Performability in Internet of Things (eBook)

Fadi Al-Turjman (Herausgeber)

eBook Download: PDF

2018
238 Seiten
Springer International Publishing (Verlag)
978-3-319-93557-7 (ISBN)

Infos

This book discusses the challenges in the convergence of technologies as the Internet of Things (IoT) evolves. These include sensing, computing, information processing, networking, and controlling intelligent technologies. The contributors first provide a survey of various assessment  and evaluation approaches available for successful convergence. They then go on to cover several operational ideas to apply. The contributors then discuss the challenges involved bridging gaps in computation and the communication process, hidden networks, intelligent decision making, human-to-machine perception and large-scale IoT environments. The contributors aim to provide the reader an overview of trends in IoT in terms of performability and traffic modeling and efforts that can be spent in assessing the graceful degradation in IoT paradigms.

• Provides a survey of IoT assessment and evaluation approaches;
  
• Covers new and innovative operational ideas that apply to the IoT industry and the industries it affects;
  
• Includes chapters from researchers and industry leaders in IoT from around the world.
  

Prof. Dr. FADI AL-TURJMAN received his Ph.D. degree in computer science from Queen's University, Kingston, ON, Canada, in 2011. He is a leading authority in the areas of smart/cognitive, wireless and mobile networks architecture, protocols, deployments, and performance evaluation. His record spans more than 170 publications in journals, conferences, patents, books, and book chapters, in addition to numerous keynotes and plenary talks at flagship venues, including the IEEE ICC, LCN, and GLOBECOM conferences. He is a full professor at Antalya Bilim University, in Turkey. He has received several recognitions and best papers' awards at top international conferences, and led a number of international symposia and workshops in flagship ComSoc conferences. He is the sole author for 4 recently published books about cognition and wireless sensor networks' deployments in smart environments with Taylor and Francis, CRC New York (a top tier publisher in the area). He is serving as the Lead Guest Editor in several journals including the IET Wireless Sensor Systems (WSS), MDPI Sensors and Wiley. He is also the publication chair for the IEEE International Conf. on Local Computer Networks (LCN'18).

