Ammari Mission-Oriented Sensor Networks and Systems: Art and Science

Volume 1: Foundations
1. Auflage 2019
ISBN: 978-3-319-91146-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 163, 820 Seiten

Reihe: Studies in Systems, Decision and Control

This book discusses topics in mission-oriented sensor networks and systems research and practice, enabling readers to understand the major technical and application challenges of these networks, with respect to their architectures, protocols, algorithms, and application design. It also presents novel theoretical and practical ideas, which have led to the development of solid foundations for the design, analysis, and implementation of energy-efficient, reliable, and secure mission-oriented sensor network applications. Covering various topics, including sensor node architecture, sensor deployment, mobile coverage, mission assignment, detection, localization, tracking, data dissemination, data fusion, topology control, geometric routing, location privacy, secure communication, and cryptograph, it is a valuable resource for computer scientists, researchers, and practitioners in academia and industry.

Autoren/Hrsg.

Ammari, Habib M.

Inhaltsverzeichnis

1;Foreword;10
2;Contents;13
3;About the Editor;16
4;Introduction;20
4.1;1 Mission-Oriented Sensor Networks and Systems: Art and Science;20
4.2;2 Book Organization;22
4.3;3 Acknowledgments;24
5;Architecture and Experimentation;28
6;Design Considerations of Mission-Oriented Sensor Node Architectures;29
6.1;1 Challenges for Sensor Nodes;30
6.1.1;1.1 Outline;31
6.2;2 General Node Architecture;31
6.2.1;2.1 Components of a Sensor Node;31
6.2.2;2.2 General Example: TMote Sky/TelosB Sensor Node;34
6.3;3 Exemplary Mission: Human Activity Monitoring;36
6.3.1;3.1 Motivation and Requirements Analysis;36
6.3.2;3.2 Market Analysis;37
6.4;4 Mission Oriented Sensor Node: INGA;39
6.4.1;4.1 Design Decisions;39
6.4.2;4.2 Evaluation;45
6.4.3;4.3 Limitations and Future Work;50
6.5;5 Exemplary Mission: Smart Farming;50
6.5.1;5.1 Motivation and Requirements Analysis;51
6.5.2;5.2 Exemplary Smart Farming Scenario;52
6.6;6 Mission Oriented Sensor Node: Amphisbaena;55
6.6.1;6.1 Collaborative Data Collection;56
6.6.2;6.2 Design Considerations;56
6.6.3;6.3 Evaluation;58
6.7;7 Summary;61
6.8;References;62
7;Failure Handling in RPL Implementations: An Experimental Qualitative Study;66
7.1;1 Introduction;66
7.2;2 Overview of RPL;67
7.2.1;2.1 Preliminary Information;68
7.2.2;2.2 Packet Forwarding;69
7.2.3;2.3 Parent Selection and Rank Computation;70
7.2.4;2.4 Control Traffic;71
7.2.5;2.5 Open Issues;73
7.2.6;2.6 RPL Implementations;73
7.3;3 Related Work;73
7.4;4 Experimental Methodology;74
7.4.1;4.1 Experimental Environments;75
7.4.2;4.2 Experimental Settings;75
7.4.3;4.3 Experimental Scenarios;76
7.5;5 Experiments Without Failures;77
7.5.1;5.1 Summary;83
7.6;6 Experiments with Link Failures;84
7.6.1;6.1 Important Link Failure;84
7.6.2;6.2 Unimportant Link Failure;88
7.6.3;6.3 Summary;89
7.7;7 Experiments with Node Failures;90
7.7.1;7.1 Important Node Failure;90
7.7.2;7.2 Node Failure and Recovery;91
7.7.3;7.3 Correlated Node Failures;94
7.7.4;7.4 Summary;98
7.8;8 Experiments with Network Partitions;98
7.8.1;8.1 Root Failure;99
7.8.2;8.2 Root Failure and Recovery;105
7.8.3;8.3 Summary;109
7.9;9 Conclusions;109
7.10;References;110
