid /Assistant professor


CIIT Islamabad Campus

Head of Department:

____________________________

Dr. Shafayat Abrar/Associate professor

HoD Electrical Engineering

vi

DEDICATIONDedicated to my parents and friends

vii

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. Also not forgetting a father, a teacher, Dr.

Safdar H.Bouk, who in every way offered much assistance before, during and at

completion stage of this thesis work. I deeply appreciate your support. Thank you

so much.

Mr. Muhammad Aslam


viii

ABSTRACT

Scalable and energy-aware routing protocol is very essential for Wireless

Sensor Networks (WSNs) in order to increase the network lifetime. Nodes of WSNs

have limited battery resources and it is observed that execution of heterogeneity-

aware clustering routing protocols in terms of energy utilizes such resources

effectively. Heterogeneity-aware clustering routing protocols enhance stability and

network lifetime of WSNs as compared to flat, location-based and conventional

homogeneous-aware clustering routing protocols. Heterogeneous-aware clustering

routing protocols is also facing some challenges like, limited scalability of the

network, un-reliable distributed algorithm for selection of Cluster-Heads (CHs) and

randomized deployment policy of nodes. In this paper, we propose a Centralized

Energy Efficient Clustering (CEEC), a heterogeneity-aware clustering protocols for

WSNs to cope with these challenges. Operation of CEEC is based upon an advance

central control algorithm, in which Base Station (BS) is responsible for selection of

optimal numbers of CHs. BS selects CHs on the basis of value of residual energy,

average energy of network and mutual distance between nodes and itself. Execution

of CEEC provides scalability, significant stability, extended network lifetime and

better control over network operation. In order to enhance scalability of the network,

CEEC can be executed in multi-level heterogeneous networks. But initially, we

design and simulate CEEC for three level heterogeneous networks. In MCEEC, we

design an advance heterogeneous network model, in which whole network area is

divided into three equal regions and nodes of same energy level are scattered in one

region. Furthermore, nodes can only associate to their own region’s cluster-heads. We

deploy the network’s nodes in ascending order of energy level from the position of

BS. We also proposed an extension of CEEC as Multi-hop CEEC. We adopt multi-

hop inter-cluster communication for MCEEC. We simulate our proposed CEEC and

MCEEC routing protocol using MATLAB. Results describe that CEEC and MCEEC

yield maximum scalability, network lifetime, stability period and throughput as

compare to other clustering routing protocols.

ix

Table of Contents

1 Introduction 2

1.1 Introduction of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 2

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

1.3 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.5 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.6 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.7 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.8 Organization of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Related Work 7

2.1 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Sensor Node Architecture . . . . . . . . . . . . . . . . . . . 7

2.2 Classification of Routing Protocol . . . . . . . . . . . . . . . . . . 8

2.2.1 Clustering Routing Protocol . . . . . . . . . . . . . . . . . . 9

2.2.2 Location-based Routing Protocols . . . . . . . . . . . . . . . 9

2.2.3 Flat Routing Protocols . . . . . . . . . . . . . . . . . . . . 9

2.3 Key Problems of routing protocols . . . . . . . . . . . . . . . . . . 9

2.4 Heterogeneous Vs Homogeneous Networks . . . . . . . . . . . . . . 10

2.5 Energy Efficient Clustering Routing Protocols . . . . . . . . . . . . 11

2.5.1 Clustering Advantages . . . . . . . . . . . . . . . . . . . . . 11

2.5.2 Data aggregation Advantages . . . . . . . . . . . . . . . . . 11

2.6 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.6.1 Low Energy Adaptive Clustering Hierarchy (LEACH) . . . . 12

2.6.2 LEACH-Centralized (LEACH-C) . . . . . . . . . . . . . . . 14

2.6.3 Solar-aware Low Energy Adaptive Clustering Hierarchy(sLEACH) 15

2.6.3.1 Solar-aware Centralized LEACH . . . . . . . . . . 15

2.6.3.2 Solar-aware Distributed LEACH . . . . . . . . . . 15

2.6.4 Energy Low-Energy Adaptive Clustering Hierarchy (E-LEACH) 16

2.6.5 Multi-hop LEACH . . . . . . . . . . . . . . . . . . . . . . . 16

x

2.6.6 Mobile-LEACH (M-LEACH) . . . . . . . . . . . . . . . . . . 18

2.6.7 LEACH-selective cluster (LEACH-SC) . . . . . . . . . . . . 18

2.6.8 SEP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.6.9 DEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.7 Comparison of Reviewed Routing Protocols . . . . . . . . . . . . . 20

3 Proposed Model of CEEC 24

3.1 Proposal of CEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2 Advance Heterogeneous Network Model for CEEC . . . . . . . . . . 24

3.3 First Order Radio Model . . . . . . . . . . . . . . . . . . . . . . . . 26

3.4 Proposed Model of CEEC . . . . . . . . . . . . . . . . . . . . . . . 28

3.4.1 Network Settling Time (NST) . . . . . . . . . . . . . . . . . 28

3.4.2 Network Transmission Time (NTT) . . . . . . . . . . . . . . 30

3.5 Simulation results of CEEC performance . . . . . . . . . . . . . . . 31

3.5.1 Results for first Scenario . . . . . . . . . . . . . . . . . . . . 33

3.5.2 Results for second Scenario . . . . . . . . . . . . . . . . . . 35

4 Proposed Model of MCEEC 39

4.1 Proposal of MCEEC . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2 Proposal of MCEEC . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2.1 Heterogeneous Network Model for MCEEC . . . . . . . . . . 39

4.2.2 Radio Energy Characteristics . . . . . . . . . . . . . . . . . 41

4.3 Proposal of MCEEC Routing protocol . . . . . . . . . . . . . . . . 42

4.3.1 Network Settling Time (NST) . . . . . . . . . . . . . . . . . 43

4.3.2 Network Transmission Time (NTT) . . . . . . . . . . . . . . 45

4.4 Simulation Results and Discussion of MCEEC performance . . . . . 48

4.4.1 Results for first Scenario . . . . . . . . . . . . . . . . . . . . 49

4.4.2 Results for second Scenario . . . . . . . . . . . . . . . . . . 52

5 Conclusion 56

5.1 Conclusion and Future work . . . . . . . . . . . . . . . . . . . . . . 56

References 57

xi

List of Figures

2.1 Sensor Node’s Architecture . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Classification of Routing Protocol . . . . . . . . . . . . . . . . . . . 8

2.3 Clustering topology of LEACH . . . . . . . . . . . . . . . . . . . . 13

2.4 Clustering topology of M-LEACH . . . . . . . . . . . . . . . . . . . 17

2.5 Combined Flow chart of clustering protocols . . . . . . . . . . . . . 19

3.1 Advance Heterogeneous Network Model . . . . . . . . . . . . . . . . 25

3.2 Clustering Topology in Heterogeneous Network Model . . . . . . . . 26

3.3 Radiomodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.4 Flow chart of CEEC operation . . . . . . . . . . . . . . . . . . . . . 32

3.5 Alive Nodes for 100m× 100m Network with 100 nodes . . . . . . . 33

3.6 Dead Nodes for 100m× 100m Network with 100 nodes . . . . . . . 34

3.7 Cluster-heads per round . . . . . . . . . . . . . . . . . . . . . . . . 34

3.8 Packet to BS Nodes for 100m× 100m Network with 100 nodes . . . 35

3.9 Alive Nodes for 210m× 210m Network with 120S nodes . . . . . . 35

3.10 Dead Nodes for 210m× 210m Network with 120S nodes . . . . . . 36

3.11 Cluster-heads per round for 210m× 210m Network with 120S nodes 36

3.12 Packet to BS for 210m× 210m Network with 120S nodes . . . . . . 37

3.13 Stability period for 210m× 210m Network with 120S nodes . . . . 37

4.1 Advance Heterogeneous Network Model for MCEEC execution . . . 40

4.2 Clustering Topology of MCEEC . . . . . . . . . . . . . . . . . . . . 40

4.3 Flow chart of MCEEC operation . . . . . . . . . . . . . . . . . . . 48

4.4 Alive Nodes for 100m× 100m Network with 100 nodes in MCEEC . 50

4.5 Dead Nodes for 100m× 100m Network with 100 nodes in MCEEC . 50

4.6 Total network lifetime with stability and instability period in MCEEC 51

4.7 Cluster-heads per round in MCEEC . . . . . . . . . . . . . . . . . . 51

4.8 Packet to BS Nodes for 100m × 100m Network with 100 nodes in

MCEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.9 Alive Nodes for 210m× 210m Network with 120S nodes in MCEEC 52

4.10 Dead Nodes for 210m× 210m Network with 120S nodes in MCEEC 53

xii

4.11 Cluster-heads per round for 210m×210m Network with 120S nodes

in MCEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.12 Packet to BS for 210m× 210m Network with 120S nodes in MCEEC 54

4.13 Stability period for 210m×210m Network with 120S nodes in MCEEC 54

xiii

List of Tables

2.1 Performance comparison of hierarchical routing protocols . . . . . . 21

3.1 Radio Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.2 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.1 Radio Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.2 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 49

xiv

Chapter 1

Introduction

1

Chapter 1

Introduction

1.1 Introduction of Thesis

Recent advancement in micro-electronics technology facilitates sensor designer

to develop low power, small size and low price sensors [1-2]. These sensors can be

deployed in extensive amount according to requirement of application. Thousands

of sensors can be deployed in order to achieve fault-tolerant and high quality of

network. Individually sensor nodes have limited ability of sensing, on-board sig-

nal processing and wireless communication. Sensor nodes can communicate to

both network’s nodes and BS. Sensor nodes have multiple adjustable transmission

power levels in order to communicate to nodes at different distance with suitable

transmission power level. WSNs are extensively used for both military and civil

applications [3-4]. A wide-range of applications has been provided by WSN, some

of popular applications are environmental monitoring, industrial sensing, infras-

tructure protection, battlefield surveillance, and smart offices.

Routing is one of the main challenges faced by WSNs. Complexity in routing

protocol is due to dynamic nature of nodes, limited battery life, computational

operating cost, no conventional addressing scheme, self-organization and scalabil-

ity requirement of sensor nodes [2-5]. Sensor nodes have limited battery. Usually

their battery cannot be replaced and recharged due to area of their deployment,

So, the network lifetime depends upon initial battery capacity of sensor nodes. A

careful management of resources is needed to increase the lifetime of the WSNs.

Number of routing protocols has been proposed for WSNs. These protocols are

classified into flat; clustering and location based routing protocols. In clustering

routing protocols whole network is divided into multiple clusters and one node in

each cluster acts as cluster-head. Non-cluster-head nodes send their data to CHs

2

and CHs forward that data to BS after aggregating. Clustering routing protocols

have been proved more energy efficient as compare to flat and location based

routing protocols [6-7].

