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Wireless Ad Hoc and Sensor Networks: Management, Performance, and Applications [Minkštas viršelis]

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  • Formatas: Paperback / softback, 376 pages, aukštis x plotis: 234x156 mm, weight: 453 g
  • Išleidimo metai: 19-Jun-2019
  • Leidėjas: CRC Press
  • ISBN-10: 0367379686
  • ISBN-13: 9780367379681
Kitos knygos pagal šią temą:
Wireless Ad Hoc and Sensor Networks: Management, Performance, and ApplicationsDidesnis paveikslėlis
  • Formatas: Paperback / softback, 376 pages, aukštis x plotis: 234x156 mm, weight: 453 g
  • Išleidimo metai: 19-Jun-2019
  • Leidėjas: CRC Press
  • ISBN-10: 0367379686
  • ISBN-13: 9780367379681
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780367379681.html

Although wireless sensor networks (WSNs) have been employed across a wide range of applications, there are very few books that emphasize the algorithm description, performance analysis, and applications of network management techniques in WSNs. Filling this need, Wireless Ad Hoc and Sensor Networks: Management, Performance, and Applications summarizes not only traditional and classical network management techniques, but also state-of-the-art techniques in this area.

The articles presented are expository, but scholarly in nature, including the appropriate history background, a review of current thinking on the topic, and a discussion of unsolved problems. The book is organized into three sections. Section I introduces the basic concepts of WSNs and their applications, followed by the summarization of the network management techniques used in WSNs.

Section II begins by examining virtual backbone-based network management techniques. It points out some of the drawbacks in classical and existing methods and proposes several new network management techniques for WSNs that can address the shortcomings of existing methods. Each chapter in this section examines a new network management technique and includes an introduction, literature review, network model, algorithm description, theoretical analysis, and conclusion.

Section III applies proposed new techniques to some important applications in WSNs including routing, data collection, data aggregation, and query processing. It also conducts simulations to verify the performance of the proposed techniques. Each chapter in this section examines a particular application using the following structure: brief application overview, application design and implementation, performance analysis, simulation settings, and comments for different test cases/scenario configurations.

List of Figures xv
List of Tables xix
Preface xxi
Authors xxiii
Contributors xxv
Section I: Background 1(8)
1 Introduction
3(6)
1.1 Wireless Sensor Networks
3(2)
1.1.1 Basic Idea
3(1)
1.1.2 Deterministic Wireless Sensor Networks and Probabilistic Wireless Sensor Networks
4(1)
1.2 Topology Control in Wireless Sensor Networks
5(6)
1.2.1 Motivation
5(1)
1.2.2 Options for Topology Control
5(2)
1.2.3 Measurements of Topology Control Algorithms
7(2)