8;Deployment and Coverage;113
9;On the Optimization of WSN Deployment for Sensing Physical Phenomena: Applications to Urban Air Pollution Monitoring;114
9.1;1 Introduction;114
9.2;2 WSN Deployment;116
9.2.1;2.1 Event-Aware Deployment Methods;116
9.2.2;2.2 Correlation-Aware Deployment Methods;117
9.2.3;2.3 Discussion;119
9.3;3 Air Pollution Prediction;119
9.3.1;3.1 Atmospheric Dispersion-Based Methods;119
9.3.2;3.2 Interpolation-Based Prediction Methods;121
9.3.3;3.3 Regression-Based Prediction Methods;121
9.4;4 Model 1: WSN Deployment for Air Pollution Mapping Based on Predicted Data;121
9.4.1;4.1 Problem Formulation;122
9.4.2;4.2 Simulation Results;128
9.5;5 Model 2: WSN Deployment for Air Pollution Detection Based on Predicted Data;134
9.5.1;5.1 Problem Formulation;134
9.5.2;5.2 Simulation Results;137
9.6;6 Model 3: WSN Deployment for Air Pollution Detection Based on Emission Inventory;141
9.6.1;6.1 Problem Formulation;142
9.6.2;6.2 Simulation Results;147
9.7;7 Discussion and Future Directions;156
9.8;8 Conclusion;157
9.9;References;158
10;Mobile Coverage;161
10.1;1 Area Coverage Using Mobility;161
10.1.1;1.1 Area Coverage Problem by Mobile Sensor Deployment Using Potential Fields;161
10.1.2;1.2 Constrained Coverage by Mobile Sensors;166
10.1.3;1.3 Cooperative Dynamic Coverage by Static Sensors and Mobile Sensors;166
10.2;2 Barrier Coverage Using Mobility;169
10.2.1;2.1 Barrier Coverage to Detect Mobile Objects;170
10.2.2;2.2 Strong k-Barrier Coverage Using Mobility;171
10.2.3;2.3 Minimizing the Maximum Sensor Movement for Barrier Coverage of a Linear Domain;174
10.2.4;2.4 Minimizing the Maximum Movement by Mobile Sensors for Barrier Coverage in the Plane;174
10.2.5;2.5 Distributed Coordination of Mobile Sensors for Barrier Coverage;175
10.2.6;2.6 Event-Driven Partial Barrier Coverage;176
10.2.7;2.7 Resilient Event-Driven Partial Barrier Coverage Using Mobile Sensors;181
10.2.8;2.8 Barrier Coverage of Variable Bounded-Range Line-of-Sight Guards;187
10.2.9;2.9 Barrier Coverage by Mobile Sensors of Polygon Region;188
10.3;3 Sweep Coverage Using Mobility;189
10.3.1;3.1 Sweep Coverage in Sensor Networks;189
10.3.2;3.2 Area Sweep Coverage;193
10.3.3;3.3 Energy Efficient Sweep Coverage;194
10.3.4;3.4 Sweep Coverage Problem with the Shorten Trajectory of Mobile Sensors;197
10.3.5;3.5 Target Coverage and Network Connectivity Using Mobile Sensors;199
10.4;References;202
11;Task Allocation and Mission Assignment;205
12;Energy-Aware Task Allocation in WSNs;206
12.1;1 Introduction;207
12.1.1;1.1 Main Objectives and Challenges of Task Allocation in WSNs;208
12.2;2 The Evaluation Metrics of Task Allocation Approaches;209
12.3;3 Modeling Application Tasks, Networks and Cost Functions of Sensor Nodes;210
12.3.1;3.1 Application Tasks Model;210
12.3.2;3.2 Network Model;211
12.3.3;3.3 Cost Functions;212
12.4;4 Classification of Task Allocation Approaches in WSNs;214
12.5;5 Optimal Task Allocation Algorithms;215
12.5.1;5.1 Sensing Tasks Allocation;215
12.5.2;5.2 General Tasks Allocation;219
12.5.3;5.3 Online Distributed Task Allocation;223
12.6;6 Heuristic Task Allocation Algorithms;228
12.6.1;6.1 Traditional Heuristic Algorithms;228
12.6.2;6.2 Bio-inspired Heuristic Algorithms;232
12.7;7 Conclusion;237
12.8;References;238
13;Sensor Assignment to Missions: A Natural Language Knowledge-Based Approach;240
