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Distributed network structure estimation using consensus methods[electronic resource] /

レコード種別:	コンピュータ・メディア : 単行資料
[NT 15000414] null:	681.2
タイトル / 著者:	Distributed network structure estimation using consensus methods/ Sai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar.
著者:	Zhang, Sai.
その他の著者:	Tepedelenlioglu, Cihan,
出版された:	San Rafael, California : : Morgan & Claypool Publishers,, 2018.
記述:	1 online resource (90 p.)
主題:	Wireless sensor networks.
主題:	Electronic data processing - Distributed processing.
主題:	Internet of things.
主題:	Networking.
国際標準図書番号 (ISBN) :	1681732904
国際標準図書番号 (ISBN) :	1681732912
国際標準図書番号 (ISBN) :	1681732920
国際標準図書番号 (ISBN) :	9781681732909
国際標準図書番号 (ISBN) :	9781681732916
国際標準図書番号 (ISBN) :	9781681732923
[NT 15000227] null:	Includes bibliographical references and index.
[NT 15000228] null:	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.
[NT 15000229] null:	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.
電子資源:	click for full text

Distributed network structure estimation using consensus methods[electronic resource] /
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:

388729
Wireless sensor networks.

LC Class. No.: TK7872.D48

Dewey Class. No.: 681.2

Distributed network structure estimation using consensus methods[electronic resource] /
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245 1 0 $a Distributed network structure estimation using consensus methods $h [electronic resource] / $c Sai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar.
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505 0 $a 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.
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.
650 0 $a Wireless sensor networks. $3 388729
650 0 $a Electronic data processing $x Distributed processing. $3 404378
650 0 $a Internet of things. $3 607448
650 0 $a Networking. $3 714596
700 1 $a Tepedelenlioglu, Cihan, $e author. $3 714573
700 1 $a Spanias, Andreas, $e author $3 712907
700 1 $a Banavar, Mahesh, $e author $3 712921
700 1 $a Tranter, William, $e author. $3 714574
830 0 $a Synthesis Lectures on Communications ; $v 13. $3 714595
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