vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xiii
LIST OF FIGURES
xiv
LIST OF ABBREVIATIONS
xvii
LIST OF APPENDICES
xx
INTRODUCTION
1
1.1
Overview
1
1.2
Background of Study
4
1.2.1 Finding the Nearest Sink in Multi-sink Mechanism
5
1.2.2 Mobile Sink Localization and Optimum Position
for Sink Movement
6
1.2.3 Selecting the Best Next-hop Node in Single Path
Geographic Routing
8
1.3
Problem Description
10
1.4
Statement of Objectives
11
1.5
Research Questions
12
1.6
Research Scope
12
1.7
Research Contributions
13
1.8
Significance of Research
15
viii
1.9
2
Organization of Thesis
16
LITERATURE REVIEW
17
2.1
Introduction
17
2.2
Background and Preliminaries
19
2.2.1 Wireless Sensor Network Architecture
19
2.2.2 Sources of Energy Consumption in WSNs
20
2.3
Classification of Routing Protocols Based on
Network Structure in WSNs
21
2.3.1 Flat Routing Protocols
21
2.3.2 Hierarchical Routing Protocols
22
2.3.3 Geographic Routing Protocols
22
2.3.3.1 Merely Greedy Forwarding Scheme
23
2.3.3.2 Optimized Greedy Forwarding
Scheme
2.4
Classification of Routing Mechanisms Based on
Protocol Operation in WSNs
31
2.4.1 Multi-path Routing
32
2.4.2 QoS-based Routing
33
2.4.3 Bio-inspired Routing
33
2.4.4 Query-based Routing
34
2.4.5 Multi-sink Routing
35
2.4.5.1 Multiple Static Sinks Strategy
35
2.4.5.2 Multiple Mobile Sinks Strategy
39
2.4.5.3 Hybrid Strategy
41
2.4.6 Mobile Sink Routing
2.5
2.6
27
44
2.4.6.1 Random/Stochastic Sink Mobility
44
2.4.6.2 Fixed Path/Predictable Sink Mobility
47
2.4.6.3 Controlled Sink Mobility
48
Discussion on Limitations of Existing Routing
Mechanisms
53
2.5.1 Network Structure Based Routing Mechanisms
54
2.5.2 Protocol Operation Based Routing Mechanisms
57
Chapter Summary
64
ix
3
RESEARCH METHODOLOGY
65
3.1
Introduction
65
3.2
Operational Framework
67
3.2.1 Phase 1: Investigation in WSNs
67
3.2.2 Phase 2: Algorithm Design and Development
67
3.2.2.1 Sink Selection Method
68
3.2.2.2 Sink Mobility Pattern
69
3.2.2.3 Forwarding Scheme
70
3.2.3 Phase 3: Evaluation
71
3.2.3.1 Comparison with Baseline Approaches
72
3.2.3.2 Metric of Comparison
73
3.2.3.3 Simulation Setup and Network
Configuration
3.2.3.4 Reliability of the Simulation
3.3
4
73
74
3.2.4 Phase 4: Revision
76
Chapter Summary
79
ENERGY-EFFICIENT DUAL-SINK ALGORITHM
WITH ROLE SWITCHING MECHANISM (EEDARS)
80
4.1
Introduction
80
4.2
Network Model and Preliminaries
80
4.2.1 Topology and Data Routing
81
4.2.2 Event Occurrence in Proposed Model
82
4.2.3 Modeling the Energy Consumption and Delay
83
The EEDARS Algorithm
88
4.3.1 Network Initialization
88
4.3.2 Network Functionality
89
4.3.3 The Role Switching Mechanism
90
4.3.4 Joint Routing Strategy and Sink Mobility
93
4.3
4.3.5 Analytical Analysis of Residual Energy and
4.4
Delay for EEDARS
95
4.3.5.1 Evaluation of Residual Energy
95
4.3.5.2 Evaluation of Delay
98
Multi-event Scenario
99
x
4.5
Simulation Results
100
4.5.1 Network Lifetime Evaluation for EEDARS
101
4.5.2 Residual Energy Evaluation for EEDARS
103
4.5.3 Number of Nodes Alive Evaluation for EEDARS 103
4.6
5
4.5.4 Delay Evaluation for EEDARS
104