Section II: Management And Performance 9(166)
2 Greedy-based Construction of Load-balanced Virtual Backbones in Wireless Sensor Networks
11(28)
2.1 Introduction
12(4)
2.2 Related Work
16(2)
2.2.1 Centralized Algorithms for CDS
16(1)
2.2.2 Distributed Algorithms for CDS
16(1)
2.2.3 Other Algorithms for CDSs
17(1)
2.2.4 Other Load-balancing Related Work
17(1)
2.2.5 Remarks
18(1)
2.3 Problem Statement
18(2)
2.3.1 Network Model
19(1)
2.3.2 Preliminary
19(1)
2.3.3 Problem Definition
20(1)
2.4 Load-Balanced CDS
20(2)
2.4.1 Algorithm Description
20(1)
2.4.2 Example Illustration
21(1)
2.4.3 Remarks
22(1)
2.5 Load-balanced Allocation of Dominatees
22(9)
2.5.1 Terminologies
22(3)
2.5.2 Algorithm Description
25(4)
2.5.2.1 Centralized Algorithm
26(1)
2.5.2.2 Distributed Algorithm
27(2)
2.5.3 Analysis
29(2)
2.6 Performance Evaluation
31(6)
2.6.1 Scenario 1 - Data Aggregation Communication Mode
31(3)
2.6.1.1 Simulation Environment
31(1)
2.6.1.2 Simulation Results
31(3)
2.6.2 Scenario 2 - Data Collection Communication Mode
34(9)
2.6.2.1 Simulation Environment
35(1)
2.6.2.2 Simulation Results
35(2)
2.7 Conclusion
37(2)
3 Load-balanced CDS Construction in Wireless Sensor Networks via Genetic Algorithm
39(24)
3.1 Introduction
40(3)
3.2 Related Work
43(3)
3.2.1 Centralized Algorithms for CDSs
44(1)
3.2.2 Subtraction-based Distributed Algorithms for CDSs
44(1)
3.2.3 Addition-based Distributed Algorithms for CDSs
44(1)
3.2.4 Other Algorithms
45(1)
3.2.5 Remarks
46(1)
3.3 Problem Definition
46(3)
3.3.1 Network Model
46(1)
3.3.2 Terminologies
46(2)
3.3.3 Problem Definition
48(1)
3.4 LBCDS-GA Algorithm
49(9)
3.4.1 GA Overview
49(1)
3.4.2 Representation of Chromosomes
49(2)
3.4.3 Population Initialization
51(1)
3.4.4 Fitness Function
52(1)
3.4.5 Selection Scheme
52(1)
3.4.6 Genetic Operations
52(3)
3.4.6.1 Crossover
53(2)
3.4.6.2 Gene Mutation
55(1)
3.4.7 Meta-gene Mutation
55(1)
3.4.8 Replacement Policy
56(2)
3.5 Performance Evaluation
58(2)
3.5.1 Simulation Environment
58(1)
3.5.2 Simulation Results and Analysis
58(2)
3.6 Conclusion
60(3)
4 Approximation Algorithms for Load-balanced Virtual Backbone Construction in Wireless Sensor Networks
63(28)
4.1 Introduction
64(3)
4.2 Related Work
67(2)
4.2.1 Subtraction-based Algorithms for CDS-based VBs
68(1)
4.2.2 Addition-based Algorithms Using Single Leader for CDS-based VBs
68(1)
4.2.3 Addition-based Algorithms Using Multiple Leader for CDS-based VBs
68(1)
4.2.4 Other Algorithms
69(1)
4.2.5 Remarks
69(1)
4.3 Problem Formulation
69(2)
4.3.1 Network Model
69(1)
4.3.2 Problem Definition
70(1)
4.4 Load-balanced Virtual Backbone Problem
71(7)
4.4.1 INP Formulation of MDMIS
72(1)
4.4.2 Approximation Algorithm
73(4)
4.4.3 Connected Virtual Backbone
77(1)
4.5 MinMax Valid-degree Non-backbone Node Allocation
78(5)
4.5.1 ILP Formulation of MVBA
80(1)
4.5.2 Randomized Approximation Algorithm
80(3)
4.6 Performance Evaluation
83(5)
4.6.1 Simulation Environment
84(1)
4.6.2 Scenario 1: Change Total Number of Nodes
84(2)
4.6.3 Scenario 2: Change Side Length of Square Area
86(2)
4.6.4 Scenario 3: Change Node Transmission Range
88(1)
4.7 Conclusion
88(3)
