TY - JOUR
T1 - Distributed Network Structure Estimation using Consensus Methods
AU - Zhang, Sai
AU - Tepedelenlioglu, Cihan
AU - Spanias, Andreas
AU - Banavar, Mahesh
N1 - Publisher Copyright:
Copyright © 2018 by Morgan & Claypool.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Internet-of-Things (IoT)
KW - diffusion adaptation
KW - node counting
KW - wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85042926047&partnerID=8YFLogxK
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U2 - 10.2200/S00829ED1V01Y201802COM013
DO - 10.2200/S00829ED1V01Y201802COM013
M3 - Article
AN - SCOPUS:85042926047
SN - 1932-1244
VL - 10
SP - 1
EP - 88
JO - Synthesis Lectures on Communications
JF - Synthesis Lectures on Communications
IS - 1
ER -