13.1;1 Introduction;240
13.2;2 Knowledge-Based Matching of ISR Assets to Tasks;244
13.2.1;2.1 NIIRS-Based Task-Asset Matching Knowledge;248
13.2.2;2.2 Matching Procedure;249
13.2.3;2.3 KB Table Generation and Asset Assignment;249
13.3;3 The SAM Matching Algorithm;250
13.3.1;3.1 Sample Queries;252
13.3.2;3.2 Code Walkthrough;252
13.4;4 Reformulating the Knowledge Base in Controlled English;254
13.4.1;4.1 A Brief Introduction to CE;255
13.4.2;4.2 Representing the Core MMF Ontology in CE;256
13.4.3;4.3 Representing Task-Asset Matching Knowledge in CE;257
13.4.4;4.4 Assignment Representation in CE;259
13.5;5 Task-Asset Assignment and Sharing Using a Tablet-Based App;262
13.5.1;5.1 Prototype Conversational Agents;266
13.6;6 Conclusion;267
13.7;References;275
14;Resource Allocation and Task Scheduling in the Cloud of Sensors;277
14.1;1 Introduction;278
14.2;2 Background Concepts;281
14.2.1;2.1 The CoS Architecture;281
14.2.2;2.2 CoS Virtualization;284
14.2.3;2.3 Task Scheduling;287
14.2.4;2.4 Resource Allocation;289
14.3;3 State of the Art;291
14.3.1;3.1 Classification Criteria;292
14.3.2;3.2 State of the Art on Task Scheduling at the Sensors Tier;295
14.3.3;3.3 State of the Art on Resource Allocation at Edge and Cloud Tiers;303
14.3.4;3.4 Open Issues;305
14.4;4 Resource Allocation and Task Scheduling in Olympus;309
14.5;5 Final Remarks;312
14.6;References;313
15;Detection, Localization, and Tracking;318
16;Target Detection, Localization, and Tracking in Wireless Sensor Networks;319
16.1;1 Target Detection in Wireless Sensor Networks;319
16.1.1;1.1 Coherent and Noncoherent WSN Detection Systems;320
16.1.2;1.2 Distributed-RSN and MIMO-RSN in Fading Channels;326
16.1.3;1.3 Nodes Deployment, Clustering Techniques, and Information Fusion;331
16.2;2 Node Localization;341
16.2.1;2.1 Range-Based Algorithms;342
16.2.2;2.2 Range-Free Algorithms;344
16.2.3;2.3 Range-Free Localization for Mobile Networks;350
16.3;3 Target Tracking in Wireless Sensor Networks;355
16.3.1;3.1 Target Tracking Protocols;355
16.3.2;3.2 Point Track Fusion;361
16.4;4 Conclusion;367
16.5;References;368
17;Regularization-Based Location Fingerprinting;372
17.1;1 Introduction;372
17.2;2 Preliminaries;376
17.3;3 Enrichment of Training Data;380
17.3.1;3.1 Manifold Regularization;381
17.3.2;3.2 Total Variation Regularization;383
17.3.3;3.3 Manifold Versus Total Variation Regularization;385
17.4;4 Trajectory Computation;389
17.5;5 Online Algorithm;395
17.5.1;5.1 Sparse Representation;395
17.5.2;5.2 Representative Buffer;400
17.5.3;5.3 Location Estimation;402
17.6;6 Conclusions;407
17.7;References;408
18;Sense-Through-Foliage Target Detection Based on UWB Radar Sensor Networks;410
18.1;1 Introduction and Motivation;411
18.2;2 Sense-Through-Foliage Data Measurement and Collection;414
18.3;3 Sense-Through-Foliage Target Detection with Good Signal Quality: A DCT-Based Approach;417
18.4;4 Waveform Design and Diversity in Radar Sensor Networks;425
18.4.1;4.1 Coexistence of Radar Waveforms;425
18.4.2;4.2 Interferences of Waveforms in Radar Sensor Networks;427
18.4.3;4.3 Radar Sensor Network for Collaborative Automatic Target Recognition;429
18.5;5 Sense-Through-Foliage Target Detection with Poor Signal Quality: A Sensor Network and DCT-Based Approach;431
18.6;6 Human-Inspired Sense-Through-Foliage Target Detection;434
