Mission-Oriented Sensor Networks and Systems: Art and Science (eBook)

Foreword 10
Contents 13
About the Editor 16
Introduction 20
1 Mission-Oriented Sensor Networks and Systems: Art and Science 20
2 Book Organization 22
3 Acknowledgments 24
Architecture and Experimentation 28
Design Considerations of Mission-Oriented Sensor Node Architectures 29
1 Challenges for Sensor Nodes 30
1.1 Outline 31
2 General Node Architecture 31
2.1 Components of a Sensor Node 31
2.2 General Example: TMote Sky/TelosB Sensor Node 34
3 Exemplary Mission: Human Activity Monitoring 36
3.1 Motivation and Requirements Analysis 36
3.2 Market Analysis 37
4 Mission Oriented Sensor Node: INGA 39
4.1 Design Decisions 39
4.2 Evaluation 45
4.3 Limitations and Future Work 50
5 Exemplary Mission: Smart Farming 50
5.1 Motivation and Requirements Analysis 51
5.2 Exemplary Smart Farming Scenario 52
6 Mission Oriented Sensor Node: Amphisbaena 55
6.1 Collaborative Data Collection 56
6.2 Design Considerations 56
6.3 Evaluation 58
7 Summary 61
References 62
Failure Handling in RPL Implementations: An Experimental Qualitative Study 66
1 Introduction 66
2 Overview of RPL 67
2.1 Preliminary Information 68
2.2 Packet Forwarding 69
2.3 Parent Selection and Rank Computation 70
2.4 Control Traffic 71
2.5 Open Issues 73
2.6 RPL Implementations 73
3 Related Work 73
4 Experimental Methodology 74
4.1 Experimental Environments 75
4.2 Experimental Settings 75
4.3 Experimental Scenarios 76
5 Experiments Without Failures 77
5.1 Summary 83
6 Experiments with Link Failures 84
6.1 Important Link Failure 84
6.2 Unimportant Link Failure 88
6.3 Summary 89
7 Experiments with Node Failures 90
7.1 Important Node Failure 90
7.2 Node Failure and Recovery 91
7.3 Correlated Node Failures 94
7.4 Summary 98
8 Experiments with Network Partitions 98
8.1 Root Failure 99
8.2 Root Failure and Recovery 105
8.3 Summary 109
9 Conclusions 109
References 110
Deployment and Coverage 113
On the Optimization of WSN Deployment for Sensing Physical Phenomena: Applications to Urban Air Pollution Monitoring 114
1 Introduction 114
2 WSN Deployment 116
2.1 Event-Aware Deployment Methods 116
2.2 Correlation-Aware Deployment Methods 117
2.3 Discussion 119
3 Air Pollution Prediction 119
3.1 Atmospheric Dispersion-Based Methods 119
3.2 Interpolation-Based Prediction Methods 121
3.3 Regression-Based Prediction Methods 121
4 Model 1: WSN Deployment for Air Pollution Mapping Based on Predicted Data 121
4.1 Problem Formulation 122
4.2 Simulation Results 128
5 Model 2: WSN Deployment for Air Pollution Detection Based on Predicted Data 134
5.1 Problem Formulation 134
5.2 Simulation Results 137
6 Model 3: WSN Deployment for Air Pollution Detection Based on Emission Inventory 141
6.1 Problem Formulation 142
6.2 Simulation Results 147
7 Discussion and Future Directions 156
8 Conclusion 157
References 158
Mobile Coverage 161
1 Area Coverage Using Mobility 161
1.1 Area Coverage Problem by Mobile Sensor Deployment Using Potential Fields 161
1.2 Constrained Coverage by Mobile Sensors 166
1.3 Cooperative Dynamic Coverage by Static Sensors and Mobile Sensors 166
2 Barrier Coverage Using Mobility 169
2.1 Barrier Coverage to Detect Mobile Objects 170
2.2 Strong k-Barrier Coverage Using Mobility 171