Two types of clustering network has been designed for WSNs, called homo-

geneous network and heterogeneous networks [1-2], [3-6]. Homogeneous networks

contain sensor nodes with same sensing, radio characteristics and energy level. On

the other hand nodes in heterogeneous networks have different sensing and energy

levels. In cost-benefit comparison heterogeneous clustering networks are more

beneficiary and stable [14]. In recent research on energy efficient routing pro-

tocol has targeted the heterogeneous network. Stable Election Protocol (SEP),

Enhanced Stable Election Protocol (E-SEP), Distributed Energy-Efficient Clus-

tering (DEEC), and Threshold Distributed Energy-Efficient Clustering (T-DEEC)

heterogeneity-aware routing protocols have been proposed for heterogeneous net-

works [3-6]. These routing protocols have some limitation due to their design. In

this paper thesis, we propose CEEC and MCEEC routing protocol to address is-

sues faced by previous clustering routing protocols. Detail of our proposed model

is provided in section IV.

1.2 Motivation

The motivation behind this thesis is to design a heterogeneous-aware clustering

routing protocol that can increase the lifetime of the network by reducing the

overall energy consumption in the system and can increase the throughput of the

network.

1.3 Objective

The objective of this thesis is to design and develop a new heterogeneous-aware

clustering routing protocol for WSN that has the following characteristics:

• The main objective of our research is to develop a routing protocol that can

centrally cope with heterogeneity of network in terms of energy level

• To design a new heterogeneity-aware centralized clustering routing protocol

that can handle three levels heterogeneity of the nodes.

• The protocol would minimize the routing overhead and energy consumption

for route discovery and maintenance.

3

• To develop a protocol that can be operated on available devices.

• Our proposed routing would provide longer network lifetime, stability and

throughput as compared to existing routing protocol for WSNs.

• To develop a protocol that can facilitate the nodes with multi-hop commu-

nication.

The main features of the proposed routing protocol can be summarized as energy

efficiency, scalability and practical implementation.

1.4 Problem Statement

In previous research work, SEP, E-SEP, and DEEC are designed for heteroge-

neous networks [2-4]. But these protocols do not provide any network deployment

planning. Because of this, nodes with extra energy (advance nodes which have to

become cluster-heads more frequently) are not uniformly dispersed throughout the

network. Furthermore, these protocols use distributed clustering algorithm that

increase computational overhead on all nodes. Another problem is that, optimum

number of cluster-heads is also not guaranteed through distributed algorithm. We

propose CEEC routing protocol to address these issues. In CEEC, Base Station

(BS) centrally selects optimum number of cluster-heads. CEEC enhances the

stability and network lifetime. We simulate our proposed routing protocol us-

ing MATLAB. The results of simulations verify that our proposed model provide

better network life time as compare to LEACH, SEP, E-SEP and DEEC. Next

section describes the CEEC’s network heterogeneous network model for our pro-

posed protocol. We also propose MCEEC in this thesis to increase the scalability

of the network

1.5 Scope

The scope of this research is to define and develop a new heterogeneous-awar

energy efficient routing protocol that would bring more stability and network

lifetime

4

1.6 Research Methodology

We initially did an extensive study of the existing routing protocols which are

proposed for WSNs. From the literature review we concluded that clustering

routing protocols are more energy efficient as compared to flat and location based

routing protocols. After that, we study recently proposed heterogeneous-awar

protocols. We finally selected this area of research. After extensive bibliogra-

phy, we propose a routing protocol [Chapter 3] for heterogeneous sensor network.

We simulated our proposed model in MATLAB. Results of multiple simulations

encouraged our proposed protocol with significant improvements.

1.7 Contribution

We make an important contribution by developing a new routing protocol. Our

contribution basically provide enhancement to SEP, E-SEP and DEEC routing

protocols. Multi-hop communication is also main feature of our proposed model.

Network deployment is challenging problem in WSNs, we also tried to handle this

issue to some extent. Another contribution of our thesis is to propose a central

routing protocol to obtain better control our network operation. To increase scal-

ability of the network, we develop our protocol that allows multi-hop inter-cluster

communication.

1.8 Organization of thesis

Chapter 2 provides an introduction to wireless sensor networks and background

knowledge of our thesis work. In Chapter 3, we present the propose models of

CEEC and MCEEC routing protocols. Simulations results and discussions are

given in chapter 4. The last chapter 5 contains the reference of our reviewed and

related literature.

5

Chapter 2

Related Work

6

Chapter 2

Related Work

2.1 Wireless Sensor Networks

A number of technologies currently exist to provide users with wireless con-

nectivity. The challenges in the hierarchy of: detecting the relevant quantities,

monitoring and collecting the data, assessing and evaluating the information, for-

mulating meaningful user displays, and performing decision-making and alarm

functions are enormous. The information needed by smart environments is pro-

vided by Wireless Sensor Networks, which are responsible for sensing as well as for

the first stages of the processing hierarchy. The security has become a big task in

wired and wireless networks. Sensor networks are self-organized networks, which

makes them suitable for dangerous and harmful situations, but at the same time

makes them easy targets for attack. For this reason we should apply some level of

security so that it will be difficult to be attacked, especially when they are used

in critical applications. Wireless Sensor Networks (WSNs) are special kinds of

Ad hoc networks that became one of the most interesting areas for researchers to

study. The most important property that affects these types of network is the lim-

itation of the available resources, especially the energy. This organization provides

some energy saving, and that was the main idea for proposing this organization.

2.1.1 Sensor Node Architecture

The four basic components of each and every node are power source, process-

ing unit, sensing unit and transceiver. Some sensor nodes also contain optional

components like location finding system (GPS), Mobilizer and power generator.

The below Fig 2.1 show the basic components of a sensor node.

7

Power Unit

Temperature

Sensing

Pressure

Sensing

Analog to

Digital

convertor

Processor

Storage

Transceiver

Global Positioning System (GPS)

Power

Generator

Sensing Unit Processing UnitMobilizer

Figure 2.1: Sensor Node’s Architecture

An optional power generator can be used to support the power unit; solar cells

can also be used for this purpose.

The processing unit consists of a processor and memory. This unit is responsible

for managing the tasks of sensor unit. The sensing unit is generally consists

of a Sensor and Analogue to Digital Convertor (ADC). The ADC converts the

analogue data to digital data so that node can process it before transmitting. The

Transceiver connects the node to the network either through radio frequency (RF)

or optical communication such as infrared. The optional location finding system

may have a low power Global Positioning System (GPS). Mobilizer is used to

enable the node movement, if mobility is required for a node to perform its task.

All of these components must be fitted in a smaller module like matchbox.

2.2 Classification of Routing Protocol

Energy constraint is a major issue in case of WSN. We can minimize it up-to

certain extent by use of efficient routing protocol. Different protocols have been

Routing Protocols in

Network

Structure

Operation Based

Flat-

Networks

Hierarchi

calLocation- Based

Negotiation Multi-PathQuery-Based

QoS-Based

Coherent

Figure 2.2: Classification of Routing Protocol

designed and implemented for WSN but still the problem of energy efficiency is

an open issue. Fig 2.2 show protocols classification; these protocols are further

divided into sub-categories. Routing protocols have different structures. Based on

8

protocol structure we divide these protocols as flat, hierarchal and location-based.

A protocol that falls under any of these categories having its design constraints

with some pros and cons related to the network structure.

2.2.1 Clustering Routing Protocol

In hierarchical networks nodes form a hierarchy and data is transmitted with

that hierarchy till it reaches the sink. Nodes are divided into clusters and cluster-

heads are selected. Nodes that sense or collect data forward it to its respective

cluster heads (CHs) which are then sent to the Base Station (BS) [1,2].

2.2.2 Location-based Routing Protocols

In this type of networks nodes are addressed by their location. These protocols

are energy efficient as it allows the nodes to shift into sleep-mode in idle time.

Geographic Adaptive Fidelity (GAF) and Geographic and Energy Aware Routing

(GEAR) are the examples of such protocols.

2.2.3 Flat Routing Protocols

All nodes play the same role in flat routing protocols i.e. sense data and trans-

mit it to the sink.

2.3 Key Problems of routing protocols

To design an ancient algorithm for wireless sensor network, the following issues

must be considered:

• 1. Sensor nodes are battery-operated and most often constrained in their

energy due to the inability of recharging the nodes. Hence, one of the most

important bottlenecks in the protocol design is the energy consumption. The

design should be able to balance network life-time and other heuristics for

accuracy of results [15].

• 2. Sensor network should be adaptive and sensitive to the dynamic environ-

ment where they are deployed.

9

• 3. Since nodes are battery-powered and communications are radio-based,

nodes are more susceptible to failures. The information collected by in-

dividual node should be aggregated to give more accurate and reliable re-

sults. Sensor network should be reliable and be able to provide relevant data

through information gathering techniques.

• 4. The hardware design should incorporate methods to conserve energy using

low powered processors and the system software should use minimal power

as possible.

• 5. A sensor network algorithm should be distributed and self-organizing,

since WSN is infrastructure-less.

• 6. The security of the network should also be considered. An intrusion might

defeat the entire purpose of the network system.

• 7. Scalability is another important factor to be considered when designing a

topology for WSN. Some applications might require hundreds or thousands

of nodes to monitor a trend intermittently [17].

• 8. Sensor network should be able to share communication resources anciently

and Support real-time communication while providing a guaranteed quality

of service [19-20].

All the attributes mentioned above reacts the role and importance of septic lay-

ers Of the OSI (Open System Interconnection) model in wireless communication,

in which we will discuss briefly in the next section.

2.4 Heterogeneous Vs Homogeneous Networks

In [9], a comparative study of homogeneous vs. heterogeneous clustered sen-

sor network” was carried out. The authors in [10] described the cost benefit of

deploying a heterogeneous system to a homogeneous system. A homogeneous sen-

sor networks can be defined as a network consisting of identical nodes with same

energy level, processing capabilities, and sensing range. On the other hand, het-

erogeneous sensor network consist of sensor nodes with different abilities, such as

different energy level, sensing range and computation power. Most heterogeneous

network may have varying level of the aforementioned abilities depending on the

deployment scenario. This thesis considers both energy homogeneous and hetero-

geneous networks. This thesis examines an energy heterogeneous network that

has the same kind of sensor nodes abilities but differs in energy levels.

10

2.5 Energy Efficient Clustering Routing Proto-

cols

Clustering routing protocols have provided much longer network lifetime in con-

trast of flat and location based routing protocols. In clustering routing protocols

whole network is divides into multiple clusters and one node in each cluster acts

as cluster-head. Normal nodes send their data to cluster-heads and cluster-heads

forward that data to BS after aggregating.

2.5.1 Clustering Advantages

Clustering have many advantages, one of the main advantages is that it reduces

the number of transmission. Clustering protocols allow non-cluster-head nodes to

communicate at smaller distance to save their energy. Clustering provides load

balancing for network traffic.

2.5.2 Data aggregation Advantages

Various kinds of data aggregation techniques have been used in different lit-

eratures. In [14] a new data aggregation model that involved data compression

was developed, originally inspired by the authors of [13]. The authors of [39]

highlighted different data aggregation schemes: in-network, grid-based and hybrid

data aggregation. A discussion on the impacts of data aggregation in WSN was

carried out in [21]. However, the most commonly used data aggregation such as

in LEACH and LEACH-like protocols assumes

a perfect aggregation in which multiple packets are sent from all cluster mem-

bers to their respective cluster-head but only a single packet is forwarded to the BS.