4.5.5 Delivery Ratio Evaluation for EEDARS
107
4.5.6 Network Load Evaluation for EEDARS
109
4.5.7 Energy Consumption Evaluation for EEDARS
110
Chapter Summary
112
ENERGY-EFFICIENT DUAL-SINK ALGORITHM WITH
FUZZY-BASED SINK MOBILITY PATTERN (EDAFSM)
113
5.1
Introduction
113
5.2
Fuzzy Logic Based Optimization
114
5.3
Model of Network
114
5.4
The EDAFSM algorithm
115
5.4.1 Initialization Phase of Network
115
5.4.2 Fuzzy Sink Mobility Scheme
116
5.4.2.1 System Inputs
117
5.4.2.2 Fuzzification
117
5.4.2.3 Fuzzy Inference
119
5.4.2.4 Defuzzification
120
5.4.2.5 Determination of Sink Destination
120
5.5
Experimental Results
123
5.5.1 EDAFSM Network Lifetime Evaluation
123
5.5.2 EDAFSM Number of Nodes Alive
Evaluation
125
5.5.3 EDAFSM Packet Loss Evaluation
127
5.5.4 EDAFSM End-to-end Delay Evaluation
128
5.5.5 EDAFSM Delivery Ratio Evaluation
130
5.5.6 EDAFSM Network Load Evaluation
131
5.5.7 EDAFSM Energy Consumption Evaluation
132
5.6
EEDARS versus EDAFSM
134
5.7
Chapter Summary
137
xi
6
JOINT DUAL-SINK AND FUZZY-BASED GEOGRAPHIC
ROUTING (JDFGR)
138
6.1
Introduction
138
6.2
JDFGR-S and JDFGR-M Algorithms
139
6.2.1 Fuzzy-based Greedy Forwarding (FGF) Scheme
139
6.2.1.1 Fuzzy Inputs for FGF
140
6.2.1.2 Normalization Phase in FGF
141
6.2.1.3 Fuzzy Rules for FGF
142
6.2.1.4 Fuzzy Output for FGF
143
6.2.1.5 Determination of Next Hop by FGF
144
6.3
Integration of Dual-sink and FGF Scheme
146
6.4
Performance Evaluation
146
6.4.1 Evaluation the Network Lifetime for JDFGR-S
and JDFGR-M
147
6.4.2 Evaluation the Average Residual Energy for
JDFGR-S and JDFGR-M
149
6.4.3 Evaluation the Number of Nodes Alive for
JDFGR-S and JDFGR-M
151
6.4.4 Evaluation the Packet Loss for JDFGR-S and
JDFGR-M
153
6.4.5 Evaluation the End-to-end Delay for JDFGR-S
and JDFGR-M
155
6.4.6 Evaluation the Delivery Ratio for JDFGR-S and
JDFGR-M
157
6.4.7 Evaluation the Network Load for JDFGR-S and
6.5
7
JDFGR-M
159
Chapter Summary
161
CONCLUSION AND FUTURE WORKS
163
7.1
Objective Achievement
163
7.2
Discussion on Contributions
167
7.3
Limitations of the Research
169
7.4
Future Work
170
7.4.1 Multi-path Routing
170
xii
REFERENCES
Appendices A – B
7.4.2 Cross-layer Mechanism
170
7.4.3 Obstacle Avoidance Method
171
172
182 – 183
xiii
LIST OF TABLES
TABLE NO.
TITLE
1.1
Research questions related to the study
2.1
Comparison of greedy forwarding mechanisms in
geographic routings
2.2
13
56
Comparison of sink selection mechanisms in multi-sink
routings
2.3
PAGE
58
Comparison of sink movement mechanisms in mobile
sink routings
60
3.1
Simulation parameters and related values
75
3.2
Simulation results for two baselines in different researches
75
3.3
Research framework for proposed energy-efficient
joint dual-sink and geographic routing
4.1
Energy saving through hop reduction for 10 packets
on a path with 9 hops
4.2
77
97
The delay minimization through hop reduction for 10
packets on a path with 9 hops
99
5.1
Fuzzy rules in the knowledge base
119
5.2
The priority of neighboring nodes based on cost and energy
123
6.1
Fuzzy rules in the knowledge base
144
xiv
LIST OF FIGURES
FIGURE NO.
