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Distributed network structure estimation using consensus methods / Sai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar.

By: Zhang, Sai (Electrical engineering student) [author.]Contributor(s): Tepedelenlioglu, Cihan [author.] | Spanias, Andreas [author.] | Banavar, Mahesh K [author.]Material type: Text

TextSeries: Synthesis digital library of engineering and computer science | Synthesis lectures on communications ; #13.Publisher: San Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, [2018]Description: 1 PDF (xi, 76 pages) : illustrations (chiefly color)Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681732916; 9781681732909Subject(s): Wireless sensor networks | Electronic data processing -- Distributed processing | wireless sensor networks | diffusion adaptation | node counting | Internet-of-Things (IoT)Genre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 681/.2 LOC classification: TK7872.D48 | .Z534 2018Online resources: Abstract with links to resource Also available in print.

Contents:

1. Introduction -- 1.1. Wireless sensor networks -- 1.2. Applications -- 1.3. Consensus methods in distributed WSNs -- 1.4. Network structure estimation -- 1.5. Organization of the book

2. Review of consensus and network structure estimation -- 2.1. Graph representation of distributed WSNs -- 2.2. Review of consensus algorithms -- 2.3. Review of network structure estimation

3. Distributed node counting in WSNs -- 3.1. System model -- 3.2. Distributed node counting based on L2 norm estimation -- 3.3. Performance analysis -- 3.4. Simulation results

4. Noncentralized estimation of degree distribution -- 4.1. System model -- 4.2. Consensus-based degree distribution estimation -- 4.3. Estimation of degree matrix -- 4.4. Performance analysis -- 4.5. Simulations

5. Network center and coverage region estimation -- 5.1. System model -- 5.2. Estimation of network center and radius -- 5.3. Performance analysis -- 5.4. Simulations -- 5.5. Discussion : global data structure estimation -- 6. Conclusions -- A. Notation.

Abstract: The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.

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Item type	Current library	Call number	Status	Date due	Barcode
Ebooks	Indian Institute of Technology Delhi - Central Library		Available

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 59-74).

1. Introduction -- 1.1. Wireless sensor networks -- 1.2. Applications -- 1.3. Consensus methods in distributed WSNs -- 1.4. Network structure estimation -- 1.5. Organization of the book

2. Review of consensus and network structure estimation -- 2.1. Graph representation of distributed WSNs -- 2.2. Review of consensus algorithms -- 2.3. Review of network structure estimation

3. Distributed node counting in WSNs -- 3.1. System model -- 3.2. Distributed node counting based on L2 norm estimation -- 3.3. Performance analysis -- 3.4. Simulation results

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm for estimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.

Also available in print.

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