By definition, data aggregation is referred to as gathering of multiple data pack-

ets by using spatial correlation to reduce the received data into a single packet.

Thus, in the context of the experiments performed in this thesis, a perfect data

aggregation EDA model is assumed as used in LEACH and SEP.

11

2.6 Literature Review

2.6.1 Low Energy Adaptive Clustering Hierarchy (LEACH)

LEACH is one of the earliest hierarchical routing protocol used for WSNs to

increase the lifespan of network. Sensor nodes organize themselves into clusters

in LEACH routing protocol. LEACH performs self-organizing and re-clustering

functions for every round [1]. In every cluster one of sensor nodes acts as CH

and remaining sensor nodes act as member nodes of that cluster. Only CHs can

directly communicate to sink and member nodes use their CH as intermediate

router in case of communication to BS. CHs collect the data from all nodes,

aggregate received data and route all meaningful compress information to BS.

Because of these additional responsibilities CH dissipates more energy and if it

remains CH permanently it will die very quickly, as it happens in case of static

clustering. LEACH tackles this problem by adopting randomized rotation of CHs

to save battery of individual node [1,2]. In this way LEACH maximizes lifetime

of network nodes and also reduces the energy dissipation by compressing the date

before transmitting to BS.

Operation of LEACH is based on rounds, where each round normally consists

of two phases. These are setup phase and steady state phase. In setup phase

CHs and clusters are created. All nodes are managed into multiple clusters. Some

nodes independently elect themselves as CHs without any negotiation to other

nodes. CH nodes elect themselves on behalf Suggested percentage P and their

previous record as a CH. All nodes which were not CHs in previous 1/p rounds,

generate a number between 0 to 1 and if it is less then threshold T (n) then these

nodes become CHs. Threshold value is set through this formula.

T (n) =

{P

1−P∗(rmod 1P)

if n ∈ G

0 otherwise(2.1)

Where G is set of nodes that have not been selected as CHs in previous 1/p

rounds, P is suggested percentage of CH, r is current round. If nodes become

CHs in current round, these nodes will be CHs after next 1/p rounds [1-3]. This

indicates that every node will serve as a CH equally and energy dissipation will

be uniform throughout the network. Elected CHs broadcast their status using

CSMA/CA protocol. Non-CH nodes select their CHs by comparing Received

Signal Strength Indication (RSSI) of multiple CHs, from where nodes receive ad-

vertisements messages. All CHs will create TDMA schedule for their associated

12

members in the cluster.

Base Station

Clusre-head node

Non-Cluster-head node

Figure 2.3: Clustering topology of LEACH

Steady state phase starts when clusters have been created. In this phase nodes

communicate to CH during allocated time slots otherwise nodes keep sleeping.

Due to this attribute LEACH minimizes energy dissipation and extends battery

lifetime of all individual nodes. When data from all nodes of cluster have been

received to CH, it will aggregate, compress and transmit to BS. Usually steady

state phase is longer than setup phase. LEACH network topology is shown in Fig

2.3.

LEACH reduces this energy dissipation with the help of following feature.

1. Reducing the number of direct transmission to BS using CH.

2. Reducing data to be transmitted, through compression technique.

3. LEACH Increases the lifetime of all nodes through randomizes rotation be-

ing as CH [1], [2], [3].

13

4. LEACH allows non-CH nodes to keep sleeping except specific duration.

5. In LEACH routing protocol nodes die randomly and dynamic clustering en-

hance network stability.

6. LEACH routing protocol makes WSNs scalable and robust.

2.6.2 LEACH-Centralized (LEACH-C)

Although conventional LEACH has many advantages e.g, energy maximization

of network and also provides limited network scalability. But LEACH does not

guarantee the effective location and optimal number of CHs during all rounds

[1-4], [6,7]. It is due to its distributed algorithm of clustering creation. So setup

phase of LEACH needs to be modified for more effective cluster formation. For

this purpose LEACH-C has been proposed by Heinzelman and co-authors in [3].

In LEACH-C during setup phase all nodes send their energy status, node IDs

and location information to BS [2]. BS specifies some nodes as CHs and non-CHs

with help of central control algorithm. Using central control algorithm BS com-

pares the energy of all nodes with specific average energy level [6]. If energy of

some nodes is less than average energy, BS categorizes these nodes as member

nodes. BS selects optimal number of CHs from nodes having energy above than

average energy level. Then BS broadcasts the node IDs of selected CHs to all

networks nodes. BS tries to minimize the distance between member nodes and

CHs. In this way LEACH-C reduces the energy dissipation of member nodes be-

cause now nodes have to transmit to CH at very short distance. This central

control algorithm produces much better clustering than distributed control algo-

rithm. LEACH-C uses some necessary assumptions that each node can compute

its energy, knows its location and can transmit this information to BS, no matter

how much far away the BS is placed. Because nodes can adapt multiple trans-

mission power level that’s why nodes can vary their range of communication for

intra-cluster communication and inter-cluster communication [2].

Steady state phase of LEACH-C is similar to LEACH but LEACH-C enhances

the number of packets received at BS. It is because of optimal number of selected

CHs and their effective location with respect to non-CH nodes. LEACH-C is

slightly better than LEACH, but it has some drawbacks also like, in setup phase all

nodes have to send their information to BS. This dissipates additional energy of all

14

nodes for every round. BS selects most suitable CHs and broadcasts their node IDs

to all nodes. Normal nodes also dissipate energy unnecessarily in matching their

node IDs to CHs node IDs. This extra computation over-head is main disadvantage

of LEACH-C.

2.6.3 Solar-aware Low Energy Adaptive Clustering Hier-

archy(sLEACH)

Energy harvesting is essential incase of some specific applications of WSNs, es-

pecially when sensor nodes are deployed in non-accessible areas like battlefield and

forest [9]. To deal with such kind of applications solar-ware LEACH (sLEACH)

has been proposed by authors in [9], in which lifetime of the WSNs has been

improved through solar cell installation over nodes. In sLEACH some nodes are

facilitated by solar power and these nodes will act as CHs more frequently, de-

pending upon their solar status. Both LEACH and LEACH-C are extended by

sLEACH.

2.6.3.1 Solar-aware Centralized LEACH

In solar-aware Centralized LEACH CHs are selected by BS with help of im-

proved central control algorithm. Normally BS selects solar powered nodes as

CHs because these nodes have maximum residual energy. Authors in [9] improve

the conventional CH selection algorithm used in LEACH-C [2], [3]. In sLEACH

nodes transmit their solar status to BS along with energy level and nodes with

higher energy are selected as CHs. Performance of sensor network increases when

number of solar-aware nodes are increased. Sensor network lifetime also depends

upon the sunDuration. It is the time when energy is harvested. If sunDuration is

smaller CH handover is performed in sLEACH [9]. If node serving as CH is run-

ning on battery and other node in same cluster sends data with a flag, denoting

that its solar power is increased this node will become CH in place of first serving

CH. This new CH is selected during steady state phase, that also enhance the

lifetime of the network nodes.

2.6.3.2 Solar-aware Distributed LEACH

In Solar-aware Distributed LEACH a distributed algorithm is used for clustering

process. In setup phase, CH’s selection preference is given to solar-driven nodes.

Initially probability for solar-driven nodes is higher than battery-driven nodes.

15

Equation 1 is needed to be changed to increase the probability of solar-driven

nodes. Remaining setup phase portion of solar-aware Distribute LEACH is like

conventional LEACH. Like solar-aware Centralized LEACH, in Steady state CH

handover can be performed. If solar-power is added in non-CH node and CH

is battery driven node then CH’s handover is executed. Efficiency of sLEACH-

Distributed can be maximized by adding more solar-driven nodes. As shown in

Flow chart, setup phase is distributed and probabilistic base like LEACH but in

this case probability of solar-driven node is kept higher. These solar-driven nodes

can become CHs consecutively in next round also if their probability is still higher

than other nodes.

2.6.4 Energy Low-Energy Adaptive Clustering Hierarchy

(E-LEACH)

Energy-LEACH is another extension of LEACH routing protocol to enhance the

lifetime of wireless sensor networks. Unlike LEACH, Residual energy of sensors

play crucial role in selection of CH in E-LEACH [11]. E-LEACH deals with the

homogeneous network where energy uniformly distributed among all the sensor

nodes initially, but after first round energy level of all nodes become different. In

this algorithm the energy level of nodes specify that it will be CH or not for the

next round. This clustering routing protocol based on some strong assumptions

like each node is aware from its own residual energy and also from the residual

energy level of all other nodes. Unlike LEACH, in setup phase CH are selected

on the base of residual energy in E-LEACH. Nodes with higher energy elected as

CH. In steady state phase normal nodes transmit data to CH and CH aggregated

data of all nodes. CH then compress aggregated data and transmit to BS.

2.6.5 Multi-hop LEACH

When network diameter is increased beyond certain level, distance between

CH and BS is increased enormously. This scenario is not suitable for LEACH

routing protocol [11] in which BS is assumed at single-hop to all CHs. In this

case transmission energy cost of CHs is not affordable. To address this problem

Multi-hop LEACH is proposed in [12]. Multi-hop LEACH is another extension of

LEACH routing protocol to increase energy efficiency of the WSNs [11-13]. Multi-

hop LEACH is also a distributed clustering routing protocol. Like LEACH, in

Multi-Hop LEACH some nodes elect themselves as CHs and other nodes associate

themselves with elected CHs to complete clustering formation in setup phase.

16

Base Station

Clusre-head node

Non-Cluster-head node

Figure 2.4: Clustering topology of M-LEACH

In steady state phase, CHs collect data from all their member nodes and trans-

mit data directly or through other CHs to BS after aggregation. Multi-Hop

LEACH allows two types of communication operations, inter-cluster communi-

cation and intra-cluster communication.

In Multi-hop inter-cluster communication, when whole network is divided into

multiple clusters each cluster has one CH. This CH is responsible for communi-

cation for all nodes in the cluster. CH receives data from all nodes at single-hop,

aggregates and transmits directly to BS or through intermediate CHs. In Multi-

hop inter-cluster communication when distance between CH and BS is large then

CH use intermediate CH to communicate to BS.

Fig. 2.4 describes Multi-Hop LEACH communication architecture. Random-

ized rotation of CH is similar to LEACH. Multi-Hop LEACH selects best path with

minimum energy consuming route. An other criteria of selecting intermediate CH

is to keep overall distance towards BS should be minimum because distance is

directly proportional to energy dissipation. So, a route with minimum hop-count

between source CH and BS is selected.

17

2.6.6 Mobile-LEACH (M-LEACH)

LEACH considers that all nodes are fixed and homogeneous with respect to

their energy and radio characteristics which is not very realistic approach. In

particular round uneven nodes are attached to multiple CH. In this case CHs with

large number of member nodes will drain their energy very quickly as compare to

CHs with smaller number of member nodes associated . Mobility support is also

very important issue faced by LEACH routing protocol. To mitigate these issues,

M-LEACH has been proposed in [16].