18.6.1;6.1 Human Information Integration Mechanisms;434
18.6.2;6.2 Human-Inspired Sense-Through-Foliage Target Detection;436
18.7;7 Fuzzy Logic System for Automatic Target Detection;436
18.7.1;7.1 Overview of Fuzzy Logic Systems;436
18.7.2;7.2 FLS for Automatic Target Detection;439
18.8;8 Conclusions and Future Works;441
18.9;References;441
19;Mobile Target Tracking with Multiple Objectives in Wireless Sensor Networks;445
19.1;1 Introduction;446
19.1.1;1.1 Motivation;446
19.1.2;1.2 Our Scheme: t-Tracking;449
19.1.3;1.3 Distinctive Advantages;450
19.1.4;1.4 Contributions;451
19.1.5;1.5 Organization;451
19.2;2 Related Work;451
19.3;3 Problem Setup and Objectives;454
19.3.1;3.1 Preliminaries;454
19.3.2;3.2 Exploration of Faces;454
19.3.3;3.3 Models;457
19.3.4;3.4 Objectives;458
19.4;4 Tracking Algorithms;460
19.4.1;4.1 Rules for Node Organization into Faces for Tracking;460
19.4.2;4.2 Target Detection and Target Moving Face Detection;462
19.4.3;4.3 Computing Target Moving Sequence Through Faces;464
19.4.4;4.4 Face Prediction;466
19.5;5 Tracking Process and Robustness;469
19.5.1;5.1 The Tracking Process;469
19.5.2;5.2 Robustness to the Special Events During Tracking;471
19.6;6 Design of t-Tracking;474
19.6.1;6.1 Key Design Elements;474
19.6.2;6.2 Sensor State Transition Techniques and Energy Saving in the WSN;475
19.6.3;6.3 Energy Saving During Target Tracking;477
19.7;7 Performance Analysis;479
19.7.1;7.1 Cost of Fault Tolerance in Detection and Tracking;479
19.7.2;7.2 Wakeup Delay;481
19.7.3;7.3 Sensing Task and Complexity;482
19.7.4;7.4 Relative Distance;482
19.8;8 Simulation Studies;483
19.8.1;8.1 Methods and Parameters;483
19.8.2;8.2 Key Simulation Results;484
19.9;9 Proof-of-Concept System Implementation;487
19.9.1;9.1 System Setup and Parameters;487
19.9.2;9.2 t's Moving Traces;489
19.9.3;9.3 Experimental Results;490
19.10;10 Conclusion and Future Work;493
19.11;References;499
20;Data Dissemination and Fusion;504
21;Data Dissemination and Remote Control in Wireless Sensor Networks;505
21.1;1 Introduction;505
21.2;2 Requirements and Challenges;508
21.2.1;2.1 Features of WSNs;508
21.2.2;2.2 Requirements of Data Dissemination;509
21.2.3;2.3 Challenges of Data Dissemination;510
21.3;3 Structure-Less Data Dissemination Schemes;511
21.3.1;3.1 Non-negotiation Schemes;511
21.3.2;3.2 Negotiation-Based Schemes;514
21.3.3;3.3 Hybrid Schemes;519
21.4;4 Structure-Based Data Dissemination Schemes;520
21.4.1;4.1 Plain-Structure Schemes;521
21.4.2;4.2 Hierarchical-Structure Schemes;523
21.5;5 Other Techniques Used in Data Dissemination;525
21.5.1;5.1 Segmentation and Pipelining;525
21.5.2;5.2 Coding Technique;526
21.5.3;5.3 Constructive Interference;528
21.6;6 Performance Evaluation;529
21.6.1;6.1 Performance Metrics;530
21.6.2;6.2 Performance Comparisons;531
21.7;7 Open Issues;532
21.8;8 Conclusion;533
21.9;References;534
22;A Data Fusion Algorithm for Multiple Applications in Wireless Sensor Networks;538
22.1;1 Introduction;538
22.2;2 Basic Concepts;541
22.2.1;2.1 Information Fusion for Wireless Sensor Networks;541
22.2.2;2.2 Kurtosis and Skewness Concepts;543
22.3;3 Related Work;546
22.4;4 Hephaestus;551
22.4.1;4.1 Local Information Fusion Procedure (LIFH);552
22.4.2;4.2 Complementary Information Fusion Procedure (CIFH);556
22.5;5 Evaluation;559
22.5.1;5.1 Environment Configuration and Application Scenario;560
