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.
Jing Selena He
, Shouling Ji
, Yi Pan
, Yingshu Li
CRC Press Inc
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BACKGROUND Introduction Wireless Sensor Networks Basic Idea Deterministic Wireless Sensor Networks and Probabilistic Wireless Sensor Networks Topology Control in Wireless Sensor Networks Motivation Options for Topology Control Measurements of Topology Control Algorithms MANAGEMENT AND PERFORMANCE Greedy-based Construction of Load-balanced Virtual Backbones in Wireless Sensor Networks Introduction Related Work Centralized Algorithms for CDS Distributed Algorithms for CDS OtherAlgorithms for CDSs Other Load-balancing Related Work Remarks Problem Statement Network Model Preliminary Problem Definition Load-Balanced CDS Algorithm Description Example Illustration Remarks Load-balanced Allocation of Dominatees Terminologies Algorithm Description Centralized Algorithm Distributed Algorithm Analysis Performance Evaluation Scenario 1 - Data Aggregation Communication Mode Simulation Environment Simulation Results Scenario2 -Data Collection Communication Mode Simulation Environment Simulation Results Conclusion Load-balanced CDS Construction in Wireless Sensor Networks via Genetic Algorithm Introduction Related Work Centralized Algorithms for CDSs Subtraction-based Distributed Algorithms for CDSs Addition-based Distributed Algorithms for CDSs Other Algorithms Remarks Problem Definition Network Model Terminologies Problem Definition LBCDS-GA Algorithm GA Overview Representation of Chromosomes Population Initialization Fitness Function Selection Scheme Genetic Operations Crossover Gene Mutation Meta-gene Mutation Replacement Policy Performance Evaluation Simulation Environment Simulation Results and Analysis Conclusion Approximation Algorithms for Load-balanced Virtual Backbone Construction in Wireless Sensor Networks Introduction Related Work Subtraction-based Algorithms for CDS-based VBs Addition-based Algorithms Using Single Leader for CDS based VBs Addition-based Algorithms Using Multiple Leader for CDS based VBs OtherAlgorithms Remarks Problem Formulation Network Model Problem Definition Load-balanced Virtual Backbone Problem INP Formulation of MDMIS Approximation Algorithm Connected Virtual Backbone Min Max Valid-degree Non-backbone Node Allocation ILP Formulation of MVBA Randomized Approximation Algorithm Performance Evaluation Simulation Environment Scenario1:Change Total Number of Nodes Scenario2:Change Side Length of Square Area Scenario3:Change Node Transmission Range Conclusion A Genetic Algorithm with Immigrants Schemes for Constructing Ï - Reliable MCDSs in Probabilistic Wireless Sensor Networks Introduction Related Work MCDS under DNM Centralized Algorithms for CDSs Subtraction-based Localized Algorithms for CDSs Distributed Algorithms for CDSs Related Literature about PNM model Remarks Problem Statement Assumptions Network Model Problem Definition Remarks RMCDS-GA Algorithm GA Overview Representation of Chromosomes Population Initialization Fitness Function Selection (Reproduction) Scheme Genetic Operations Crossover Mutation Replacement Policy Genetic Algorithms with Immigrants Schemes Performance Evaluation Simulation Environment Simulation Results Conclusion Constructing Load-balanced Virtual Backbones in Probabilistic Wireless Sensor Networks via Multi-Objective Genetic Algorithm Introduction Related Work CDS-based VBs under DNM Related Literature about PNM model Literature Review of MOGAs Remarks Network Model and Problem Definition Assumptions Network Model Preliminary Problem Definition LBVBP-MOGA Algorithm Overview of MOGAs Multi-objective Problem (MOP) Definitions and Overview GA Overview MOGA Overview Design of LBVBP-MOGA Representation of Chromosomes Population Initialization Fitness Function Selection Scheme and Replacement Policy Genetic Operations . Convergence Analysis Performance Evaluation Simulation Environment Simulation Results Conclusion Constructing Load-balanced Data Aggregation Trees in Probabilistic Wireless Sensor Network Introduction Related Work Energy-efficient Aggregation Scheduling Minimum Latency Aggregation Scheduling Maximum Lifetime Aggregation Scheduling Remarks Network