5 A Genetic Algorithm with Immigrants Schemes for Constructing σ-Reliable MCDSs in Probabilistic Wireless Sensor Networks
91(26)
5.1 Introduction
92(3)
5.2 Related Work
95(3)
5.2.1 MCDS under DNM
95(2)
5.2.1.1 Centralized Algorithms for CDSs
96(1)
5.2.1.2 Subtraction-based Localized Algorithms for CDSs
96(1)
5.2.1.3 Distributed Algorithms for CDSs
96(1)
5.2.2 Related Literature about PNM Model
97(1)
5.2.3 Remarks
97(1)
5.3 Problem Statement
98(3)
5.3.1 Assumptions
98(1)
5.3.2 Network Model
98(2)
5.3.3 Problem Definition
100(1)
5.3.4 Remarks
100(1)
5.4 RMCDS-GA Algorithm
101(11)
5.4.1 GA Overview
101(2)
5.4.2 Representation of Chromosomes
103(1)
5.4.3 Population Initialization
103(3)
5.4.4 Fitness Function
106(1)
5.4.5 Selection (Reproduction) Scheme
107(2)
5.4.6 Genetic Operations
109(4)
5.4.6.1 Crossover
109(2)
5.4.6.2 Mutation
111(1)
5.4.6.3 Replacement Policy
112(1)
5.5 Genetic Algorithms with Immigrants Schemes
112(1)
5.6 Performance Evaluation
113(2)
5.6.1 Simulation Environment
114(1)
5.6.2 Simulation Results
114(1)
5.7 Conclusion
115(2)
6 Constructing Load-balanced Virtual Backbones in Probabilistic Wireless Sensor Networks via Multi-Objective Genetic Algorithm
117(28)
6.1 Introduction
118(3)
6.2 Related Work
121(2)
6.2.1 CDS-based VBs under DNM
121(1)
6.2.2 Related Literature about PNM Model
122(1)
6.2.3 Literature Review of MOGAs
122(1)
6.2.4 Remarks
122(1)
6.3 Network Model and Problem Definition
123(5)
6.3.1 Assumptions
123(1)
6.3.2 Network Model
123(1)
6.3.3 Preliminary
124(4)
6.3.4 Problem Definition
128(1)
6.4 LBVBP-MOGA Algorithm
128(12)
6.4.1 Overview of MOGAs
129(2)
6.4.1.1 Multi-objective Problem (MOP) Definitions and Overview
129(1)
6.4.1.2 GA Overview
129(1)
6.4.1.3 MOGA Overview
130(1)
6.4.2 Design of LBVBP-MOGA
131(7)
6.4.2.1 Representation of Chromosomes
131(1)
6.4.2.2 Population Initialization
132(1)
6.4.2.3 Fitness Function
132(1)
6.4.2.4 Selection Scheme and Replacement Policy
133(2)
6.4.2.5 Genetic Operations
135(3)
6.4.3 Convergence Analysis
138(2)
6.5 Performance Evaluation
140(2)
6.5.1 Simulation Environment
140(1)
6.5.2 Simulation Results
141(1)
6.6 Conclusion
142(3)
7 Constructing Load-balanced Data Aggregation Trees in Probabilistic Wireless Sensor Networks
145(30)
7.1 Introduction
146(4)
7.2 Related Work
150(2)
7.2.1 Energy-efficient Aggregation Scheduling
150(1)
7.2.2 Minimum Latency Aggregation Scheduling
150(1)
7.2.3 Maximum Lifetime Aggregation Scheduling
151(1)
7.2.4 Remarks
152(1)
7.3 Network Model and Problem Definition
152(4)
7.3.1 Assumptions
152(1)
7.3.2 Network Model
153(1)
7.3.3 Problem Definition
153(3)
7.3.4 Remarks
156(1)
7.4 Connected Maximal Independent Set
156(8)
7.4.1 INP Formulation of LBMIS
156(2)
7.4.2 Approximation Algorithm
158(3)
7.4.3 Connecting LB MIS
161(2)
7.4.4 LBPNA for Non-leaf Nodes
163(1)
7.5 Load-balanced Data Aggregation Tree
164(5)
7.5.1 ILP Formulation of LBPNA for Leaf Nodes
164(1)
7.5.2 Randomized Approximation Algorithm
165(4)
7.6 Performance Evaluation
169(5)
7.6.1 Simulation Environment
169(1)
7.6.2 Scenario 1: Change side length of square area
169(4)