22.5.2;5.2 Metrics;561
22.5.3;5.3 Energy Model;562
22.5.4;5.4 Evaluating Hephaestus Overhead;563
22.5.5;5.5 Evaluating Hephaestus Accuracy;568
22.5.6;5.6 Analysis of Results;570
22.6;6 Final Remarks;570
22.7;References;571
23;Topology Control and Routing;574
24;Underwater Networks for Ocean Monitoring: A New Challenge for Topology Control and Opportunistic Routing;575
24.1;1 Introduction;576
24.2;2 The Fundamental Role of Underwater Sensor Networks;577
24.3;3 Characteristics of Underwater Sensor Networks;578
24.3.1;3.1 Network Architecture;579
24.3.2;3.2 High Cost;580
24.3.3;3.3 Involuntary Mobility;581
24.3.4;3.4 Underwater Acoustic Channel;581
24.4;4 The Benefits of Topology Control in Underwater Sensor Networks;584
24.4.1;4.1 Power Control-Based Topology Control;585
24.4.2;4.2 Wireless Interface Management-Based Topology Control;587
24.4.3;4.3 Mobility-Assisted-Based Topology Control;590
24.5;5 Geographic and Opportunistic Routing in Underwater Sensor Networks;591
24.5.1;5.1 Void-Handling Procedure;592
24.5.2;5.2 Candidate Set Selection Procedure;595
24.5.3;5.3 Candidate Coordination Procedure;598
24.6;6 Future Research Directions;600
24.7;7 Concluding Remarks;601
24.8;References;602
25;Geometric Routing Without Coordinates but Measurements;606
25.1;1 Introduction—Routing in Wireless Ad Hoc Networks;606
25.1.1;1.1 Local and Stateless Routing;608
25.1.2;1.2 Geometric Routing;609
25.2;2 Geometric Routing on Virtual Raw Anchor Coordinates;611
25.2.1;2.1 Why Another Coordinate System?;611
25.3;3 Virtual Raw Anchor Coordinate System;612
25.4;4 Graph Planarization on Virtual Raw Anchor Coordinate System;613
25.5;5 Combined Greedy–Face Routing with Delivery Guarantees;616
25.5.1;5.1 Greedy Routing Primitives;616
25.5.2;5.2 Face Routing Primitives: Combinatorial Approach;617
25.5.3;5.3 Face Routing Primitives : Geometric Approach;625
25.5.4;5.4 Numerical Validation;629
25.6;6 Greedy Routing over Virtual Raw Anchor Coordinates;630
25.6.1;6.1 Schnyder Characterization and Saturated Graph;631
25.6.2;6.2 Characterization of Greedy Paths;632
25.6.3;6.3 Routing in Maximal Planar Graph;635
25.7;References;636
26;Delay-Tolerant Mobile Sensor Networks: Routing Challenges and Solutions;638
26.1;1 Introduction;638
26.2;2 General (Terrestrial) Delay-Tolerant Mobile Sensor Networks;642
26.2.1;2.1 Replication-Based Routing;644
26.2.2;2.2 Utility or Single-Copy-Based Routing;645
26.2.3;2.3 Erasure-Coding-Based Routing;647
26.2.4;2.4 Social-Based Routing;650
26.3;3 Underwater Delay-tolerant Mobile Sensor Networks;652
26.3.1;3.1 Geographical Routing;653
26.3.2;3.2 Mobile Relays (AUVs)-Based Routing;654
26.3.3;3.3 Clustering-Based Routing;654
26.3.4;3.4 Opportunistic and Prediction-Based Routing;655
26.4;4 Flying Delay-Tolerant Mobile Sensor Networks;656
26.4.1;4.1 Routing Table-Based Routing;658
26.4.2;4.2 Hierarchical/Clustering-Based Routing;659
26.4.3;4.3 Geographical Routing;660
26.5;5 Performance Evaluation Metrics;661
26.5.1;5.1 Average Delivery Ratio;661
26.5.2;5.2 Average End-to-End Delivery Delay;662
26.5.3;5.3 Average Delivery Cost or Messaging Overhead;662
26.5.4;5.4 Routing Efficiency;663
26.5.5;5.5 Network Lifetime;663
26.6;6 Conclusion;664
26.7;References;665
27;Privacy and Security;670
28;Location Privacy in Wireless Sensor Networks;671
28.1;1 Introduction;672
28.2;2 Anonymity Definition and Categorization;674