2.1
TITLE
PAGE
Taxonomy of energy-efficient routing protocols in
wireless sensor networks
18
2.2
The structure of a typical wireless sensor node
20
2.3
The node x in greedy forwarding scheme is S’s nearest
neighbor to D
2.4
23
Delaunay triangulation (DT) structure and data forwarding
path on the edges of DTs
26
2.5
Path selection with minimum hop count
37
2.6
Different number of sojourn points and covered area by the
sinks
43
2.7
Joint routing mechanism and sink mobility
48
2.8
Grid strategy to support sink mobility
51
3.1
The steps of research methodology (operational framework)
66
4.1
The sensor field and the source nodes (five dark gray nodes
on the circle) involved in an event
81
4.2
Path selection in EEDARS
82
4.3
Past events (event 1 & 2) and new event (event 3)
accompany with sensors involved in routing
4.4
84
Rthreshold as maximum range for sink movement from the
center of field based on the sink speed
91
4.5
Two scenarios for role switching mechanism in EEDARS
92
4.6
Sink movement pattern towards the source nodes. a-d) The
sink moves three hops diagonally. e) The sink moves one
hop horizontally
94
xv
4.7
The number of diagonal and horizontal hops between the
mobile sink and the center of event region in an example
with 9 hops and 10 packets
95
4.8
The Multi-event scenario
100
4.9
Average lifetime and related standard deviation value
(source nodes produce 10 packets)
4.10
101
Average lifetime and related standard deviation value
(source nodes produce 30 packets)
102
4.11
Average residual energy
104
4.12
Average number of nodes alive
105
4.13
Average end-to-end delay and related standard deviation
value for all six strategies (source nodes produce 10 packets) 106
4.14
Average end-to-end delay and related standard deviation
value for all six strategies (source nodes produce 30 packets) 107
4.15
Average delivery ratio
108
4.16
Network load at the end of simulation for all six strategies
109
4.17
Energy consumption for all strategies
111
5.1
Three simultaneous events accompany with sensors
involved in routing
5.2
115
Membership functions for the fuzzy input variables cost
and E
118
5.3
Membership functions for the output variable “priority”
120
5.4
Determining next destination by fuzzy-based sink
mobility scheme.
5.5
Average network lifetime and standard deviation value
(source nodes report 10 packets)
5.6
122
124
Average network lifetime and standard deviation value
(source nodes report 30 packets)
125
5.7
Average number of nodes alive
126
5.8
Average packet loss
127
5.9
Average delay and standard deviation values for all
strategies (sources produce 10 packets)
5.10
Average delay and standard deviation value for all
128
xvi
strategies (sources produce 30 packets)
129
5.11
Average delivery ratio
130
5.12
Network load for all six strategies at the end of simulation
132
5.13
Energy consumption diagrams for all mobile sink strategies
133
5.14
Average network lifetime for single-event scenario
134
5.15
Average residual energy for single-event scenario
135
5.16
Average number of nodes alive for single-event scenario.
a) Source nodes report 10 packets. b) Source nodes report
30 packets
5.17
135
Average packet loss for single-event scenario. a) Source
nodes report 10 packets. b) Source nodes report 30 packets
136
5.18
Average end-to-end delay for single-event scenario
136
5.19
Average delivery ratio for single-event scenario. a) Source
nodes report 10 packets. b) Source nodes report 30 packets
137
6.1
Number of source neighbors closer to the sink
140
6.2
Membership functions for the fuzzy input variables Hop, E
and Radius
142
6.3
Membership functions for the output variable “priority”
143
6.4
Average network lifetime for single-event scenario
148
6.5
Average network lifetime for multi-event scenario
149
6.6
