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Distributed network structure estima...

Banavar, Mahesh,

Distributed network structure estimation using consensus methods

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed network structure estimation using consensus methodsSai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar.
作者:	Zhang, Sai.
其他作者:	Tepedelenlioglu, Cihan,
出版者:	San Rafael, California :Morgan & Claypool Publishers,2018.
面頁冊數:	1 online resource (90 p.)
標題:	Wireless sensor networks.
電子資源:	click for full text
ISBN:	1681732904

Distributed network structure estimation using consensus methods
Zhang, Sai.

Distributed network structure estimation using consensus methods[electronic resource] /Sai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar. - 1st ed. - San Rafael, California :Morgan & Claypool Publishers,2018. - 1 online resource (90 p.) - Synthesis Lectures on Communications ;13.. - Synthesis Lectures on Communications ;13..

Includes bibliographical references and index.

Distributed network structure estimation using consensus methods -- Synthesis Lectures on Communications -- Abstract & Keywords -- Contents -- Preface -- Acknowledgments -- Chapter 1: Introduction -- Chapter 2: Review of Consensus and Network Structure Estimation -- Chapter 3: Distributed Node Counting in WSNs -- Chapter 4: Noncentralized Estimation of Degree Distribution -- Chapter 5: Network Center and Coverage Region Estimation -- Chapter 6: Conclusions -- Appendix A: Notation -- Bibliography -- Authors' Biographies.

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.

ISBN: 1681732904Subjects--Topical Terms:

376151
Wireless sensor networks.

LC Class. No.: TK7872.D48

Dewey Class. No.: 681.2

Distributed network structure estimation using consensus methods
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520 3 $a 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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