Model and Problem Definition Assumptions Network Model Problem Definition Remarks Connected Maximal Independent Set INP Formulation of LBMIS Approximation Algorithm Connecting LBMIS LBPNA for Non-leaf Nodes Load-Balanced Data Aggregation Tree ILP Formulation of LBPNA for Leaf Nodes Randomized Approximation Algorithm Performance Evaluation Simulation Environment Scenario1: Change side length of square area Scenario 2: Change node transmission range Scenario 3: Change total number of nodes Conclusion APPLICATIONS Reliable and Energy Efficient Target Coverage for Wireless Sensor Networks Introduction Related Work Target Coverage Other Coverage Remarks Network Model and Related Definitions Network Model Related Definitions Problem Formulation Our Proposed Algorithm Î±-RMSC Heuristic Algorithm Overview Contribution Function Relation between MSC and Î±-RMSC Performance Evaluation Simulation 1: Control Failure Probability Simulation 2: Comparison between Î± -RMSC and MSC Conclusion CDS-based Multi-regional Query Processing in Wireless Sensor Networks Introduction RelatedWork Periodic Query Scheduling Dynamic Query Scheduling Remarks Problem Formulation Network Model Multi-regional Query Problem Definition Multi-regional Query Scheduling Construction of MRQF MRQSA Scheduling Initialization Scheduling Algorithm Performance Analysis Performance Evaluation Simulation Environment Simulation Results Conclusion CDS-based Snapshot and Continuous Data Collection in Dual-radio Multi-channel Wireless Sensor Networks Introduction Related Work Capacity for Single-radio Single-channel Wireless Networks Capacity for Multi-channel Wireless Networks Remarks Network Model and Preliminaries Network Model Routing Tree Vertex Coloring Problem Capacity of SDC Scheduling Algorithm for SDC Capacity Analysis Discussion Capacity of CDC Compressive Data Gathering (CDG) Pipeline Scheduling Capacity Analysis Simulations and Results Analysis Performance of MPS Performance of PS Impacts of N and M Conclusion CDS-based Distributed Data Collection in Wireless Sensor Networks Introduction Related Works Data Collection Capacity Multicast Capacity Uni/Broadcast Capacity Uni/Broadcast Capacity for Random Wireless Networks Uni/Broadcast Capacity for Arbitrary Wireless Networks Unicast Capacity for Mobile Wireless Networks Remarks Network Model Carrier-sensing Range Distributed Data Collection and Capacity Distributed Data Collection Capacity Analysis R0-PCR-based Distributed Data Aggregation Data Collection and Aggregation under Poisson Distribution Model Simulation Results DDC Capacity versus R0 and Î± Scalability of DDC PerformanceofDDA Conclusion CDS-based Broadcast Scheduling in Cognitive Radio Networks Introduction Related Work Broadcast Scheduling in Traditional Wireless Networks Broadcast Scheduling in CRNs Remarks System Model and Problem Definition Network Model Interference Model Problem Definition Broadcasting Tree and Coloring CDS-based Broadcasting Tree Tessellation and Coloring Broadcast Scheduling under UDG Model MLBS under UDG Model Analysis of MBS-UDG Broadcast Latency of MBS-UDG Broadcast Redundancy of MBS-UDG Broadcast Scheduling under PrIM Redundancy of MBS-PrIM Simulation and Analysis Broadcast Latency of MBS Broadcast Redundancy of MBS Conclusion References Index
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. Pan's 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 guest editor for 10 special issues for 9 journals including 2 IEEE Transactions. Dr. Yingshu Li received her Ph.D. and M.S. from the Department of Computer Science and Engineering at University of Minnesota-Twin Cities. She received her B.S. from the Department of Computer Science and Engineering at Beijing Institute of Technology, China. Dr. Li is currently an Associate Professor in the Department of Computer Science at Georgia State University. Her research interests include wireless networking, cyber-physical systems, and phylogenetic analyses. Her research has been supported by the National Science Foundation (NSF) of the U.S., the National Science Foundation of China (NSFC), the Electronics and Telecommunications Research Institute (ETRI) of South Korea, and GSU internal grants. Dr. Li is the recipient of an NSF CAREER Award.