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E-raamat: Clustering and Routing Algorithms for Wireless Sensor Networks: Energy Efficiency Approaches [Taylor & Francis e-raamat]

(KIIT University, Bhubaneswar, India),
  • Formaat: 224 pages, 14 Tables, black and white; 17 Line drawings, black and white; 53 Halftones, black and white; 70 Illustrations, black and white
  • Ilmumisaeg: 20-Sep-2017
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781315152660
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Clustering and Routing Algorithms for Wireless Sensor Networks: Energy Efficiency ApproachesSuurem pilt
  • Formaat: 224 pages, 14 Tables, black and white; 17 Line drawings, black and white; 53 Halftones, black and white; 70 Illustrations, black and white
  • Ilmumisaeg: 20-Sep-2017
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9781315152660
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781315152660

Wireless Sensor Networks have a wide range of applications in different areas. Their main constraint is the limited and irreplaceable power source of the sensor nodes. In many applications, energy conservation of the sensor nodes and their replacement or replenishment due to the hostile nature of the environment is the most challenging issue. Energy efficient clustering and routing are the two main important topics studied extensively for this purpose. This book focuses on the energy efficient clustering and routing with a great emphasis on the evolutionary approaches. It provides a comprehensive and systematic introduction of the fundamentals of WSNs, major issues and effective solutions.