7.6.3 Scenario 2: Change node transmission range
173(1)
7.6.4 Scenario 3: Change total number of nodes
173(1)
7.7 Conclusion
174(1)
Section III: Applications 175(168)
8 Reliable and Energy Efficient Target Coverage for Wireless Sensor Networks
177(18)
8.1 Introduction
178(1)
8.2 Related Work
179(2)
8.2.1 Target Coverage
180(1)
8.2.2 Other Coverage
180(1)
8.2.3 Remarks
181(1)
8.3 Network Model and Related Definitions
181(5)
8.3.1 Network Model
181(1)
8.3.2 Related Definitions
182(3)
8.3.3 Problem Formulation
185(1)
8.4 Our Proposed Algorithm
186(3)
8.4.1 a-RMSC Heuristic Algorithm Overview
187(1)
8.4.2 Contribution Function
187(2)
8.4.3 Relation between MSC and α-RMSC
189(1)
8.5 Performance Evaluation
189(4)
8.5.1 Simulation 1: Control Failure Probability
189(2)
8.5.2 Simulation 2: Comparison between α-RMSC and MSC
191(2)
8.6 Conclusion
193(2)
9 CDS-based Multi-regional Query Processing in Wireless Sensor Networks
195(26)
9.1 Introduction
196(2)
9.2 Related Work
198(2)
9.2.1 Periodic Query Scheduling
198(1)
9.2.2 Dynamic Query Scheduling
199(1)
9.2.3 Remarks
200(1)
9.3 Problem Formulation
200(5)
9.3.1 Network Model
200(1)
9.3.2 Multi-regional Query
201(1)
9.3.3 Problem Definition
202(3)
9.4 Multi-regional Query Scheduling
205(10)
9.4.1 Construction of MRQF
205(2)
9.4.2 MRQSA
207(4)
9.4.2.1 Scheduling Initialization
207(1)
9.4.2.2 Scheduling Algorithm
207(4)
9.4.3 Performance Analysis
211(4)
9.5 Performance Evaluation
215(3)
9.5.1 Simulation Environment
215(1)
9.5.2 Simulation Results
215(3)
9.6 Conclusion
218(3)
10 CDS-based Snapshot and Continuous Data Collection in Dual-radio Multi-channel Wireless Sensor Networks
221(38)
10.1 Introduction
222(2)
10.2 Related Work
224(4)
10.2.1 Capacity for Single-radio Single-channel Wireless Networks
224(3)
10.2.2 Capacity for Multi-channel Wireless Networks
227(1)
10.2.3 Remarks
227(1)
10.3 Network Model and Preliminaries
228(4)
10.3.1 Network Model
228(1)
10.3.2 Routing Tree
229(2)
10.3.3 Vertex Coloring Problem
231(1)
10.4 Capacity of SDC
232(8)
10.4.1 Scheduling Algorithm for SDC
232(5)
10.4.2 Capacity Analysis
237(2)
10.4.3 Discussion
239(1)
10.5 Capacity of CDC
240(10)
10.5.1 Compressive Data Gathering (CDG)
240(2)
10.5.2 Pipeline Scheduling
242(1)
10.5.3 Capacity Analysis
243(7)
10.6 Simulations and Results Analysis
250(7)
10.6.1 Performance of MPS
253(1)
10.6.2 Performance of PS
253(2)
10.6.3 Impacts of N and M
255(2)
10.7 Conclusion
257(2)
11 CDS-based Distributed Data Collection in Wireless Sensor Networks
259(34)
11.1 Introduction
260(3)
11.2 Related Works
263(2)
11.2.1 Data Collection Capacity
263(1)
11.2.2 Multicast Capacity
263(1)
11.2.3 Uni/Broadcast Capacity
264(1)
11.2.3.1 Uni/Broadcast Capacity for Random Wireless Networks
264(1)
11.2.3.2 Uni/Broadcast Capacity for Arbitrary Wireless Networks
264(1)
11.2.3.3 Unicast Capacity for Mobile Wireless Networks
265(1)
11.2.4 Remarks
265(1)
11.3 Network Model
265(1)
11.4 Carrier-sensing Range
266(5)
11.5 Distributed Data Collection and Capacity
271(9)
11.5.1 Distributed Data Collection
271(2)
11.5.2 Capacity Analysis
273(7)
11.6 R0-PCR-based Distributed Data Aggregation