M-LEACH allows mobility for all nodes during the setup and steady state

phase. Some assumptions are also made in M-LEACH like other routing proto-

cols. Initially all nodes are homogeneous in sense of antenna gain, all nodes have

their location information through Global Positioning System (GPS) and BS is

considered fixed in M-LEACH. Distributed setup phase of LEACH is modified by

M-LEACH in order to select most suitable CHs. M-LEACH also considers remain-

ing energy of the nodes in selection of CHs. In M-LEACH CHs are elected on the

basis of attenuation model [17]. Other criteria for CH selection is speed of mobil-

ity. Nodes with minimum mobility and lowest attenuation power are selected as

CHs. Then selected CHs broadcast their status to all nodes in their transmission

range. Nodes compute their willingness from multiple CHs and select the CH with

maximum residual energy.

In steady state phase, if nodes move away from CH or CH moves away from

its member nodes then any other CH will become suitable for member nodes.

This phenomena results into inefficient clustering formation. To deal with this

problem M-LEACH provides handover mechanism for nodes to switch on to new

CH. When nodes decide to make handoff, then nodes send DIS-JOIN message to

current CH and also send JOIN-REQ to new CH. After handoff, CHs re-schedule

the transmission pattern for member nodes.

2.6.7 LEACH-selective cluster (LEACH-SC)

In earlier clustering routing protocols, authors address the position of nodes

with respect to their CH and BS to some extent. But LEACH-SC proposed

in [8], deals with this phenomena and provides the reasonable solutions about

their relative distance and position. Actually energy dissipation depends upon the

relative position and distance among non-CHs, CHs and BS. Clustering protocols

basically try to minimize the distance of transmission among normal nodes to CHs

and CHs to BS. But some time nodes are sending data to their CH in opposite

18

direction of BS. In this scenario data is transmitted with additional distance.

LEACH-SC addresses these kinds of issues in order to save the transmitting energy

cost of the sensor nodes and improves the network’s lifetime .

Operation of LEACH-SC is based on rounds. Each round is consisting of setup

phase and steady state phase. But LEACH-SC slightly alters the clustering for-

mation. In improved clustering formation algorithm of LEACH-SC, selected CHs

advertise their IDs and location information to all nodes in range. Nodes receive

these information from all CHs within communication range. Nodes compare in-

formation and select their CHs which is nearest to the middle-point between BS

and comparing non-CH node itself. Basically in this improved clustering forma-

tion algorithm, authors change the way of making membership between non-CHs

nodes and selected CHs. A combined flow chart of all is shown in Fig 2.5.

Start

Broadcast cluster‐head

advertisement

Send Association Request to Selected cluster‐head

YES NO

Wait and listen Medium till its selection

Assign TDMA slots for communication for member node

No

Yes

Send data to CH at allocated time slot

CH aggregate data received from all nodes

Base Station

Setup Phase

Steady State

Phase

Cluster‐head is elected on probability

Compare RSSI and

link quality

Elected CHs

Receive CHs Advertisements

Selected CH

Receive Association Request from nodes

Receive TDMS slot for communication

Receive Data from member nodes

CH sends aggregated data to next Cluster‐head(through energy

efficient path) closer to BS

CH at single‐hop Send aggregated data to BS

Multi‐hop LEACH

LEACH, LEACH‐

SC, LEACH‐C, M‐

LEACH

protocol

Multi‐hop LEACH, LEACH,

sLEACH‐distributed,

sLEACH‐Centralized,

Node selects CH which is closest to middle‐point

of node itself and BS

Selected CH

protocol

Nodes transmit location information and energy level to BS

BS calculates average energy of whole network

Below

average enegy

BS uses simulated annealing algorithm for optimal CHs selection for optimal number of clusters

YES

NO

protocol

LEACH, LEACH‐SC, Multi‐hop

LEACH, sLEACH .‐Distributed,

M‐LEACH

LEACH‐C

Final selection Cluster‐headsCHs selection is centralized

Distributed

Centralizedprotocol

sLEAH. Centralized Nodes transmit location information, IDs and energy levels to BS

BS selects K+3 Optimal nodes as possible cluster‐heads using

central control algorithm

BS Removes a potential CH with minimal sum of distance

to other potential CHs

BS Removes a potential CH from two CHs having closest

distance to each other

BS does not remove potential CH which is solar‐driven, so maximum solar‐driven CH are preferred by BS

Solar ‐power is

added

Normal Nodes will keep sending

data to CH for

whole round

No

Yes

Solar‐driven CH node

Converted CH

Receive Data with flag from

member nodes

No

Yes

CH handover

protocolSend data to CH

Solar‐LEACH

CHs aggregate and send data to BS

CHs aggregate and send data to BS

CHs send data to BS

Not‐solar‐aware LEACH

End

Selected CH

protocol

LEACH‐SC, M‐LEACH

LEACH‐SC

M‐LEACH

No

Yes

Nodes select CHs using best signal quality and most

slowest nodes

No

Yes

Receive Data

Node sends data to cluster‐head with a flag solar‐power added

Nodes belong to G participate in CH

selection

Figure 2.5: Combined Flow chart of clustering protocols

19

2.6.8 SEP

The authors in SEP [32] were one of the first to address the impact of energy

heterogeneity of nodes in WSNs that are hierarchically clustered. Their approach

was to assign weighted probability to each node based on its’ energy level as the

network evolves. One major characteristic of this approach is that it rotates the

cluster-head to adapt the election probability to suit the heterogeneous settings.

The authors exploited the capabilities of LEACH to develop an adaptive and well

distributed model to cater for extra energy introduced into the network, which

is a source of heterogeneity. Under the model development of SEP, two kinds of

nodes with different energy levels were used, constituting a two-level hierarchical

WSN in a single-hop setting. The assumption is that the nodes are not mobile

and are uniformly distributed over the sensing region

2.6.9 DEEC

The authors of DEEC [5] proposed a distributed energy-efficient clustering

scheme following the thought of LEACH and SEP. The idea of the protocol is

to elect cluster-heads using probability based approach to estimate the ratio of

the residual energy of each node and the average energy of the network. DEEC is

more SEP-like in the sense that, it adapts the rotating epoch of each node to its

energy. Recall, that an epoch is a set of rounds in a network. Eventually, the node

with high residual energy will become cluster-heads than the nodes with low en-

ergy. The goal of DEEC is to design an energy aware algorithm for heterogeneous

network

2.7 Comparison of Reviewed Routing Protocols

All routing protocols have some significant properties and address specific issues

to produce some betterment in existing routing protocols. Each routing protocol

has some advantages and capabilities. Routing protocols face some common en-

ergy dissipation challenges e.g., Cost of Clustering, Selection of CHs and Clusters,

Synchronization, Data Aggregation, Repair Mechanisms, Scalability, Mobility, and

initial energy of all nodes[14]. We compare above mentioned routing protocols with

respect to some very important performance characteristics for WSNs. These char-

acteristics of WSNs are following.

20

Table

2.1:Perform

ance

comparisonofhierarchicalroutingprotocols

Rou

ting

protocol

Type

Mob

i-lity

Scal-

able

Self-

orga-

nize

Rota-

tion

Dist-

ribu-

ted

Cent-

rali-

zed

Hop

-count

Energy

effi-

ciency

Resou

rce

awar-

eness

Data

aggre-

gation

Hom

o-ge-

neous

LEACH

Hierarchi-

cal

fixed

limited

Yes

Yes

Yes

No

Single-

hop

High

Good

Yes

Yes

LEACH-C

Hierarchi-

cal

fixed

Good

Yes

yes

No

Yes

Single-

hop

High

Good

Yes

Yes

sLEACH-

CHierarchi-

cal

fixed

Good

Yes

Yes

No

Yes

Single-

hop

Very

High

Very

Good

Yes

Yes

sLEACH-

Distributed

Hierarchi-

cal

fixed

Good

Yes

Yes

Yes

No

Single-

hop

Very

High

Very

Good

Yes

Yes

Multi-Hop

LEACH

Hierarchi-

cal

fixed

Very

Good

Yes

Yes

Yes

No

multi-

hop

Very

High

Very

Good

Yes

Yes

M-LEACH

Hierarchi-

cal

Mob

ile

Very

Good

Yes

Yes

Yes

No

single-

hop

Very

High

Very

Good

Yes

Yes

LEACH-

SC

Hierarchi-

cal

fixed

Good

Yes

Yes

Yes

No

Single-

hop

High

Good

Yes

Yes

SEP

Hierarchi-

cal

fixed

Better

Yes

Yes

Yes

Yes

Single-

hop

High

Very

Good

Yes

No

DEEC

Hierarchi-

cal

fixed

Btter

Yes

Yes

Yes

Yes

Single-

hop

High

Better

Yes

No

21

• Classification: The classification of a routing protocol indicates that it is

flat, location-based or hierarchal routing protocol [15].

• Mobility: It specifies that routing protocol support mobility or not.

• Scalability: It describes how much routing protocol is scalable, if the network

density is increased.

• Randomized Rotation of CHs: Randomized Rotation of CH is very necessary

in order to drain the battery of all nodes equally [1].

• Distributed clustering algorithm: CHs are self-elected in distributed cluster-

ing algorithm [1].

• Centralized clustering algorithm: CHs are selected by BS, using central con-

trol algorithm [3].

• Single-hop or Multi-hop: It is also important feature of routing protocol.

Single-hop is energy efficient if it is smaller area of network and multi-hop

is better for denser network [11].

• Energy Efficiency: It is the main concern of energy efficient routing protocol

to maximize lifetime of the network [1], [2], [4], [11], [15].

• Data Aggregation: In order to reduce amount of data transmitted to BS,

CHs perform data-aggregation in this way CHs transmission energy cost is

reduced [1], [2].

• Homogeneous: Homogeneity of all nodes is considered in some routing pro-

tocols which describes that initial energy level of all the nodes is same.

Table.I shows the comparison LEACH, LEACH-C, sLEACH, M-LEACH, Multi-

Hop LEACH and LEACH-SC. Performance comparison shows that behavior of

these routing protocols is similar in many ways. All routing protocol are hier-

archal, homogeneous, perform Data aggregation, self-organization, randomized

rotation of CHs and having fixed BS despite M-LEACH. LEACH, LEACH-SC,

M-LEACH and Multi-Hop LEACH use distributed algorithm for CH selection.

LEACH-C uses central control algorithm for CH selection and sLEACH is designed

for both centralized and distributed algorithm. LEACH, sLEACH, LEACH-SC

and M-LEACH are routing protocol in which BS is at single-hop and in Multi-

Hop LEACH BS can be at multi-hop distance from CHs. LEACH and M-LEACH

allow limited scalability. LEACH-C, sLEACH and LEACH-SC allow good scala-

bility while, Multi-Hop LEACH is providing maximum scalability feature due to

multi-hop communication option for CHs.

22

Chapter 3

Propose Model of CEEC

23

Chapter 3

Proposed Model of CEEC

3.1 Proposal of CEEC

In our thesis, we propose two energy efficient clustering routing protocols. First

we present the CEEC routing protocol in this chapter

3.2 Advance Heterogeneous Network Model for

CEEC

In WSNs, nodes are randomly dispersed in network area without any deploy-

ment management. Although nodes deployment is very challenging task in WSNs,

but we can still address this issue by dividing whole network area into multiple

logical regions. We present an advance heterogeneous network model in this sec-

tion. Our proposed network model contains three different types of nodes called,

normal, advance and super nodes. These nodes preserve different levels of energy.