Inhalt

Preface 7
Contents 9
1 Performability Analysis Methods for Clustered WSNsas Enabling Technology for IoT 11
1.1 Introduction 11
1.2 Existing Methods for Performance Evaluation 13
1.2.1 Pure Performance Evaluation Models for Multiserver Systems 13
1.2.2 Availability Models for Multiserver Systems 15
1.2.3 Performability Models for Multiserver Systems 17
1.3 Performability Modelling of Wireless Sensor Networks 18
1.4 System Description and Modelling for Performability Evaluation 19
1.4.1 System Description 19
1.4.2 System Modelling 20
1.5 Performability Results and Discussions 24
1.6 Conclusion and Future Directions 27
References 27
2 Practical Performability Assessment for ZigBee-Based Sensors in the IoT Era 30
2.1 Introduction 30
2.2 Background 31
2.3 System Architecture 34
2.4 Results 36
2.5 Conclusion 38
References 40
3 Evaluation of Simulation Approaches and Need for MDEin Energy Efficiency, Performance and Availability Assessmentof IoT 41
3.1 Introduction 41
3.2 Related Works 44
3.2.1 Instruction-Level Simulators 45
3.2.2 Algorithm-Level Simulators 45
3.2.3 Packet-Level Simulators 46
3.3 Methodology: Performance Modelling of Wireless Sensor Networks 47
3.4 Results and Discussions 49
3.5 Conclusion and Future Directions 50
References 51
4 False Data Injection Attacks in Internet of Things 55
4.1 False Data Injection Attacks 55
4.2 Impact of False Data Injection Attacks 56
4.3 False Data Injection Attack in Internet of Things 56
4.3.1 Importance of False Data Injection Attack in Internet of Things Applications 57
4.3.2 Analysis of False Data Injection Attack Countermeasures 57
4.4 False Data Injection Attack Detection and Prevention Challenges in Internet of Things 59
4.4.1 System Security 60
4.4.2 Data Storage 60
4.4.3 Data Sanitization 60
4.4.4 Power Consumption 61
4.4.5 Tolerance Level 61
4.4.6 Access Control 62
4.4.7 Resilience to Attacks 62
4.5 Future Directions 62
4.5.1 False Data Injection Attack Modelling Using Deep Learning 63
4.6 Conclusion 63
References 64
5 Energy-Efficient Clustering for Wireless Sensor Devicesin Internet of Things 67
5.1 Introduction 67
5.2 State of the Art of Clustering for WSNs 68
5.2.1 Clustering Protocols for Homogeneous WSNs 68
5.2.2 Clustering Protocols for Heterogeneous WSNs 70
5.2.3 Clustering Protocols with Harvesting 71
5.2.4 Clustering Protocols with Machine Learning 73
5.3 REECHD Clustering Protocol 74
5.3.1 REECHD Leader Election Probability 75
5.3.2 REECHD Intra-Traffic Rate Limit 76
5.3.3 REECHD Algorithm 77
5.3.4 REECHD Cluster Head Election 77
5.3.4.1 Cluster Formation and Iteration 79
5.3.4.2 Cluster Rotation 80
5.4 Comparing the State-of-the-Art Clustering Protocols 80
5.4.1 Network Model 80
5.4.2 Simulation Results and Analysis 82
5.5 Conclusions 85
References 86
6 Toward Optimum Topology Protocol in Health Monitoring 89
6.1 Background 89
6.2 Motivation 92
6.3 Problem Statement 92
6.4 Objective of the Research 93
6.5 Scope of the Research 94
6.6 Approach Description 94
6.6.1 Energy Model 94
6.6.2 Analytical Model of Dense and Sparse Topology Sensor Networks 95
6.6.2.1 Dense Topology Sensor Network Model 95
6.6.2.2 Sparse Topology Sensor Network Model 98
6.7 Lifetime Evaluation of the Dense Topology Sensor Network 101
6.7.1 Experiment 1: Number of Active Nodes 102
6.7.2 Experiment 2: Number of Reachable Nodes from Sink 103
6.7.3 Experiment 3: Communication Covered Area 105
6.7.4 Experiment 4: Sensing Coverage Area 106
6.8 Lifetime Evaluation of the Sparse Topology Sensor Network 108
6.8.1 Experiment 1: Number of Active Nodes 108
6.8.2 Experiment 2: Number of Reachable Nodes from Sink 109
6.8.3 Experiment 3: Coverage Area for Communication 110
6.8.4 Experiment 4: Coverage Area for Sensing 112
6.9 Comparison Between Dense and Sparse Topology Sensor Networks 113
6.10 Discussion and Conclusion 115
References 116
7 Internet of Things (IoT) Considerations, Requirements,and Architectures for Disaster Management System 118
7.1 Introduction 118
7.2 Considerations and Requirements for Disaster Management System 119
7.3 Emerging Technologies for Disaster Management System 121
7.3.1 Device to Device 122
7.3.2 Internet of Things 124
7.4 Architecture for Disaster Management System 124
7.4.1 Mode-1 125
7.4.2 Mode-2 125
7.4.2.1 Shortest Path Routing Algorithm 126
7.4.2.2 Network Configuration 126
7.4.3 Energy Efficiency and Spectral Efficiency 127
7.4.4 Simulation Results 128
7.5 Conclusion 131
References 131
8 Internet of Things and Statistical Analysis 133