2.3 Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain 174
2.4 Minimizing the Maximum Movement by Mobile Sensors for Barrier Coverage in the Plane 174
2.5 Distributed Coordination of Mobile Sensors for Barrier Coverage 175
2.6 Event-Driven Partial Barrier Coverage 176
2.7 Resilient Event-Driven Partial Barrier Coverage Using Mobile Sensors 181
2.8 Barrier Coverage of Variable Bounded-Range Line-of-Sight Guards 187
2.9 Barrier Coverage by Mobile Sensors of Polygon Region 188
3 Sweep Coverage Using Mobility 189
3.1 Sweep Coverage in Sensor Networks 189
3.2 Area Sweep Coverage 193
3.3 Energy Efficient Sweep Coverage 194
3.4 Sweep Coverage Problem with the Shorten Trajectory of Mobile Sensors 197
3.5 Target Coverage and Network Connectivity Using Mobile Sensors 199
References 202
Task Allocation and Mission Assignment 205
Energy-Aware Task Allocation in WSNs 206
1 Introduction 207
1.1 Main Objectives and Challenges of Task Allocation in WSNs 208
2 The Evaluation Metrics of Task Allocation Approaches 209
3 Modeling Application Tasks, Networks and Cost Functions of Sensor Nodes 210
3.1 Application Tasks Model 210
3.2 Network Model 211
3.3 Cost Functions 212
4 Classification of Task Allocation Approaches in WSNs 214
5 Optimal Task Allocation Algorithms 215
5.1 Sensing Tasks Allocation 215
5.2 General Tasks Allocation 219
5.3 Online Distributed Task Allocation 223
6 Heuristic Task Allocation Algorithms 228
6.1 Traditional Heuristic Algorithms 228
6.2 Bio-inspired Heuristic Algorithms 232
7 Conclusion 237
References 238
Sensor Assignment to Missions: A Natural Language Knowledge-Based Approach 240
1 Introduction 240
2 Knowledge-Based Matching of ISR Assets to Tasks 244
2.1 NIIRS-Based Task-Asset Matching Knowledge 248
2.2 Matching Procedure 249
2.3 KB Table Generation and Asset Assignment 249
3 The SAM Matching Algorithm 250
3.1 Sample Queries 252
3.2 Code Walkthrough 252
4 Reformulating the Knowledge Base in Controlled English 254
4.1 A Brief Introduction to CE 255
4.2 Representing the Core MMF Ontology in CE 256
4.3 Representing Task-Asset Matching Knowledge in CE 257
4.4 Assignment Representation in CE 259
5 Task-Asset Assignment and Sharing Using a Tablet-Based App 262
5.1 Prototype Conversational Agents 266
6 Conclusion 267
References 275
Resource Allocation and Task Scheduling in the Cloud of Sensors 277
1 Introduction 278
2 Background Concepts 281
2.1 The CoS Architecture 281
2.2 CoS Virtualization 284
2.3 Task Scheduling 287
2.4 Resource Allocation 289
3 State of the Art 291
3.1 Classification Criteria 292
3.2 State of the Art on Task Scheduling at the Sensors Tier 295
3.3 State of the Art on Resource Allocation at Edge and Cloud Tiers 303
3.4 Open Issues 305
4 Resource Allocation and Task Scheduling in Olympus 309
5 Final Remarks 312
References 313
Detection, Localization, and Tracking 318
Target Detection, Localization, and Tracking in Wireless Sensor Networks 319
1 Target Detection in Wireless Sensor Networks 319
1.1 Coherent and Noncoherent WSN Detection Systems 320
1.2 Distributed-RSN and MIMO-RSN in Fading Channels 326
1.3 Nodes Deployment, Clustering Techniques, and Information Fusion 331
2 Node Localization 341
2.1 Range-Based Algorithms 342
2.2 Range-Free Algorithms 344
2.3 Range-Free Localization for Mobile Networks 350