We divide whole network’s M ×M area into three equal rectangular regions Low

Energy Region (LER), Medium Energy Region (MER), and Higher Energy Region

(HER). We assumed that BS is placed at top corner of the network. We homoge-

neously spread normal nodes in nearest region LER with respect to BS. Advance

and Super nodes are homogeneously placed in MER and HER region respectively.

Overall heterogeneous network is produced by combining all regions, as shown in

Fig 3.1.

One more distinguish feature of our proposed heterogeneous network model is

that, nodes associate with their own type of cluster-heads nodes as shown in Fig

24

0 10 20 30 40 50 60 70 80 90 1000

34

68

100

Hig

h E

nerg

y Le

vel

Med

ium

Ene

rgy

Leve

l L

ow E

nerg

y Le

vel

X

Figure 3.1: Advance Heterogeneous Network Model

3.2.

In CEEC, total number of nodes will be:

N = Nn +Na +Ns (3.1)

where, Nn are normal nodes, Na are all advance nodes and Ns are all super nodes.

In three levels heterogeneous network, energy assigned to normal nodes is E0.

Advance and super nodes have α and 2α factors more energy respectively as

compared to normal nodes. Total energy of all normal nodes will be:

En = Nn × EO (3.2)

total energy of advance nodes will be:

Ea = Na × (EO.(1× α)) (3.3)

Similarly the total energy of super nodes can be calculated by:

Es = Ns × (EO.(1× 2α)) (3.4)

25

0 10 20 30 40 50 60 70 80 90 1000

34

68

100

Hig

h E

nerg

y Le

vel

Med

ium

Ene

rgy

leve

l Lo

w E

nerg

y Le

vel

X

Figure 3.2: Clustering Topology in Heterogeneous Network Model

In this way, total energy of all network’s nodes will be:

ET = Nn × EONa × (EO.(1× α))

+Ns × (EO.(1× 2α)) (3.5)

From above equations, it is clearly understandable that, proposed advance het-

erogeneous network model spread the nodes in network area with the ascending

order of energy. As the distance of nodes from BS increases, the energy level of the

nodes is also increases. It brings the equal distribution of resources with respect

to responsibilities of nodes.

3.3 First Order Radio Model

First order Radio model is adopted by mostly energy efficient routing protocols

is given in [1-4]. We also adopted this radio model to analyze realistically our

proposed model to other clustering routing protocols. Radio model’s energy dis-

sipation values indicate the hardware energy consumptions during transmitting,

receiving and aggregation of data. EelecTX and EelecRX are consumed energy

values to run transmitter and receiver circuitry per bit. Radio dissipates εamp for

transmission amplifier in order to obtain suitable Eb/N0 [1].

Energy values used in selection of suitable Eamp are given in Table I. These values

26

Table 3.1: Radio Characteristics

Operation Energy Dissipation

Transmitter Electronics (EelecTx) 50 nj/bitReceiver Electronics (EelecRx) 50 nj/bit

Transmit amplifier (εamp) 100 pj/bit/m2

are adopted in extensive research work .

L bit packet Transmit

ElectronicsTx Amlifier

Receiver

Electronics

L bit packet

ETx(d)

EeleTX *L Eamp *L*d2

EeleRX *L

d

Figure 3.3: Radiomodel

Energy dissipation of a individual node depends upon the number of trans-

missions, number of receptions, amount of data to transmit, distance between

transmitter and receiver. Radio model of sensors node is shown in Fig 3.3 But

in most of cases, only transmission energy cost is considered during performance

analysis of clustering routing protocol. Transmitting energy cast for L data bit

will be:

ETX(L, d) =

L× Eelec + L× Efsd2 If d < do

L× Eelec + L× Eempd4 If d ≥ do

(3.6)

where, ETX(L, d) is transmitting energy cost, L data bits to be transmitted, Efs

is free space transmission Model, Emp is Multi-path transmission Model. Eelec is

energy cost used for both Transmitter and Receiver circuit to run for each bit. In

next section, we use this radio model’s information in energy computation of our

proposed model shown in Fig 3.3. Some basic assumptions are made by all earlier

clustering routing protocols. We also make some assumption for our advance

network model.

• All nodes and BS are fixe

27

• All nodes have homogeneous radio and processing abilities.

• Nodes are location aware through Global Positioning System (GPS) equip-

ment facility over nodes

• All nodes are un-rechargeable

• Nodes have adjustable transmission power level for intra-cluster-communication

and inter-cluster communication

3.4 Proposed Model of CEEC

In current section, we propose a Centralized Energy Efficient Clustering (CEEC)

routing protocol. In earlier section, we proposed advance heterogeneous network

model, in which nodes with different energy level are deployed in separate re-

gions. In CEEC, BS performs central clustering formation of network, with help

of CEEC’s central control algorithm. CEEC’s advance central control algorithm

considers four factors for selection of cluster-heads, initial energy of nodes, resid-

ual energy of nodes, average energy of each region’s nodes and location of nodes.

Operation of CEEC is based on rounds, with adjustable duration. Each round

is divided into Network Settling Time (NST) and Network Transmission Time

(NTT). During NST cluster-heads are selected and multiple clusters are formed.

During NTT, sensed information from all nodes is transmitted to BS with help of

cluster-heads.

3.4.1 Network Settling Time (NST)

Efficient cluster formation is key technique to enhance the network lifetime.

During NST suitable cluster-heads are selected by BS, with the help of central

control algorithm. In central control algorithm, BS calculates three different aver-

age energies for normal, advance and super nodes to obtain separate cluster-heads

for all regions. BS knows the initial energy of all nodes for the first round and it

can easily calculate the average energies for first round. After first round, nodes

provide their residual energy information to BS. Another significance of our pro-

posed protocol is that, nodes provide their residual energy information with data

packets transmitted in NTT of previous round. This factor also saves their extra

transmission energy cost, paid by all nodes during NST, as it is paid in con-

ventional centralized control protocols. Average energy of residual energy of all

normal nodes, which are spread in closest LER to BS, is calculated by:

28

En(r) =1

Nn

Nn∑i=1

E(ni)(r) (3.7)

where, En(r) is average energy and r is current round of operation. similarly

average energy of advance nodes, which are spread in MER to BS, is calculated

by:

Ea(r) =1

Na

Na∑i=1

E(ai)(r) (3.8)

average energy of super nodes, which are spread in HER to BS, will be:

Es(r) =1

Ns

Ns∑i=1

E(si)(r) (3.9)

After calculation of average energy of each region, BS compares the energy of

each node (1 ≤ i ≥ N) to their corresponding region’s average energy. Nodes with

higher or equal energy to average energies (Ei ≥ AverageEnergy) are selected by

BS as Expected Cluster-Heads (ECHs). A Node from ECHs (1 ≤ j ≥ ECHs)

is not still final a cluster-head. BS has to select desired percentage P of cluster-

heads in every round, for each type of nodes. When all nodes are alive, then

overall required number of cluster-heads, will be:

CHs = N × P (3.10)

if number of ECHs is greater than required CHs, then BS will select AlliveNodes×P cluster-heads with maximum residual energy and minimum distance between

ECHs and BS. BS calculates this minimum distance using this equation:

Min− disttoBS =√

(X−xj)2+(Y−yj)2 (3.11)

where Min − disttoBS is minimum distance between BS and jth ECH. X and Y

are location coordinates of BS. Similarly xj and yj are location coordinates of jth

ECH in whole network’s region. If number of ECHs are equal to required cluster-

heads then BS elects all ECHs as Final Selected Cluster-Heads. These finally

elected cluster-heads will be grouped as Finally Selected Cluster-Heads (FSCHs).

BS multi-casts announcements of selection to FSCHs, instead of broadcasting

to all nodes, as it happen in previous centralized routing protocols. It also reduces

computational over-head of non-cluster-head nodes. FSCHs receive the BS’s final

decision. A FSCH from the group (1 ≤ k ≥ FSCHs) advertises its status updates

29

along its location information to all nodes laying in its range. If non-cluster-head

node (1 ≤ i ≥ N − FSCHs) receives multiple advertisements, then it selects its

Corresponding Cluster-Head (CCH) with highest Received Signal Strength Indi-

cation (RSSI), and with minimum distance to CCH. Nodes calculate the minimum

distance among all approaching FSCHs with help of this equation:

Min− disttoFSCH =√

(xk−ik)2+(yk−yi)2 (3.12)

where, Min − disttoFSCH is minimum distance among nodes and FSCH. Non-

cluster-head nodes send their Association Request (AS-Req) to their CCHs using

CSMA/CA. The main restriction in association of nodes is that, these nodes have

to select their cluster-heads, laying in their own corresponding region. Then CCHs

assign specific TDMA slots to its member nodes for data transmission during NTT.

CEEC’s central algorithm also allows nodes to select themselves as Self-Selected

Cluster-Heads (SSCHs) that are not receiving FSCHs advertisements. In CEEC,

BS selects FSCHs uniformly from whole network area, in order to minimize the

SSCHs. NST is very small as compared to NTT and total duration of single NST

is between the end of a NTT to start of next NTT.

3.4.2 Network Transmission Time (NTT)

NTT is very much similar to LEACH and other clustering routing protocols. In

NTT all nodes send their data to their CCHs, in assigned time slots. Cluster-heads

receive the data from their clusters and aggregate the data. Data aggregation is

key technique to compress data amount. Cluster-heads only send meaningful

information to BS in order to save the battery lifetime.

During NST and sensing environment all nodes pay specific energy cost. But

transmission energy cost is considered as major source of energy dissipation. If we

consider a network with M × M area, then energy dissipation of a cluster-head

will be:

ECH = ((N

K− 1)(EeleRX × LC)) +

N

K× LC × EAD +

EeleTX × LA + Eafs × LA × d2toBS (3.13)

where, ECH is energy dissipation of a cluster-head, N is total number of nodes,

dtoBS is distance between BS and cluster-head and its value is equal to 0.765(M2)

[3-4], K is optimal number of clusters, LC is data bits received from each node in

with in cluster and LA is aggregated data bits. Energy dissipation of non-cluster-

30

heads of that cluster will be:

Enon−CHs = (N

K− 1)(EeleTX × LC + Eafs × LC × d2toCH) (3.14)

where, Enon−CHs is energy dissipated by all non-cluster-heads nodes within one

cluster, dtoCH is distance between cluster-head and its member. Total energy

dissipation of a cluster during transmission is:

EC = ECH + Enon−CHs (3.15)

as it is known that K are total number of clusters in the whole network, if we

put the energy consumption value ECH and Enon−CHs in equation 15 then total

energy dissipation of that cluster will be:

EC = ((N

K− 1)(EeleRX × LC)) +

n

K× LC × EAD +

EeleTX × LA + Eafs × LA × d2toBS + (N

K− 1) (3.16)

(EeleTX × LC + Eafs × LC × d2toCH) (3.17)

Et = K × Ec (3.18)

where, Et is transmission energy cost for one round. Network lifetime can be

estimated in terms of total number rounds in which nodes are alive. Total number

of rounds will be:

R =NnEO.Na(EO.(1× α)) +Ns(EO.(1× 2α))

Et

(3.19)

duration of one round is NST+NTT seconds. Flow chart of CEEC operation is

shown in Fig 3.4.

