Distributed network structure estimation using consensus methods /

Bibliographic Details
Main Authors:	Zhang, Sai (Electrical engineering student) (Author), Banavar, Mahesh K. (Author), Spanias, Andreas (Author), Tepedelenlioğlu, Cihan (Author)
Corporate Author:	Morgan & Claypool Publishers
Format:	eBook
Language:	English
Published:	San Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, [2018]
Series:	Synthesis digital library of engineering and computer science. Synthesis lectures on communications ; #13.
Subjects:	Electronic data processing > Distributed processing. Wireless sensor networks. Internet-of-Things (IoT) diffusion adaptation node counting wireless sensor networks Electronic books.
Online Access:	Connect to the full text of this electronic book (PDF)

Description
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.
Item Description:	Part of: Synthesis digital library of engineering and computer science.
Physical Description:	1 online resource (xi, 76 pages) : illustrations (chiefly color) Also available in print.
Format:	Mode of access: World Wide Web. System requirements: Adobe Acrobat Reader.
Bibliography:	Includes bibliographical references (pages 59-74).
ISBN:	9781681732909 9781681732916
ISSN:	1932-1708 ;
DOI:	10.2200/S00829ED1V01Y201802COM013
Access:	Abstract freely available; full-text restricted to subscribers or individual document purchasers.