3 Target Tracking in Wireless Sensor Networks 355
3.1 Target Tracking Protocols 355
3.2 Point Track Fusion 361
4 Conclusion 367
References 368
Regularization-Based Location Fingerprinting 372
1 Introduction 372
2 Preliminaries 376
3 Enrichment of Training Data 380
3.1 Manifold Regularization 381
3.2 Total Variation Regularization 383
3.3 Manifold Versus Total Variation Regularization 385
4 Trajectory Computation 389
5 Online Algorithm 395
5.1 Sparse Representation 395
5.2 Representative Buffer 400
5.3 Location Estimation 402
6 Conclusions 407
References 408
Sense-Through-Foliage Target Detection Based on UWB Radar Sensor Networks 410
1 Introduction and Motivation 411
2 Sense-Through-Foliage Data Measurement and Collection 414
3 Sense-Through-Foliage Target Detection with Good Signal Quality: A DCT-Based Approach 417
4 Waveform Design and Diversity in Radar Sensor Networks 425
4.1 Coexistence of Radar Waveforms 425
4.2 Interferences of Waveforms in Radar Sensor Networks 427
4.3 Radar Sensor Network for Collaborative Automatic Target Recognition 429
5 Sense-Through-Foliage Target Detection with Poor Signal Quality: A Sensor Network and DCT-Based Approach 431
6 Human-Inspired Sense-Through-Foliage Target Detection 434
6.1 Human Information Integration Mechanisms 434
6.2 Human-Inspired Sense-Through-Foliage Target Detection 436
7 Fuzzy Logic System for Automatic Target Detection 436
7.1 Overview of Fuzzy Logic Systems 436
7.2 FLS for Automatic Target Detection 439
8 Conclusions and Future Works 441
References 441
Mobile Target Tracking with Multiple Objectives in Wireless Sensor Networks 445
1 Introduction 446
1.1 Motivation 446
1.2 Our Scheme: t-Tracking 449
1.3 Distinctive Advantages 450
1.4 Contributions 451
1.5 Organization 451
2 Related Work 451
3 Problem Setup and Objectives 454
3.1 Preliminaries 454
3.2 Exploration of Faces 454
3.3 Models 457
3.4 Objectives 458
4 Tracking Algorithms 460
4.1 Rules for Node Organization into Faces for Tracking 460
4.2 Target Detection and Target Moving Face Detection 462
4.3 Computing Target Moving Sequence Through Faces 464
4.4 Face Prediction 466
5 Tracking Process and Robustness 469
5.1 The Tracking Process 469
5.2 Robustness to the Special Events During Tracking 471
6 Design of t-Tracking 474
6.1 Key Design Elements 474
6.2 Sensor State Transition Techniques and Energy Saving in the WSN 475
6.3 Energy Saving During Target Tracking 477
7 Performance Analysis 479
7.1 Cost of Fault Tolerance in Detection and Tracking 479
7.2 Wakeup Delay 481
7.3 Sensing Task and Complexity 482
7.4 Relative Distance 482
8 Simulation Studies 483
8.1 Methods and Parameters 483
8.2 Key Simulation Results 484
9 Proof-of-Concept System Implementation 487
9.1 System Setup and Parameters 487
9.2 t's Moving Traces 489
9.3 Experimental Results 490
10 Conclusion and Future Work 493
References 499
Data Dissemination and Fusion 504
Data Dissemination and Remote Control in Wireless Sensor Networks 505
1 Introduction 505
2 Requirements and Challenges 508
2.1 Features of WSNs 508
2.2 Requirements of Data Dissemination 509
2.3 Challenges of Data Dissemination 510
3 Structure-Less Data Dissemination Schemes 511
3.1 Non-negotiation Schemes 511
3.2 Negotiation-Based Schemes 514
3.3 Hybrid Schemes 519
4 Structure-Based Data Dissemination Schemes 520