3.5 Simulation results of CEEC performance

In this section we simulate CEEC along with LEACH, SEP, E-SEP and DEEC

to make critical performance analysis of our proposed protocol. We simulate these

routing protocols for two different scenarios. Value of α and Eo is kept same in

both scenarios. In first scenario 100 nodes are deployed in 100m× 100m network

area. In second scenario, 120 nodes are scattered in 210m× 210m network’s area.

31

Figure 3.4: Flow chart of CEEC operation

In first simulations scenario, network area of 100m×100m is divided into three

rectangular regions LER, MER and HER. BS is placed at the top of the network.

Other important simulation parameters are given in Table I. Before we discuss the

results, following performance measurements are necessary to be defined as given

in [3-5].

1. Stability period: It is duration of network operation over which all nodes

are alive and it continues until the death of first node.

2. Network lifetime: A period, from start of operation to death of last node is

called network lifetime

3. Instability period: When nodes begin to die instability period is started and

it goes on till the death of last node.

32

Table 3.2: Simulation Parameters

Parameter value

Network size 100m * 100mInitial Energy .5 j

p .1 jData Aggregation Energy cost 50pj/bit j

number of nodes 100packet size 200 bit

Transmitter Electronics (EelectTx) 50 nj/bitReceiver Electronics (EelecRx) 50 nj/bit

Transmit amplifier (Eamp) 100 pj/bit/m2

4. Number of Cluster-heads: In each some nodes are selected as cluster-heads.

It also indicates the number of clusters generated per round.

5. Packet to BS: It shows the data amount received by BS from cluster-heads.

Results of simulations are described in subsection of current section.

3.5.1 Results for first Scenario

Fig 3.5 shows the how many nodes are alive as the number of rounds increase.

Results shows that overall CEEC has maxim network liffe time as compared to

other protocols. Stability of CEEC is significantly greater achievement of our

proposed model. Stability period of CEEC is almost 120 %, 70 %, 55 %, 48 % is

greater than LEACH, SEP, E-SEP and DEEC respectively. It is because of well

deployment planning of nodes and centralized clustering formation.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Number of alive nodes

LEACHDEECSEPESEPCEEC

Figure 3.5: Alive Nodes for 100m× 100m Network with 100 nodes

In Fig 3.6, numbers of dead node are measured as network operation goes on.

Like earlier case, CEEC performs much better to minimize dead nodes ration as

33

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

10

20

30

40

50

60

70

80

90

100

Number of roundsNumber of dead nodes

LEACH

DEEC

SEP

ESEP

CEEC

Figure 3.6: Dead Nodes for 100m× 100m Network with 100 nodes

rounds progress, in CEEC, last node dies after 4200 rounds. Another significant

feature of CEEC is that instability period starts later in CEEC as compared to

other routing protocols. In CEEC, nodes do not start to die instantaneously in

instability period as it happens in SEP and LEACH case. It means CEEC has

resistance capability in instability period and continues to send sensing reports

from network field as long as possible. this is because of CEEC deals with nodes

according to their location and energy discrimination. CEEC centrally varies the

transmission responsibilities of nodes according to their remaining resources that’s

why execution of CEEC helps the nodes to prolong their lifetime.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

5

10

15

20

25

30

number of rounds

Nu

mb

er

of C

lust

er−

He

ad

s

SEPESEPDEECLEACHCEEC

Figure 3.7: Cluster-heads per round

In Fig 3.7 numbers of cluster-head per round are shown. From results it clearly

understandable that only CEEC is providing required number of cluster-heads

continuously. LEACH, SEP, E-SEP and DEEC do not provide guaranteed number

of cluster-heads per round. Their uneven cluster-heads generation, effect badly the

amount packets received by BS from cluster-heads. As it shown in Fig 3.8, CEEC

produces maximum numbers of packets that are successfully received by BS.

34

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

4

number of roundsNumber of packets


Figure 3.8: Packet to BS Nodes for 100m× 100m Network with 100 nodes

3.5.2 Results for second Scenario

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

20

40

60

80

100

120

Number of rounds

Number of alive nodes


Figure 3.9: Alive Nodes for 210m× 210m Network with 120S nodes

We also simulate LEACH, SEP, E-SEP, DEEC and CEEC for 120 nodes that

are scattered in 210m× 210m network’s area. We do not alter the value of α and

initial energy normal nodes for SEP, E-SEP and DEEC. We also keep energy of

advance and super nodes same as it is in first simulation scenario for our proposed

protocol.

It is shown in results that efficiency of all routing protocols is decreased sig-

nificantly. But comparatively, CEEC perform much better scalability than other

clustering routing protocols. If we analyze performance with respect of stabil-

ity of SEP, E-SEP and DEEC, it is reduced dramatically. Fig 3.9 and Fig 3.10

show network lifetime with respect to alive and dead nodes respectively. Stabil-

ity decreases 45%, 170%, 190%, 205%, 100% for LEACH, SEP, E-SEP, DEEC

and CEEC respectively. LEACH stability period is already very small that’s why

stability period decrement is too so big. Comparatively CEEC conserves better

stability period. From Fig Fig 3.10 it is noticeable that instability period for

CEEC is very small as compared to first scenario. This is because of advance

35

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

20

40

60

80

100

120

Number of roundsNumber of dead nodes


Figure 3.10: Dead Nodes for 210m× 210m Network with 120S nodes

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

10

20

30

40

50

Number of rounds

Nu

mb

er

of C

lust

er−

He

ad

s

DEECLEACHSEPESEPCEEC

Figure 3.11: Cluster-heads per round for 210m× 210m Network with 120S nodes

and super nodes in CEEC are scattered at larger distance and energy dissipa-

tion is very high. Fig 3.11 shows the cluster-heads selection per round for

210m × 210m network’s area. It is interesting to see that cluster-heads selection

has become more challenging when network diameter and number of nodes are

increased. This is because of probabilistic distributed algorithm of cluster-head

selection in LEACH, SEP, E-SEP and DEEC. As shown in Fig 3.11, fluctuations

in cluster-heads selection per round has been increased in this case. SEP, E-SEP

and DEEC define different probabilities for nodes with different energies. That’s

why cluster-heads selections will become more and more complex if the number

of nodes increase in SEP, E-SEP and DEEC. In CEEC, cluster-head’s selection

is centralized and it provides similar results like first scenario. Fig 3.12 shows

the simulations evidence that proposed CEEC routing protocol performs better in

successful packet transmission to BS.

Fig 3.13 shows the stability periods for LEACH, SEP, E-SEP, DEEC and CEEC

for both scenarios. Blue bar charts values are showing stability periods for 100m×100m network’s area and red values for 210m × 210m network’s area. From this

36

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.5

1

1.5

2

2.5

3

3.5x 10

4

number of roundsNumber of packets


Figure 3.12: Packet to BS for 210m× 210m Network with 120S nodes

Fig, it is shown that stability period of CEEC is significantly longer than other

protocols in both scenario.

Figure 3.13: Stability period for 210m× 210m Network with 120S nodes

37

Chapter 4

Propose Model of MCEEC

38

Chapter 4

Proposed Model of MCEEC

4.1 Proposal of MCEEC

4.2 Proposal of MCEEC

In CEEC, CHs directly communicate to BS and results (discussed in chapter

4) show that performance of CEEC decreases if sensor network gets denser and

larger. To tackle this issue, we propose a model of Multi-hop Centralized Energy

Efficient Clustering (MCEEC) that significantly efficient in network scalability

and energy efficiency for large network. Before presentation of propose model of

MCEEC, We will discuss the network model of MCEEC.

4.2.1 Heterogeneous Network Model for MCEEC

In WSNs, nodes are randomly dispersed in network area without any deploy-

ment management. Although nodes deployment is very challenging task in WSNs,

but we can still address this issue by dividing whole network area into multiple

logical regions. We present an advance heterogeneous network model to deal with

deployment problem to some extent. Our proposed network model contains three

different types of nodes called, normal, advance, super nodes with low, medium

and high energy level respectively. We divide whole network’s M ×M area into

three equal rectangular regions called Low Energy Region (LER), Medium Energy

Region (MER), and Higher Energy Region (HER). We assume that BS is placed

at midway of x-axis of the network. We homogeneously spread normal nodes in

most faraway LER from BS. Super nodes are homogeneously placed in HER region

which is closest to BS. Advance nodes are placed in MER, which is middle region

39

0 50 100 150 2000

70

140

210

Hig

h E

nerg

y Le

vel M

ediu

m E

nerg

y L

evel

Low

Ene

rgy

Leve

lFigure 4.1: Advance Heterogeneous Network Model for MCEEC execution

0 20 40 60 80 100 120 140 160 180 2000

70

140

210

Hig

h E

nerg

y R

egio

n M

ediu

m E

nerg

y R

egio

n Lo

w E

nerg

y R

egio

n

Figure 4.2: Clustering Topology of MCEEC

of LER and HER. Overall heterogeneous network is produced by combining all

regions, as shown in Fig 4.1.

One more distinguish feature of our proposed heterogeneous network model

is that, nodes can only associate with their own type of CHs for intra-cluster

communication. CHs of LER and MER utilize intermediate CHs for multi-hop

communication as it is shown in Fig 4.2. Energy level assign to all nodes is

according to their traffic burden.

In MCEEC, total number of nodes will be:

N = Nn +Na +Ns (4.1)

Where, Nn are normal nodes, Na are all advance nodes and Ns are all super

nodes. In three energy level heterogeneous network, initial energy assigned to

normal nodes is Eo. Advance and super nodes have α and 2α factors more energy

respectively as compared to normal nodes. Total energy of all normal nodes will

be:

En = Nn × EO (4.2)

40

Total energy of advance nodes will be:

Ea = Na × (EO.(1× α)) (4.3)

Similarly, total energy of super nodes can be calculated by:

Es = Ns × (EO.(1× 2α)) (4.4)

In this way, total energy of all network’s nodes will be:

ET = Nn × EO +Na × (EO.(1× α))

+Ns × (EO.(1× 2α)) (4.5)

In this heterogeneous network model area of the network is equally divided and

each region has equal number of nodes. We can also vary design of three level

heterogeneous network model for variable values of nodes and energy levels.

4.2.2 Radio Energy Characteristics

First order Radio model is adopted by mostly energy efficient routing protocols

is given in [1-4]. We also adopted this radio model to analyze realistically our

proposed model to other clustering routing protocols. Radio model’s energy dis-

sipation values indicate the hardware energy consumptions during transmitting,

receiving and aggregation of data. EelecTX and EelecRX are consumed energy

values to run transmitter and receiver circuitry per bit. Radio dissipates εamp for

transmission amplifier in order to obtain suitable Eb/N0 [1].

Table 4.1: Radio Characteristics

Operation Energy Dissipation

Transmitter Electronics (EelecTx) 50 nj/bitReceiver Electronics (EelecRx) 50 nj/bit

Transmit amplifier (εamp) 100 pj/bit/m2

Energy values used in selection of suitable Eamp are given in Table I. These values

are adopted in extensive research work .

