Distributed Network Structure Estimation Using Consensus Methods (Record no. 86126)

000 -LEADER
fixed length control field 03964nam a22005535i 4500
001 - CONTROL NUMBER
control field 978-3-031-01684-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730165139.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2018 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031016844
-- 978-3-031-01684-4
082 04 - CLASSIFICATION NUMBER
Call Number 620
100 1# - AUTHOR NAME
Author Zhang, Sai.
245 10 - TITLE STATEMENT
Title Distributed Network Structure Estimation Using Consensus Methods
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2018.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XI, 76 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Communications,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- Acknowledgments -- Introduction -- Review of Consensus and Network Structure Estimation -- Distributed Node Counting in WSNs -- Noncentralized Estimation of Degree Distribution -- Network Center and Coverage Region Estimation -- Conclusions -- Bibliography -- Authors' Biographies.
520 ## - SUMMARY, ETC.
Summary, etc 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 forestimating 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.
700 1# - AUTHOR 2
Author 2 Tepedelenlioglu, Cihan.
700 1# - AUTHOR 2
Author 2 Spanias, Andreas.
700 1# - AUTHOR 2
Author 2 Banavar, Mahesh.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-01684-4
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2018.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electrical engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Telecommunication.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Technology and Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Electrical and Electronic Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Communications Engineering, Networks.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1932-1708
912 ## -
-- ZDB-2-SXSC

No items available.