4.1 Plain-Structure Schemes 521
4.2 Hierarchical-Structure Schemes 523
5 Other Techniques Used in Data Dissemination 525
5.1 Segmentation and Pipelining 525
5.2 Coding Technique 526
5.3 Constructive Interference 528
6 Performance Evaluation 529
6.1 Performance Metrics 530
6.2 Performance Comparisons 531
7 Open Issues 532
8 Conclusion 533
References 534
A Data Fusion Algorithm for Multiple Applications in Wireless Sensor Networks 538
1 Introduction 538
2 Basic Concepts 541
2.1 Information Fusion for Wireless Sensor Networks 541
2.2 Kurtosis and Skewness Concepts 543
3 Related Work 546
4 Hephaestus 551
4.1 Local Information Fusion Procedure (LIFH) 552
4.2 Complementary Information Fusion Procedure (CIFH) 556
5 Evaluation 559
5.1 Environment Configuration and Application Scenario 560
5.2 Metrics 561
5.3 Energy Model 562
5.4 Evaluating Hephaestus Overhead 563
5.5 Evaluating Hephaestus Accuracy 568
5.6 Analysis of Results 570
6 Final Remarks 570
References 571
Topology Control and Routing 574
Underwater Networks for Ocean Monitoring: A New Challenge for Topology Control and Opportunistic Routing 575
1 Introduction 576
2 The Fundamental Role of Underwater Sensor Networks 577
3 Characteristics of Underwater Sensor Networks 578
3.1 Network Architecture 579
3.2 High Cost 580
3.3 Involuntary Mobility 581
3.4 Underwater Acoustic Channel 581
4 The Benefits of Topology Control in Underwater Sensor Networks 584
4.1 Power Control-Based Topology Control 585
4.2 Wireless Interface Management-Based Topology Control 587
4.3 Mobility-Assisted-Based Topology Control 590
5 Geographic and Opportunistic Routing in Underwater Sensor Networks 591
5.1 Void-Handling Procedure 592
5.2 Candidate Set Selection Procedure 595
5.3 Candidate Coordination Procedure 598
6 Future Research Directions 600
7 Concluding Remarks 601
References 602
Geometric Routing Without Coordinates but Measurements 606
1 Introduction—Routing in Wireless Ad Hoc Networks 606
1.1 Local and Stateless Routing 608
1.2 Geometric Routing 609
2 Geometric Routing on Virtual Raw Anchor Coordinates 611
2.1 Why Another Coordinate System? 611
3 Virtual Raw Anchor Coordinate System 612
4 Graph Planarization on Virtual Raw Anchor Coordinate System 613
5 Combined Greedy–Face Routing with Delivery Guarantees 616
5.1 Greedy Routing Primitives 616
5.2 Face Routing Primitives: Combinatorial Approach 617
5.3 Face Routing Primitives : Geometric Approach 625
5.4 Numerical Validation 629
6 Greedy Routing over Virtual Raw Anchor Coordinates 630
6.1 Schnyder Characterization and Saturated Graph 631
6.2 Characterization of Greedy Paths 632
6.3 Routing in Maximal Planar Graph 635
References 636
Delay-Tolerant Mobile Sensor Networks: Routing Challenges and Solutions 638
1 Introduction 638
2 General (Terrestrial) Delay-Tolerant Mobile Sensor Networks 642
2.1 Replication-Based Routing 644
2.2 Utility or Single-Copy-Based Routing 645
2.3 Erasure-Coding-Based Routing 647
2.4 Social-Based Routing 650
3 Underwater Delay-tolerant Mobile Sensor Networks 652
3.1 Geographical Routing 653
3.2 Mobile Relays (AUVs)-Based Routing 654
3.3 Clustering-Based Routing 654
3.4 Opportunistic and Prediction-Based Routing 655
4 Flying Delay-Tolerant Mobile Sensor Networks 656
4.1 Routing Table-Based Routing 658