28.3;3 Source Location Privacy in WSNs;676
28.3.1;3.1 Attack Models for Source Location Privacy;676
28.3.2;3.2 Privacy-Preserving Measures for Source Location;678
28.4;4 Sink Location Privacy in WSN;682
28.4.1;4.1 BS Location Privacy Attack Models;683
28.4.2;4.2 Base Station Location Privacy-Preserving Measures;688
28.5;5 Sink Location Privacy Attack Models: Strengths and Weaknesses;694
28.5.1;5.1 Traffic Volume—Discussion and Critique;694
28.5.2;5.2 GSAT Test—Discussion and Critique;698
28.5.3;5.3 Evidence Theory—Discussion and Critique;698
28.6;6 A Novel Traffic Analysis Attack Model and Base Station Anonymity Metrics;702
28.6.1;6.1 EARS’s Hot Spot Cells Identification Phase;703
28.6.2;6.2 EARS’s Sink Cell Identification Phase;706
28.6.3;6.3 EARS Complexity Analysis and Anonymity Metrics;708
28.6.4;6.4 EARS Evaluation;709
28.7;7 Conclusion;712
28.8;References;713
29;Implementation of Secure Communications for Tactical Wireless Sensor Networks;717
29.1;1 Introduction;717
29.1.1;1.1 Low Power Wireless Sensor Networks;718
29.1.2;1.2 Introduction to 6LoWPAN/IEEE 802.15.4;719
29.1.3;1.3 Chapter Motivations;719
29.1.4;1.4 Chapter Objectives and Outline;721
29.2;2 Related Works;721
29.2.1;2.1 Architecture of a Tactical WSN;722
29.2.2;2.2 Routing;722
29.2.3;2.3 6LoWPAN Frame Structure;723
29.2.4;2.4 Security Mechanisms;723
29.3;3 Theoretical Framework for Security Architecture;726
29.3.1;3.1 Network Design;726
29.3.2;3.2 Encryption;735
29.3.3;3.3 6LoWPAN Enabled IEEE 802.15.4 Frame Structure;736
29.3.4;3.4 Deployment of Nodes;739
29.3.5;3.5 Proposed Attacks;740
29.4;4 Experimental Setup;741
29.4.1;4.1 Sensor Parameters;741
29.4.2;4.2 Node Characteristics;742
29.4.3;4.3 Frame Parameters;744
29.4.4;4.4 Network Parameters;745
29.5;5 Simulation Results and Analysis;746
29.5.1;5.1 Spoofing Results;747
29.5.2;5.2 DOS Results;749
29.5.3;5.3 MITM Results;754
29.6;6 Conclusion;756
29.7;References;757
30;Data-Driven Detection of Sensor-Hijacking Attacks on Electrocardiogram Sensors;758
30.1;1 Introduction;759
30.1.1;1.1 Sensor-Hijacking Attacks on Wearable Medical IoT Systems;760
30.1.2;1.2 Challenges in Detecting Sensor Hijacking;761
30.1.3;1.3 ECG Sensor Hijack Detection;763
30.1.4;1.4 Chapter Organization;765
30.2;2 Related Work;766
30.3;3 System Model, Threat Model, and Problem Statement;767
30.4;4 Background;768
30.5;5 Detecting Temporal ECG Alterations;770
30.6;6 Evaluation of ECG Temporal Alteration Detector;773
30.7;7 Discussion;777
30.8;8 Conclusions;779
30.8.1;8.1 Future Work;779
30.9;References;780
31;Cryptography in WSNs;783
31.1;1 Introduction;783
31.2;2 Symmetric Key Cryptography;786
31.2.1;2.1 Block Ciphers;787
31.2.2;2.2 Stream Ciphers;794
31.2.3;2.3 Key Exchange;796
31.3;3 Asymmetric Key Cryptography;798
31.3.1;3.1 RSA;799
31.3.2;3.2 Hash Functions;801
31.3.3;3.3 HMAC;803
31.3.4;3.4 Digital Signatures;804
31.3.5;3.5 Digital Certificates;805
31.4;4 Applications of Cryptography;806
31.4.1;4.1 Privacy;806
31.4.2;4.2 Key Wrapping by Asymmetric Key Cryptography;807
31.4.3;4.3 User Authentication;807
31.5;5 Cryptography in WSNs;809
31.5.1;5.1 Security Threats;810
31.5.2;5.2 Symmetric Versus Asymmetric Key Cryptography;810
31.5.3;5.3 ECC;811
31.5.4;5.4 Key Management;813
31.5.5;5.5 Identity-Based Cryptography;814
31.6;6 Conclusions and Open Issues;815
31.7;References;816