8.1 Introduction 133
8.2 Growth of IoT 134
8.3 Increasing Volumes of Large Data Sets, the Need for Online Analysis, and the Privacy Challenges 135
8.4 Data Science 135
8.4.1 Plenty of Techniques and Algorithms 136
8.4.2 Predictive Modeling 137
8.4.3 The Danger of Lack of Theoretical Knowledge 138
8.5 Statistical Modeling 138
8.6 Conclusions 141
References 142
9 Internet of Vehicle (IoV) Applications in Expediting the Implementation of Smart Highway of Autonomous Vehicle:A Survey 143
9.1 Introduction 143
9.1.1 Smart City 144
9.1.2 Driverless Vehicle 144
9.1.3 Outline and Contributions of the Paper 147
9.2 Internet of Vehicles 148
9.2.1 Internet of Vehicles Background 149
9.3 Internet of Vehicles in Expediting the Autonomous Vehicle Smart Highway 150
9.3.1 Improving Vehicle Connectivity 150
9.3.2 Enhanced Perception Modules 151
9.3.3 Data Transferring Between Platforms 152
9.3.4 Vehicle-to-X 154
9.3.5 Aiding Collision Avoidance Systems 154
9.3.6 Improved Localization 155
9.3.7 Blind Spot 156
9.3.8 Improving Path Planning and Motion Guidance During Nonlinear Vehicle Dynamics Scenario 157
9.3.9 Better Infotainment 157
9.3.10 Reducing Traffic Jam 158
9.4 Assimilation of Internet of Vehicles in a Collision Avoidance System for Autonomous Vehicle: A Case Study 158
9.4.1 Results and Discussions 159
9.5 Future Works 160
9.6 Conclusions 161
References 161
10 Virtual Coordinate Systems and Coordinate-Based Operations for IoT 164
10.1 Introduction 164
10.2 Physical Coordinates vs. Virtual Coordinates 165
10.3 Classification of Virtual Coordinate Systems 169
10.3.1 Virtual Coordinate Systems Embedding a Graph/Tree Topology 169
10.3.2 Virtual Coordinate Systems Based on Hop Distances to Anchors 170
10.3.3 Topological Coordinate Systems 170
10.3.4 Virtual Coordinate Systems using Network Measurement Parameters 170
10.4 Attributes of Virtual Coordinate System 171
10.4.1 Use of Anchors 171
10.4.2 Efficiency of Routing/Measurements 171
10.4.3 Susceptibility to Local Minima Issue 171
10.4.4 Ability to Deal with Node Failures and Changing Topologies 172
10.4.5 Ability to Capture the Network Shape and Voids 172
10.4.6 Applicability to 3-D Networks 172
10.4.7 Distributed Computability of VCs 172
10.4.8 Directionality 173
10.4.9 Applicability to Wireless Sensor Networks 173
10.5 Virtual Coordinate Systems 174
10.5.1 Virtual Coordinate Systems Based on an Embedded Graph/Tree Topology 174
10.5.1.1 Gradient Landmark-Based Virtual Coordinate System [37] 174
10.5.1.2 Medial Axis Protocol for Virtual Coordinate System [38] 176
10.5.1.3 Graph Embedding for Virtual Coordinate System [39] 179
10.5.1.4 Hyperbolic Embedding in Dynamic Graphs for Virtual Coordinate System [40] 182
10.5.2 Virtual Coordinate Systems Based on Hop Distances to Anchors 185
10.5.2.1 Anchor-Based Virtual Coordinates 186
10.5.2.2 Axis-Based Virtual Coordinate Assignment Protocol [41] 190
10.5.2.3 Directional Virtual Coordinate Systems [23] 193
10.5.3 Topology Preserving Maps 197
10.5.3.1 Topology Preserving Maps: Extracting Layout Maps of Wireless Sensor Networks from Virtual Coordinates [35] 197
10.5.4 Coordinate Systems Using Network Properties 201
10.5.4.1 Vivaldi (Network Coordinate System) [42] 201
10.5.4.2 Maximum Likelihood Topology Maps for Wireless Sensor Networks Using an Automated Robot [25] 205
10.6 Conclusion 209
References 209
11 Small Data in IoT: An MCS Perspective 213
11.1 Introduction 213
11.1.1 Small Data 214
11.2 Literature Review and Related Work 216
11.3 Mobile Crowdsensing Model and Preliminary Mathematics 219
11.3.1 Robust Statistics 221
11.3.1.1 MAD-Mean: Median Absolute Deviation Filtered Mean 221
11.3.2 Nonparametric Bootstrap 224
11.3.3 The Bootlier 225
11.4 MMTM: A Quality Metric for Small Data 225
11.5 Potential Applications and Directions for Mobile Crowdsensing 228
11.6 Overview and a Closing Word 229
References 231
Index 234

Produkt-Details

• Erscheint lt. Verlag: 22.8.2018
• Reihe/Serie: EAI/Springer Innovations in Communication and Computing
• Reihe/Serie: EAI/Springer Innovations in Communication and Computing
• Zusatzinfo: X, 238 p. 87 illus., 62 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Maschinenbau
• Schlagworte: cellular networks • internet of things • IoT applications • IoT assessment • IoT convergence • IoT evaluation • IoT Performance • mobile applications • wireless sensor networks
• ISBN-10: 3-319-93557-7 / 3319935577
• ISBN-13: 978-3-319-93557-7 / 9783319935577