Preface xiii
Who Can Use This Book xv
About the Authors xvii
Acknowledgments xix
Organization of the Book xxi
List of Tables
xxiii
List of Figures
xxv
List of Algorithms
xxix
1 Introduction
3(16)
1.1 Background of wireless sensor networks
3(1)
1.2 Structure of sensor nodes
4(2)
1.3 WSN architecture
6(2)
1.4 WSNs vs. ad-hoc networks
8(1)
1.5 Challenges in WSNs
9(1)
1.6 System models
10(3)
1.6.1 Network model
10(2)
1.6.2 Energy model
12(1)
1.6.3 Fault model
12(1)
1.7 Clustering and routing algorithms
13(6)
1.7.1 Challenges in WSN clustering
14(1)
1.7.2 Challenges in WSN routing
15(4)
2 WSN Applications
19(8)
2.1 Forest fire detection
19(1)
2.2 Flood detection
19(1)
2.3 Tsunami detection
20(1)
2.4 Home applications
20(1)
2.5 Agricultural applications
21(1)
2.6 Traffic tracking
22(1)
2.7 Military applications
23(1)
2.8 Industrial applications
23(1)
2.9 Sensors in education
23(1)
2.10 Healthcare
24(1)
2.11 Underground coal mining
25(1)
2.12 WSNs and IoT
25(2)
3 A Survey
27(18)
3.1 Approximation algorithms
27(1)
3.2 Centralized algorithms
28(1)
3.3 Metaheuristic algorithms
29(5)
3.3.1 Genetic algorithm-based routing by Bari et al.
29(1)
3.3.2 GAR
30(1)
3.3.3 GA based hierarchical clustering by Sajid et al.
31(1)
3.3.4 Energy aware evolutionary routing protocol (EAERP)
31(3)
3.4 Distributed algorithms
34(8)
3.4.1 Flooding
34(1)
3.4.2 Gossiping
34(1)
3.4.3 Sensor Protocol for Information via Negotiation (SPIN)
35(1)
3.4.4 Directed Diffusion (DD)
36(2)
3.4.5 Low-energy adaptive clustering hierarchy (LEACH)
38(1)
3.4.6 Efficient Routing-LEACH (ER-LEACH)
39(1)
3.4.7 Adaptive and energy efficient clustering (AEEC)
39(1)
3.4.8 Power-efficient gathering in sensor information systems (PEGASIS)
39(1)
3.4.9 Hybrid energy efficient distributed clustering (HEED)
40(1)
3.4.10 TEEN and APTEEN
40(1)
3.4.11 DEEC: Distributed energy efficient clustering
40(1)
3.4.12 DEBR: Distributed energy balance routing
41(1)
3.4.13 Exponential and Sine Cost Function based Routing
41(1)
3.5 Mobile sink routing (MSR)
42(3)
4 Approximation Algorithms for Clustering
45(18)
4.1 Introduction
45(1)
4.2 System model and problem formulation
46(2)
4.3 Terminologies
48(1)
4.4 Load balanced clustering algorithms
49(11)
4.4.1 Algorithm for equal load
49(2)
4.4.2 An illustration
51(3)
4.4.3 Approximation algorithms for unequal load
54(2)
4.4.4 Performances based on load balancing
56(1)
4.4.5 Execution time
57(1)
4.4.6 Heap based approximation algorithm
58(2)
4.5
Chapter summary
60(3)
5 Centralized Algorithms
63(10)
5.1 Introduction
63(1)
5.2 Load balancing algorithms
63(4)
5.3 Energy efficient algorithm
67(4)
5.4
Chapter Summary
71(2)
6 Metaheuristic Approaches
73(44)
6.1 Introduction
73(2)
6.2 System model and terminologies
75(1)
6.3 Genetic algorithms for clustering
76(8)
6.3.1 An overview of genetic algorithm
76(1)
6.3.2 GA based clustering
77(1)
6.3.2.1 Problem formulation
77(1)
6.3.2.2 Chromosome representation
78(1)
6.3.2.3 Initial population
79(1)
6.3.2.4 Fitness function
80(1)
6.3.2.5 Selection
80(1)
6.3.2.6 Crossover
81(1)
6.3.2.7 Mutation
81(3)
6.4 Differential evolution for clustering and routing
84(12)
6.4.1 An overview of differential evolution
84(2)
6.4.2 DE based clustering
86(1)
6.4.2.1 Problem formulation
86(1)
6.4.2.2 Initialization of the population vector
87(1)
6.4.2.3 Derivation of fitness function
88(3)
6.4.2.4 Mutation
91(1)
6.4.2.5 Crossover
92(1)
6.4.2.6 Local improvement
92(2)
6.4.2.7 Selection
94(2)
6.5 Particle swarm optimization based algorithms
96(19)
6.5.1 An overview of particle swarm optimization
97(2)
6.5.2 PSO based routing
99(1)
6.5.2.1 LP Formulation for Routing Problem
99(1)
6.5.2.2 Routing algorithm
99(4)
6.5.3 PSO Based Clustering
103(1)
6.5.3.1 NLP Formulation for clustering problem
103(1)
6.5.3.2 Clustering algorithm
104(11)
6.6
Chapter summary
115(2)
7 Distributed Algorithms
117(22)
7.1 Introduction
117(2)
7.2 Network model and terminologies
119(1)
7.3 Distributed cost based algorithm
120(7)
7.3.1 Clustering algorithms
121(1)
7.3.1.1 Selection of CHs
121(1)
7.3.1.2 Cluster setup
122(3)
7.3.2 Routing algorithm
125(2)
7.4 Distributed energy efficient algorithm
127(9)
7.4.1 Clustering algorithms
128(1)
7.4.1.1 Selection of CHs
128(1)
7.4.1.2 Cluster setup
129(1)
7.4.2 Routing algorithms
130(6)
7.5
Chapter summary
136(3)
8 Fault Tolerant Algorithms
139(18)
8.1 Introduction
139(1)
8.2 Fault tolerant algorithms
140(14)
8.2.1 Clustering algorithm
140(1)
8.2.1.1 Bootstrapping
141(1)
8.2.1.2 Distributed clustering algorithm
142(3)
8.2.1.3 Fault tolerance
145(2)
8.2.2 Routing algorithm
147(1)
8.2.2.1 Bootstrapping
147(1)
8.2.2.2 Distributed routing
147(7)
8.3
Chapter summary
154(3)
9 Unequal Clustering and Mobile Sink-Based Routing Algorithms
157(18)
9.1 Introduction
157(1)
9.2 Unequal clustering algorithms
157(4)
9.2.1 Energy-efficient unequal clustering (EEUC)
157(2)
9.2.2 Multi-hop routing protocol with unequal clustering (MR-PUC)
159(1)
9.2.3 Grid based fault tolerant clustering and routing algorithms
160(1)
9.2.4 Unequal multi-hop balanced immune clustering protocol
160(1)
9.3 Mobile sink routing (MSR) schemes
161(13)
9.3.1 Clustering and routing algorithms
162(1)
9.3.1.1 MobiCluster
162(6)
9.3.1.2 Cluster based routing
168(6)
9.4
Chapter summary
174(1)
Bibliography 175(16)
Index 191
Prasanta K. Jana received M. Tech. degree in Computer Science from University of Calcutta, in 1988 and Ph. D. from Jadavpur University in 2000. Currently he is a Professor in the department of Computer Science and Engineering, Indian School of Mines,Dhanbad, India. He has contributed 109 research publications in his credit and coauthored five books. He has also produced six Ph.Ds. As a recognition of his outstanding research contributions, he has been awarded Senior Member of IEEE(The Institute of Electrical and Electronics Engineers, USA) in 2010. He is in the editorial board of two international journals and acted as referees in many reputed international journals including Journal of Parallel & Distributed Computing, IEEE Transaction on Parallel & Distributed Systems, IEEE Communication Letters, Algorithmica, Journal of Supercomputing, Pattern recognition etc. Dr. Jana has served as the General Chair of the International Conference RAIT-2012, Co-chair of national conference RAIT-2009 and the workshop WPDC-2008. He has also acted as the PC members of several conferences of national and international reputes.His current research interest includes wireless sensor networks, parallel and distributed computing, cloud computing and data clustering. He visited University of Aizu, Japan, Las Vegas, USA, Imperial college of London, UK and University of Macau, Macau for academic purpose.



Pratyay Kuila received his B.Tech degree in 2008 and M.Tech degree in 2011 both in Computer Science and Engineering from West Bengal University of Technology, Kolkata. He has received his PhD degree in 2014 from Indian School of Mines, Dhanbad. Currently, he is Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar. He acted as referees in many reputed international journals including Ad Hoc Networks, Telecommunication Systems, etc. He has contributed 14 research papers in the field of wireless sensor networks. His main research interest is to develop clustering and routing algorithms for Wireless Sensor Networks.
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