Clustering and Routing Algorithms for Wireless Sensor Networks: Energy Efficiency Approaches

225 Pages·2018·11.01 MB·English

by  Pratyay Kuila

CLUSTERING
AND ROUTING
ALGORITHMS
FOR WIRELESS
SENSOR
NETWORKS
Energy Efficient
Approaches

CLUSTERING
AND ROUTING
ALGORITHMS
FOR WIRELESS
SENSOR
NETWORKS
Energy Efficient
Approaches
Pratyay Kuila
Prasanta K. Jana
CRC Press
Taylor & Francis Group
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Version Date: 20170823
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Contents
Preface xiii
WhoCanUseThisBook xv
AbouttheAuthors xvii
Acknowledgments xix
OrganizationoftheBook xxi
ListofTables xxiii
ListofFigures xxv
ListofAlgorithms xxix
1 Introduction 3
1.1 Backgroundofwirelesssensornetworks . . . . . . . . . . . . . . 3
1.2 Structureofsensornodes . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 WSNarchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 WSNsvs.ad-hocnetworks . . . . . . . . . . . . . . . . . . . . . . 8
1.5 ChallengesinWSNs . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6 Systemmodels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.1 Networkmodel . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.2 Energymodel . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.6.3 Faultmodel . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.7 Clusteringandroutingalgorithms . . . . . . . . . . . . . . . . . . 13
1.7.1 ChallengesinWSNclustering . . . . . . . . . . . . . . . . 14
1.7.2 ChallengesinWSNrouting . . . . . . . . . . . . . . . . . 15
2 WSNApplications 19
2.1 Forestfiredetection . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Flooddetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3 Tsunamidetection . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4 Homeapplications . . . . . . . . . . . . . . . . . . . . . . . . . . 20
vii
viii Contents
2.5 Agriculturalapplications . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 Traffictracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.7 Militaryapplications . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.8 Industrialapplications . . . . . . . . . . . . . . . . . . . . . . . . 23
2.9 Sensorsineducation . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.10 Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.11 Undergroundcoalmining . . . . . . . . . . . . . . . . . . . . . . 25
2.12 WSNsandIoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 ASurvey 27
3.1 Approximationalgorithms . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Centralizedalgorithms . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Metaheuristicalgorithms . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.1 Geneticalgorithm-basedroutingbyBarietal. . . . . . . . . 29
3.3.2 GAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3.3 GAbasedhierarchicalclusteringbySajidetal. . . . . . . . 31
3.3.4 Energyawareevolutionaryroutingprotocol(EAERP) . . . 31
3.4 Distributedalgorithms . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.1 Flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.2 Gossiping . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.3 SensorProtocolforInformationviaNegotiation(SPIN) . . . 35
3.4.4 DirectedDiffusion(DD) . . . . . . . . . . . . . . . . . . . 36
3.4.5 Low-energyadaptiveclusteringhierarchy(LEACH) . . . . 38
3.4.6 EfficientRouting-LEACH(ER-LEACH) . . . . . . . . . . 39
3.4.7 Adaptiveandenergyefficientclustering(AEEC) . . . . . . 39
3.4.8 Power-efficientgatheringinsensorinformationsystems(PE-
GASIS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4.9 Hybridenergyefficientdistributedclustering(HEED) . . . 40
3.4.10 TEENandAPTEEN . . . . . . . . . . . . . . . . . . . . . 40
3.4.11 DEEC:Distributedenergyefficientclustering . . . . . . . . 40
3.4.12 DEBR:Distributedenergybalancerouting . . . . . . . . . 41
3.4.13 ExponentialandSineCostFunctionbasedRouting . . . . . 41
3.5 Mobilesinkrouting(MSR) . . . . . . . . . . . . . . . . . . . . . 42
4 ApproximationAlgorithmsforClustering 45
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 Systemmodelandproblemformulation . . . . . . . . . . . . . . . 46
4.3 Terminologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.4 Loadbalancedclusteringalgorithms . . . . . . . . . . . . . . . . . 49
4.4.1 Algorithmforequalload . . . . . . . . . . . . . . . . . . . 49
4.4.2 Anillustration . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.3 Approximationalgorithmsforunequalload . . . . . . . . . 54
4.4.4 Performancesbasedonloadbalancing . . . . . . . . . . . . 56
Contents ix
4.4.5 Executiontime . . . . . . . . . . . . . . . . . . . . . . . . 57
4.4.6 Heapbasedapproximationalgorithm . . . . . . . . . . . . 58
4.5 Chaptersummary . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 CentralizedAlgorithms 63
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.2 Loadbalancingalgorithms . . . . . . . . . . . . . . . . . . . . . . 63
5.3 Energyefficientalgorithm . . . . . . . . . . . . . . . . . . . . . . 67
5.4 ChapterSummary . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6 MetaheuristicApproaches 73
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.2 Systemmodelandterminologies . . . . . . . . . . . . . . . . . . . 75
6.3 Geneticalgorithmsforclustering . . . . . . . . . . . . . . . . . . 76
6.3.1 Anoverviewofgeneticalgorithm . . . . . . . . . . . . . . 76
6.3.2 GAbasedclustering . . . . . . . . . . . . . . . . . . . . . 77
6.3.2.1 Problemformulation. . . . . . . . . . . . . . . . 77
6.3.2.2 Chromosomerepresentation . . . . . . . . . . . . 78
6.3.2.3 Initialpopulation . . . . . . . . . . . . . . . . . 79
6.3.2.4 Fitnessfunction . . . . . . . . . . . . . . . . . . 80
6.3.2.5 Selection . . . . . . . . . . . . . . . . . . . . . . 80
6.3.2.6 Crossover . . . . . . . . . . . . . . . . . . . . . 81
6.3.2.7 Mutation . . . . . . . . . . . . . . . . . . . . . . 81
6.4 Differentialevolutionforclusteringandrouting . . . . . . . . . . . 84
6.4.1 Anoverviewofdifferentialevolution . . . . . . . . . . . . 84
6.4.2 DEbasedclustering . . . . . . . . . . . . . . . . . . . . . 86
6.4.2.1 Problemformulation. . . . . . . . . . . . . . . . 86
6.4.2.2 Initializationofthepopulationvector . . . . . . . 87
6.4.2.3 Derivationoffitnessfunction . . . . . . . . . . . 88
6.4.2.4 Mutation . . . . . . . . . . . . . . . . . . . . . . 91
6.4.2.5 Crossover . . . . . . . . . . . . . . . . . . . . . 92
6.4.2.6 Localimprovement . . . . . . . . . . . . . . . . 92
6.4.2.7 Selection . . . . . . . . . . . . . . . . . . . . . . 94
6.5 Particleswarmoptimizationbasedalgorithms . . . . . . . . . . . . 96
6.5.1 Anoverviewofparticleswarmoptimization . . . . . . . . . 97
6.5.2 PSObasedrouting . . . . . . . . . . . . . . . . . . . . . . 99
6.5.2.1 LPFormulationforRoutingProblem . . . . . . . 99
6.5.2.2 Routingalgorithm . . . . . . . . . . . . . . . . . 99
6.5.3 PSOBasedClustering . . . . . . . . . . . . . . . . . . . . 103
6.5.3.1 NLPFormulationforclusteringproblem: . . . . . 103
6.5.3.2 Clusteringalgorithm . . . . . . . . . . . . . . . . 104
6.6 Chaptersummary . . . . . . . . . . . . . . . . . . . . . . . . . . . 115