Average residual energy for single-event scenario
150
6.7
Average residual energy for multi-event scenario
151
6.8
Average number of nodes alive for single-event scenario
152
6.9
Average number of nodes alive for multi-event scenario
153
6.10
Average packet loss for single-event scenario
154
6.11
Average packet loss for multi-event scenario
155
6.12
Average delay for single-event scenario
156
6.13
Average delay for multi-event scenario
157
6.14
Average delivery ratio for single-event scenario
158
6.15
Average delivery ratio for multi-event scenario
159
6.16
Network load for single-event scenario
160
6.17
Network load for multi-event scenario
161
xvii
LIST OF ABBREVIATIONS
ACO
-
Ant Colony Optimization
ADV
-
Advertisement
AGEM
-
Adaptive Greedy-Compass Energy-Aware Multi-path
AODV
-
Ad hoc On-demand Distance Vector
APTEEN
-
Adaptive Periodic Threshold-sensitive Energy-Efficient sensor
Network protocol
CAGIF
-
Channel-Aware Geographic-Informed Forwarding
CD
-
Critical Degree
CDMA
-
Code Division Multiple Access
CHs
-
Cluster Heads
CPL
-
Cumulative Path Load
CPU
-
Central Processing Unit
CTS
-
Clear To Send
DD
-
Directed Diffusion
D-MPR
-
Disjoint Multi-path Routing
DSA
-
Dynamic Sampling Algorithm
DSR
-
Dynamic Source Routing
DT
-
Delaunay Triangulation
DT-MSM
-
Delay-Tolerant Mobile Sink Model
EAM
-
Efficient Advancement Metric
EAR
-
Energy-Aware Routing
EEABR
-
Energy-Efficient Ant Based Routing
EEDARS
-
Energy-Efficient Dual-sink Algorithm with Role Switching
Mechanism
ED
-
Exploratory Data
EDAFSM
-
Energy-efficient Dual-sink Algorithm with Fuzzy-based Sink
Mobility Pattern
xviii
ELBR
-
Energy Level Based Routing
EM-GMR
-
Energy and Mobility-aware Geographical Multipath Routing
FEEM
-
Fault-tolerant and Energy-Efficient Multi-path routing
FGF
-
Fuzzy-based Greedy Forwarding
GAF
-
Geographic Adaptive Fidelity
GEAR
-
Geographic and Energy Aware Routing
GLOBAL
-
Gradient-based routing protocol for LOad BALancing
GMR
-
Geographical Multipath Routing
GPS
-
Global Positioning System
GPSR
-
Greedy Perimeter Stateless Routing
ID
-
Identifier
IEEE
-
Institute of Electrical and Electronics Engineers
ILP
-
Integer Linear Programming
JDFGR-M
-
Joint Dual-sink and Fuzzy-based Geographic Routing for
enhancing lifetime in Mingle-event
JDFGR-S
-
Joint Dual-sink and Fuzzy-based Geographic Routing for
enhancing lifetime in Single-event
LEAN
-
Local Event ANnouncer
LEACH
-
Low-Energy Adaptive Clustering Hierarchy
MAC
-
Media Access Control
MCFA
-
Minimum Cost Forwarding Algorithm
MFR
-
Most Forwarding Routing
MGF
-
Merely Greedy Forwarding
MLBRF
-
Multi-Sink Load Balanced Reliable Forwarding protocol
M-MPR
-
Mesh Multi-path Routing
MS
-
Mobile Sink
MSDD
-
Multi-Sink Directed Diffusion
MSLBR
-
Multi-Sink and Load-Balance Routing
MSRP
-
Mobile Sink Based Routing Protocol
NS2
-
Network Simulator 2
OBGR
-
Online-Battery aware Geographic Routing algorithm
PBR
-
Primary Based Routing
PEGASIS
-
Power-Efficient Gathering in Sensor Information Systems
PR
-
Packet Replication
xix
QoS
-
Quality of Service
REDR
-
Residual Energy Depletion Rate
REM
-
Rule Evaluation Method
RFID
-
Radio Frequency Identification
RTS
-
Request To Send
RWP
-
Random Way Point
SF
-
Selective Forwarding
SPIN
-
Sensor Protocols for Information via Negotiation
SPR
-
Shortest Path Routing
SS
-
Static Sink
TDMA
-
Time Division Multi Access
TEEN
-
Threshold-sensitive Energy-Efficient sensor Network protocol
TTDD
-
Two Tier Data Dissemination
TTL
-
Time To Live
WLDT
-
Weighted Localized Delaunay Triangulation-based data
forwarding
WLDT w/c
-
Weighted Localized Delaunay Triangulation-based data
forwarding without checkpoints
WSNs
-
Wireless Sensor Networks
xx
LIST OF APPENDICES
APPENDICES
TITLE
PAGE
A
Calculating the Centroid of N Point
182
B
A List of Publications
183