280(3)
11.7 Data Collection and Aggregation under Poisson Distribution Model
283(2)
11.8 Simulation Results
285(6)
11.8.1 DDC Capacity versus R0 and α
286(2)
11.8.2 Scalability of DDC
288(1)
11.8.3 Performance of DDA
288(3)
11.9 Conclusion
291(2)
12 CDS-based Broadcast Scheduling in Cognitive Radio Networks
293(50)
12.1 Introduction
294(2)
12.2 Related Work
296(2)
12.2.1 Broadcast Scheduling in Traditional Wireless Networks
296(1)
12.2.2 Broadcast Scheduling in CRNs
297(1)
12.2.3 Remarks
298(1)
12.3 System Model and Problem Definition
298(2)
12.3.1 Network Model
298(1)
12.3.2 Interference Model
299(1)
12.3.3 Problem Definition
300(1)
12.4 Broadcasting Tree and Coloring
300(3)
12.4.1 CDS-based Broadcasting Tree
300(2)
12.4.2 Tessellation and Coloring
302(1)
12.5 Broadcast Scheduling under UDG Model
303(10)
12.5.1 MLBS under UDG Model
303(4)
12.5.2 Analysis of MBS-UDG
307(6)
12.5.2.1 Broadcast Latency of MBS-UDG
307(6)
12.5.2.2 Broadcast Redundancy of MBS-UDG
313(1)
12.6 Broadcast Scheduling under PrIM
313(2)
12.6.1 Redundancy of MBS-PrIM
315(1)
12.7 Simulation and Analysis
315(5)
12.7.1 Broadcast Latency of MBS
316(2)
12.7.2 Broadcast Redundancy of MBS
318(2)
12.8 Conclusion
320(1)
References
321(22)
Index 343
Dr. Jing (Selena) He is currently the Assistant Professor in the Department of Computer Science at Kennesaw State University. She received her B.S. in Electric Engineering from Wuhan Institute of Technology and her M.S. of Computer Science from Utah State University, respectively. Her research interests include wireless ad hoc networks, wireless sensor networks, cyber-physical systems, social networks, and cloud computing. She is now an IEEE member and an IEEE COMSOC member.

Shouling Ji is currently a Ph.D. student in the Department of Computer Science at Georgia State University. He received his B.S. and M.S. in Computer Science from the School of Computer Science and Technology at Heilongjiang University, China, in 2007 and 2010, respectively. His research interests include wireless sensor networks, data management in wireless networks, cognitive radio networks, and cyber physical systems. He is now an ACM student member, an IEEE student member, and an IEEE COMSOC student member. Dr. Yi Pan is a professor and chair of the Department of Computer Science and a professor in the Department of Computer Information Systems at Georgia State University. Dr. Pan received his B.Eng. and M.Eng. in Computer Engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. in Computer Science from the University of Pittsburgh, in 1991. Dr. Pans research interests include parallel and distributed computing, optical networks, wireless networks, and bioinformatics. Dr. Pan has published more than 100 journal papers with about 50 papers published in various IEEE/ACM journals. He is a co-inventor of three U.S. patents (pending) and 5 provisional patents, and has received many awards from agencies such as NSF, AFOSR, JSPS, IISF and the Mellon Foundation. Dr. Pan has served as an editor-in-chief or editorial board member for 15 journals including 6 IEEE Transactions and a