Energy dissipation of a individual node depends upon the number of trans-

missions, number of receptions, amount of data to transmit, distance between

transmitter and receiver. But in most of cases, only transmission energy cost is

41

considered during performance analysis of clustering routing protocol. Transmit-

ting energy cast for L data bit will be:

ETX(L, d) =

L× Eelec + L× Efsd2 If d < do

L× Eelec + L× Eempd4 If d ≥ do

(4.6)

where, ETX(L, d) is transmitting energy cost, L data bits to be transmitted, Efs

is free space transmission Model, Emp is Multi-path transmission Model. Eelec is

energy cost used for both Transmitter and Receiver circuit to run for each bit. In

next section, we use this radio model’s information in energy computation of our

proposed model.

Some basic assumptions are made by all earlier clustering routing protocols.

We also make some assumption for our advance network model. All nodes and

BS are fixe All nodes have homogeneous radio and processing abilities. Nodes are

location aware through Global Positioning System (GPS) equipment facility over

nodes All nodes are un-rechargeable Nodes have adjustable transmission power

level for intra-cluster-communication and inter-cluster communication.

4.3 Proposal of MCEEC Routing protocol

We propose a Multi-hop Centralized Energy Efficient Clustering (MCEEC)

routing protocol in this section. In MCEEC, BS is responsible for selection of

CHs. BS selects optimal number of CHs using MCEEC’s central control algo-

rithm. In this clustering formation algorithm, BS considers information of initial

energy, residual energy of nodes, average energy of each region’s nodes and loca-

tion of nodes. BS selects equal number of CHs for each region. In MCEEC, only

HER’s CHs can directly communicate to BS while LER’s CHs can send data to

MER’s CHs. Similarly MER’s CHs can transmit data to HER’s CHs instead of

direct communication to BS. MCEEC increases the scalability of the network by

providing multi-hop inter-cluster communication.

Like other clustering routing protocols, MCEEC’s function is also based on

rounds. A round is divided into Network Settling Time (NST) and Network

Transmission Time (NTT). During NST, CHs are selected and multiple clusters

are formed. During NTT, sensed information from all nodes is transmitted to BS

with help of CHs.

42

4.3.1 Network Settling Time (NST)

Effective cluster formation is necessary for network scalability and longer net-

work lifetime. In process of NST, suitable CHs are elected by BS. BS selects

CHs with the help of central control algorithm that ensure the effective clustering.

Initially, BS knows the initial energy of all nodes for the first round and it can

easily calculate the average energies for first round. In central control algorithm,

BS calculates three different average energies for HER, MER and LER simultane-

ously. After first round, BS needs residual energy information of all nodes. Nodes

provide their residual energy information to BS continuously in every round. One

of the main development we added in our proposed model is that, residual energy

information is sent during NTT. Data packets, transmitted in NTT, contain the

information of residual energy. Average energy of of all super nodes, which are

scattered in HER, is calculated by:

Es(r) =1

Nn

Nn∑i=1

E(ni)(r) (4.7)

where, Es(r) is average energy and r is current round of operation. Similarly

average energy of advance nodes, which are spread in MER, is calculated by:

Ea(r) =1

Na

Na∑i=1

E(ai)(r) (4.8)

where, Ea(r) is average energy of advance nodes. similarly average energy of

normal nodes will be:

En(r) =1

Ns

Ns∑i=1

E(si)(r) (4.9)

where, En(r) is average energy of normal nodes. When BS has calculated the

average energy for every region, then BS compares the residual energy of each node

(1 ≤ i ≥ N−FSCHs) to their corresponding region’s average energy. Nodes with

higher or equal energy to average energies (Ei ≥ AverageEnergy) are selected as

Expected CHs (ECHs) by BS. A Node from ECHs (1 ≤ j ≥ Total − ECHs) is

not still a final CH. In every round, BS has to select desired percentage P of CHs

from every region. When all nodes are alive, then overall required number of CHs,

will be:

CHs = N × P (4.10)

if number of ECHs is greater than required number of CHs, then BS will calculate

minimum distance ECHs and itself to select AlliveNodes × P desired CHs. BS

43

calculates this minimum distance with this equation:

Min− disttoBS =√

(X−xj)2+(Y−yj)2 (4.11)

where Min − disttoBS is minimum distance between BS and jth ECH. X and Y

are location coordinates of BS. Similarly xj and yj are location coordinates of jth

ECH in. If ECHs are equal to desired CHs then BS elects all ECHs as Final CHs.

These finally elected CHs will be grouped as Finally Selected CHs (FSCHs).

After selection of FSCHs, BS multi-casts announcements , instead of broadcast-

ing to all nodes. These announcements contain list of FSCHs and their location

information. In previous centralized routing protocols, BS broadcasts ID informa-

tion of all CHs to all nodes and nodes match their ID to each ID of CH. It was re-

sulting a significant computational over-head for all nodes. Multi-casting strategy

of MCEEC reduces computational over-head of non-cluster-head nodes. FSCHs

receive the BS’s final decision. Every FSCH from the group (1 ≤ k ≥ ECHs) of

FSCHs advertises its status updates along its location information to all nodes lay-

ing in its range. If non-cluster-head node receives multiple advertisements, then it

selects its Corresponding Cluster-Head (CCH) with high Received Signal Strength

Indication (RSSI), minimum distance and type of CCH. Non-cluster-head nodes

calculate the minimum distance among all approaching FSCHs with help of this

equation:

Min− disttoFSCH =√

(xk−ik)2+(yk−yi)2 (4.12)

where, Min − disttoFSCH is minimum distance among nodes and FSCH. After

completing selection criteria, non-cluster-head nodes send their Association Re-

quest (As-Req) to their CCHs using CSMA/CA. The main criteria of association

is, nodes have to select CHs that belong their own corresponding region. It means

that nodes laying in LER can only associate to LER’s CHs. Same restrictions

are followed by nodes of MER and HER. CCHs receive the As-Req and recognize

their members. Then CCHs assign specific TDMA slots to its member nodes for

data transmission during NTT. MCEEC’s central algorithm also allows nodes to

select themselves as Self-Selected Cluster-Heads (SSCHs), which are not receiving

any FSCHs advertisements. In MCEEC, In order to minimize SSCHs, BS selects

FSCHs uniformly from whole network area. NST is very small as compare to NTT

and total duration of single NST is between the end of a NTT to start of next

NTT.

44

4.3.2 Network Transmission Time (NTT)

In MCEEC, during NTT all nodes send their data to CCHs, in assigned time

slots. CHs receive data from their clusters and aggregate data. This all phenom-

ena is called intra-cluster communication. Data aggregation is key technique to

compress data amount. CH performs data aggregation by utilizing signal pro-

cessing techniques. After aggregation, CHs route data packets to BS. CHs adopt

different techniques to transmit data to BS. This type of communication is called

inter-cluster communication.

In our proposed MCEEC routing protocol, all nodes have adjustable transmis-

sion power level for intra-cluster communication and inter-cluster communication.

Nodes initially have maximum transmission power level in NST, because nodes

have to communicate to BS. After cluster formation, nodes adjust their trans-

mission power level according to their communication responsibilities. Non-CH

node adjusts its transmission power level to reach its CH. CHs of LER and MER

adjust their transmission power level to adjust their communication range to next

region respectively. It will save energy, that dissipates due to fixed transmission

power level. In fixed transmission power level, all nodes have to transmit data at

maximum transmission power unnecessarily.

In our proposed model, Multi-hop communication is adopted for CCHs of LER

and MER. CHs of LER and MER select intermediate CHs with minimum com-

munication energy cost. Different techniques have been proposed for multi-hop

commination [10-13]. We prefer minimum distance based selection of intermedi-

ate CHs. In MCEEC, CHs of LER send data packets to CHs of MER. In similar

fashion CHs of MER transmit their data along with previous regions data to CHs

of HER. Only CHs of HER can directly communicate to BS. CHs of all three

regions have the location information of every FSCHs. BS provides this informa-

tion to all FSCHs. As we know that computational energy cost is neglect-able as

compare to transmission cost. Because of this, execution of MCEEC algorithm

allow CHs to calculate distance of multiple links and to select a link with overall

minimum distance to BS.

During NST and sensing environment all nodes consume specific energy. But

transmission energy cost is considered as major source of energy dissipation. Dur-

ing intra-cluster communication non-CHs and CHs pay communication cost. If we

consider a network with M × M area, then intra-cluster communication energy

dissipation of single CH will be:

45

ECHintra= ((

N

K− 1)(EeleRX × Lc)) +

n

K× Lc × EAD

where, ECHintrais energy consumed by CH during intra-cluster communication, K

is optimal number of clusters, N is total number of nodes, Lc is data bits received

from each node in with in cluster and EAD is data aggregation model. Energy

consumption for transmission by non-CH nodes of a single cluster will be:

EnonCHsintra= (

N

K− 1)(EeleRX × Lc + Eafs × Lc × d2toCH) (4.13)

where, EnonCHsintrais energy dissipated by all non-CHs nodes within one cluster,

dtoCH is distance between cluster-head and its member. Total energy dissipation

of intra-cluster transmission is:

ECintra= Enon−CHsintra

+ ECHintra(4.14)

where, ECintrais total energy cost of single cluster during intra-cluster communi-

cation. Intra-cluster energy cost of each region is same but inter-cluster commu-

nication will be different due to distance and data traffic load. If we calculate the

energy dissipation for a normal CH node of LER it will be:

EnCHinter= EeleTX × LA + Eafs × LA × d2toMER−CH (4.15)

where, ECHinteris energy dissipation of a CH of LER during inter-cluster commu-

nication, dtoMER−CH is distance between CH of MER and CH of LER. Normally

its value is equal to M√2πK

, and LA is aggregated data bits of that cluster. Energy

dissipation for a advance CH is:

EaCHinter= EeleRX × LA + EeleTX × (LA + LB)

+Eafs × (LA + LB)× d2toHER−CH (4.16)

where, EaCHinteris advance CH node energy dissipation, EeleRX ×LA is receiving

energy cost for LA data bits of source CH and LB is data bits of CH of MER.

From equation 16 it is easily understandable that advance nodes have more data

traffic burden (LA + LB) as compare to normal nodes, that’s why we consider

that advance nodes have more initial energy than normal nodes. Similarly energy

46

dissipation of a super CH node will be:

EsCHinter= EeleRX × (LA + LB) + EeleTX ×

(LA + LB + LC) + Eafs × (LA + LB + LC)× d2toBS (4.17)

where, LC is data bits of CH of HER and d2toBS is distance to BS and normally it

is equal to 0.765(M2) [4-5]. From equation 17 it is shown that super CH nodes have

maximum data for transmission and CHs of HER are only directly communicating

to BS. In order to estimate the energy dissipation of whole network we have

to sum the energy dissipation of intra-cluster communication and inter-cluster

communication. As we have assumed that, network is uniformly deployed and

every cluster has almost same number of CHs thats’s why intra-cluster energy

cost will be similar in all region’s cluster. if Nn × P = Kn are total number of

clusters in LER then total energy dissipation of LER will be:

ELER = Kn × (ECintra+ EnCHinter

) (4.18)

in the same way if Na × P = Ka are total number of clusters in MER then total

energy dissipation of MER will be:

EMER = Ka × (ECintra+ EaCHinter

) (4.19)

Similarly, if Ns × P = Ks are total number of clusters in HER then total energy

dissipation of HER will be:

EMER = Ks × (ECintra+ EsCHinter

) (4.20)

total number of clusters in whole network will be:

K = Ka +Kn +Ks (4.21)

total energy dissipation of all CHs will be:

ETdissipation= ELER + EMER + EHER (4.22)

where, ETdissipationis transmission energy cost for one round. If we have energy

dissipation of one round and total energy of network then network lifetime can be

estimated in terms of total number rounds. Total number of rounds will be:

R =ETdissipation

ET

(4.23)

47

duration of one round is NST+NTT seconds. NTT is designed longer as compare

to NST in order to maximize the period of realtime communication. Flow-chart

of MCEEC is shown in Fig 4.3.