4.2 Hierarchical/Clustering-Based Routing 659
4.3 Geographical Routing 660
5 Performance Evaluation Metrics 661
5.1 Average Delivery Ratio 661
5.2 Average End-to-End Delivery Delay 662
5.3 Average Delivery Cost or Messaging Overhead 662
5.4 Routing Efficiency 663
5.5 Network Lifetime 663
6 Conclusion 664
References 665
Privacy and Security 670
Location Privacy in Wireless Sensor Networks 671
1 Introduction 672
2 Anonymity Definition and Categorization 674
3 Source Location Privacy in WSNs 676
3.1 Attack Models for Source Location Privacy 676
3.2 Privacy-Preserving Measures for Source Location 678
4 Sink Location Privacy in WSN 682
4.1 BS Location Privacy Attack Models 683
4.2 Base Station Location Privacy-Preserving Measures 688
5 Sink Location Privacy Attack Models: Strengths and Weaknesses 694
5.1 Traffic Volume—Discussion and Critique 694
5.2 GSAT Test—Discussion and Critique 698
5.3 Evidence Theory—Discussion and Critique 698
6 A Novel Traffic Analysis Attack Model and Base Station Anonymity Metrics 702
6.1 EARS’s Hot Spot Cells Identification Phase 703
6.2 EARS’s Sink Cell Identification Phase 706
6.3 EARS Complexity Analysis and Anonymity Metrics 708
6.4 EARS Evaluation 709
7 Conclusion 712
References 713
Implementation of Secure Communications for Tactical Wireless Sensor Networks 717
1 Introduction 717
1.1 Low Power Wireless Sensor Networks 718
1.2 Introduction to 6LoWPAN/IEEE 802.15.4 719
1.3 Chapter Motivations 719
1.4 Chapter Objectives and Outline 721
2 Related Works 721
2.1 Architecture of a Tactical WSN 722
2.2 Routing 722
2.3 6LoWPAN Frame Structure 723
2.4 Security Mechanisms 723
3 Theoretical Framework for Security Architecture 726
3.1 Network Design 726
3.2 Encryption 735
3.3 6LoWPAN Enabled IEEE 802.15.4 Frame Structure 736
3.4 Deployment of Nodes 739
3.5 Proposed Attacks 740
4 Experimental Setup 741
4.1 Sensor Parameters 741
4.2 Node Characteristics 742
4.3 Frame Parameters 744
4.4 Network Parameters 745
5 Simulation Results and Analysis 746
5.1 Spoofing Results 747
5.2 DOS Results 749
5.3 MITM Results 754
6 Conclusion 756
References 757
Data-Driven Detection of Sensor-Hijacking Attacks on Electrocardiogram Sensors 758
1 Introduction 759
1.1 Sensor-Hijacking Attacks on Wearable Medical IoT Systems 760
1.2 Challenges in Detecting Sensor Hijacking 761
1.3 ECG Sensor Hijack Detection 763
1.4 Chapter Organization 765
2 Related Work 766
3 System Model, Threat Model, and Problem Statement 767
4 Background 768
5 Detecting Temporal ECG Alterations 770
6 Evaluation of ECG Temporal Alteration Detector 773
7 Discussion 777
8 Conclusions 779
8.1 Future Work 779
References 780
Cryptography in WSNs 783
1 Introduction 783
2 Symmetric Key Cryptography 786
2.1 Block Ciphers 787
2.2 Stream Ciphers 794
2.3 Key Exchange 796
3 Asymmetric Key Cryptography 798
3.1 RSA 799
3.2 Hash Functions 801
3.3 HMAC 803
3.4 Digital Signatures 804
3.5 Digital Certificates 805
4 Applications of Cryptography 806
4.1 Privacy 806
4.2 Key Wrapping by Asymmetric Key Cryptography 807
4.3 User Authentication 807
5 Cryptography in WSNs 809
5.1 Security Threats 810
5.2 Symmetric Versus Asymmetric Key Cryptography 810
5.3 ECC 811
5.4 Key Management 813
5.5 Identity-Based Cryptography 814
6 Conclusions and Open Issues 815
References 816