ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR NODES
geneous network and heterogeneous networks [1-2], [3-6]. Homogeneous networks contain sensor nodes...
Transcript of 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
Routing Protocol for Wireless Sensor Networks
A Thesis presented to
COMSATS Institute of Information Technology
In partial fulfillment
of the requirement for the degree of
MS (Electrical Engineering)
By
Mr. Muhammad Aslam
CIIT/FA10-REE-008/ISB
Spring, 2012
COMSATS Institute of Information Technology
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
CEEC: Centralized Energy Efficient Clustering
Routing Protocol For Wireless Sensor Network
iii
Final Approval
This thesis titled
CEEC: Centralized Energy Efficient Clustering
Routing Protocol for Wireless Sensor Networks
By
Mr. Muhammad Aslam
CIIT/FA10-REE-008/ISB
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
Department of Electrical Engineering
Islamabad Campus
HoD: ________________________
Dr. Shafayat Abrar / Associate professor
Department of Electrical Engineering
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
CIIT/FA10-REE-008/ISB
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
Department of Electrical Engineering
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
CIIT/FA10-REE-008/ISB
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.
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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
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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
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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
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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
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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.
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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.
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• 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
LEACHDEECSEPESEPCEEC
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
LEACHDEECSEPESEPCEEC
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
LEACHDEECSEPESEPCEEC
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
LEACHDEECSEPESEPCEEC
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
LEACHDEECSEPESEPM−CEEC
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
LEACHDEECSEPESEPM−CEEC
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
LEACHDEECSEPESEPM−CEEC
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
LEACHDEECSEPESEPM−CEEC
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
LEACHDEECSEPESEPM−CEEC
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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