Figure 4.3: Flow chart of MCEEC operation

4.4 Simulation Results and Discussion of MCEEC

performance

We simulate MCEEC along with LEACH, SEP, E-SEP and DEEC to analyze

the performance of our proposed protocol. We simulate these routing protocols

using MATLAB for two different scenarios. Value of α and Eo is kept same in both

48

scenarios. 100 nodes are deployed in 100m× 100m network area for first scenario

and in second scenario, 120 nodes are scattered in 210m× 210m network’s area.

In first simulations scenario, network area of 100m×100m is divided into three

rectangular regions LER, MER and HER. BS is placed at midway of x-axis of the

network. Table II shows all other important parameters of simulation. Before

Table 4.2: Simulation Parameters

Parameter value

Network size 100m * 100mInitial Energy .5 j

p .1 jData Aggregation Energy cost 50pj/bit j

number of nodes 100packet size 200 bit

Transmitter Electronics (EelectTx) 50 nj/bitReceiver Electronics (EelecRx) 50 nj/bit

Transmit amplifier (Eamp) 100 pj/bit/m2

we discuss the results, performance measurements are necessary to be defined.

Some performance measurements are given in [3-5].

1. Packet to BS: It shows the data amount received by BS from CHs.

2. Instability period: When nodes begin to die instability period is started and

it goes on till the death of last node.

3. Network lifetime: A period, from start of operation to death of last node is

called network lifetime

4. Stability period: It is duration of network operation over which all nodes

are alive and it continues until the death of first node.

5. Number of CHs: In each some nodes are selected as CHs. It also indicates

the number of clusters generated per round.

Results of simulations are described in following subsection.

4.4.1 Results for first Scenario

Fig. 4,4 shows the how many nodes are alive as the number of rounds increases.

Results indicates that MCEEC has better network lifetime time as compare to

other protocols. Stability of MCEEC is also significantly high and insure the

monitoring of entire region. Stability period of MCEEC is almost 250 %, 128 %,

49

100 %, 90 % is greater than LEACH, SEP, E-SEP and DEEC respectively. This

is because of well deployment planning of nodes, centralized clustering formation

and multi-hop communication in inter-cluster communication.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 55000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Num

ber

of a

live

node

s

LEACHDEECSEPESEPM−CEEC

Figure 4.4: Alive Nodes for 100m× 100m Network with 100 nodes in MCEEC

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 55000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Num

ber

of d

ead

node

s


Figure 4.5: Dead Nodes for 100m× 100m Network with 100 nodes in MCEEC

In Fig 4.5, numbers of dead node are described as network operation proceed.

Like earlier case, MCEEC performs much better to minimize dead nodes ratio as

rounds progress, in MCEEC, last node dies after 5100 rounds. Another significant

feature of MCEEC is that instability period starts very late in MCEEC as compare

to other routing protocols. MCEEC has resistance capability in instability period

and continues to send sensing reports from network field as long as possible. In

MCEEC, nodes do not start to die instantaneously in instability period as it

happens in SEP and LEACH case. This is because of MCEEC deals with nodes

according to their location and energy discrimination. MCEEC centrally varies the

transmission responsibilities of nodes according to their remaining resources that’s

why execution of MCEEC helps the nodes to prolong their lifetime. MCEEC is

performing much better as compare to other multi-hop routing protocols. This is

because of CHs have location information of all other CHs and can easily calculate

50

Figure 4.6: Total network lifetime with stability and instability period in MCEEC

the best energy saving route. In Fig 4.6, bar chart shows the overall network

lifetime, stability and instability periods of all routing protocols.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 55000

5

10

15

20

25

30

Number of rounds

Num

ber

of C

Hs

per

roun

d

SEPESEPDEECLEACHM−CEEC

Figure 4.7: Cluster-heads per round in MCEEC

In Fig 4.7 numbers of cluster-head per round are shown. MCEEC is provid-

ing required number of CHs continuously due to centrally selection of CHs. In

MCEEC, CHs are nor selected on probabilistic base. LEACH, SEP, E-SEP and

DEEC do not provide guaranteed number of CHs per round and it is because of

their distributed algorithms of CH’s selection. Their uneven CHs generation also

badly effect the amount packets received by BS from CHs. Results are shown in

Fig 4.8, in MCEEC, maximum numbers of packets are successfully received by

BS. This is because of optimal number of CHs provided by MCEEC. Another

main reason of throughput enhancement is multi-hop communication execution of

MCEEC, due to which CHs have to transmit to limited range.

51

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

4

Number of roundsN

umbe

r of

pac

kets


Figure 4.8: Packet to BS Nodes for 100m× 100m Network with 100 nodes in MCEEC

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 55000

10

20

30

40

50

60

70

80

90

100

110

120

Number of rounds

Num

ber

of a

live

node

s


Figure 4.9: Alive Nodes for 210m× 210m Network with 120S nodes in MCEEC

4.4.2 Results for second Scenario

We also simulate LEACH, SEP, E-SEP, DEEC and MCEEC for 120 nodes that

are scattered in 210m× 210m network’s area. We do not alter the value of α and

initial energy normal nodes for SEP, E-SEP and DEEC. We also keep energy of

advance and super nodes same as it is in first simulation scenario for our proposed

protocol.

It is shown in results that efficiency of all routing protocols is decreased signifi-

cantly. But comparatively, MCEEC perform much better scalability and stability

than other clustering routing protocols. If we analyze performance with respect

of stability of SEP, E-SEP and DEEC, it is reduced dramatically. Fig 4.9 and

Fig 4.10 show network lifetime with respect to alive and dead nodes respectively.

MCEEC has almost 100 %, 90 %, 85 %, 70 % better stability as compared to

LEACH, SEP, E-SEP and DEEC respectively. LEACH stability period is already

very small that’s why stability period decrement is noticeable as area of network is

increases. Comparatively MCEEC conserves better stability period. From Fig 10

it is noticeable that instability period for MCEEC is very large as compare to first

52

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 55000

10

20

30

40

50

60

70

80

90

100

110

120

Number of roundsN

umbe

r of

dea

d no

des


Figure 4.10: Dead Nodes for 210m× 210m Network with 120S nodes in MCEEC

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 55000

5

10

15

20

25

30

35

40

45

50

Number of rounds

Num

ber

of C

Hs

per

roun

ds

DEECLEACHSEPESEPM−CEEC

Figure 4.11: Cluster-heads per round for 210m × 210m Network with 120S nodes inMCEEC

scenario. This is because of advance and super nodes in MMCEEC are scattered

at larger distance and energy dissipation is very high.

Fig 4.11 shows the CHs selection per round for 210m×210m network’s area. It is

interesting to see that CHs selection has become more challenging when network

diameter and number of nodes are increased. This is because of probabilistic

distributed algorithm of cluster-head selection in LEACH, SEP, E-SEP and DEEC.

As shown in fig 4.12, fluctuations in CHs selection per round has been increased

in this case. SEP, E-SEP and DEEC define different probabilities for nodes with

different energies. That’s why CHs selections will become more and more complex

if the number of nodes increase in SEP, E-SEP and DEEC. In MCEEC, cluster-

head’s selection is centralized and it provides similar results like first scenario.

Fig 111 shows the simulations evidence that proposed MCEEC routing protocol

performs better in successful packet transmission to BS.

Fig 4.13 shows the stability periods for LEACH, SEP, E-SEP, DEEC and

MCEEC for both scenarios. Blue bar charts values are showing stability peri-

53

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 55000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

4

Number of roundsN

umbe

r of

pac

kets


Figure 4.12: Packet to BS for 210m× 210m Network with 120S nodes in MCEEC

ods for 100m × 100m network’s area and red values for 210m × 210m network’s

area. From this Fig, it is shown that stability period of MCEEC is significantly

longer than other protocols in both scenario.

Figure 4.13: Stability period for 210m× 210m Network with 120S nodes in MCEEC

54

Chapter 5

Conclusion

55

Chapter 5

Conclusion

5.1 Conclusion and Future work

This thesis discussed different clustering schemes that have been executed in

both heterogeneous and homogeneous WSNs that are hierarchically clustered. En-

ergy heterogeneity is one of the key consideration in design of WSNs protocols.

As these sensors are battery-operated, energy management has been one of the

core objectives for protocol design.As already discussed in this thesis, network

researchers have worked on extending the network life-time of WSN, but there

still exists the need for a more robust protocol design that is heterogeneity-aware.

Clustering scheme, originally inspired by [3] and further enhanced by [4] was pro-

posed to cope with energy distribution in WSN. Following this thoughts, this thesis

developed CEEC and MCEEC an adaptive clustering algorithm that is centralized

in three-level hierarchy using three types of nodes: normal nodes, advance nodes

and super nodes in a heterogeneous setting. This idea improved on the LEACH-

C, SEP and DEEC protocol by considering an energy heterogeneous environment.

The LEACH protocol assumed an energy homogeneous system, where all nodes

have equal energy at the beginning of the network operation. We have developed

distributed heterogeneous network for our proposed model.

In this Thesis, we proposed a CEEC and MCEEC routing protocol for three

level heterogeneous wireless sensor networks. Network deployment is also a key

technique in CEEC routing protocol. We divided the network area into three

equal regions. Instead of spreading nodes randomly, we deployed same type of

nodes with respect to energy in one region. BS selects cluster-heads for all three

regions, and nodes can only associate with their own type of cluster-heads. CEEC

and MCEEC are first centralized clustering algorithm that supports heterogeneous

56

networks. Simulations results provide view of performance enhancement achieved

by CEEC and MCEEC as compared to LEACH, SEP, E-SEP, DEEC for WSNs.

CEEC and MCEEC can also perform better than DEEC, SEP, E-SEP in multi-

level heterogeneous sensor network. CEEC provides maximum network lifetime,

throughput and stability for the network nodes.

This research work has open-up new possible ways of network deplyment schemes.

This research work also encourage the new idea to introduce heterogeneity in

location-based and flat routing protocol. In order to obtain better network con-

trol, central algorithms are more efficient as compared to distributed algorithm

schemes. Resource distribution according to the responsibilities of nodes also sup-

port the idea to extend stability and network lifetime.

57

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geneous network and heterogeneous networks [1-2], [3-6]. homogeneous networks contain sensor nodes...

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