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.

Original languageEnglish (US)
Pages (from-to)1-88
Number of pages88
JournalSynthesis Lectures on Communications
Volume10
Issue number1
DOIs
StatePublished - Jan 1 2018

Fingerprint

Wireless sensor networks
Parallel algorithms
Military applications
Graph theory
Sensor nodes
Sensor networks
Scalability
Sustainable development
Health
Monitoring

Keywords

  • diffusion adaptation
  • Internet-of-Things (IoT)
  • node counting
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Distributed Network Structure Estimation using Consensus Methods. / Zhang, Sai; Tepedelenlioglu, Cihan; Spanias, Andreas; Banavar, Mahesh.

In: Synthesis Lectures on Communications, Vol. 10, No. 1, 01.01.2018, p. 1-88.

Research output: Contribution to journalArticle

@article{e56023048d9443d1b121f5f671ac5735,
title = "Distributed Network Structure Estimation using Consensus Methods",
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.",
keywords = "diffusion adaptation, Internet-of-Things (IoT), node counting, wireless sensor networks",
author = "Sai Zhang and Cihan Tepedelenlioglu and Andreas Spanias and Mahesh Banavar",
year = "2018",
month = "1",
day = "1",
doi = "10.2200/S00829ED1V01Y201802COM013",
language = "English (US)",
volume = "10",
pages = "1--88",
journal = "Synthesis Lectures on Communications",
issn = "1932-1244",
publisher = "Morgan and Claypool Publishers",
number = "1",

}

TY - JOUR

T1 - Distributed Network Structure Estimation using Consensus Methods

AU - Zhang, Sai

AU - Tepedelenlioglu, Cihan

AU - Spanias, Andreas

AU - Banavar, Mahesh

PY - 2018/1/1

Y1 - 2018/1/1

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 - diffusion adaptation

KW - Internet-of-Things (IoT)

KW - node counting

KW - wireless sensor networks

UR - http://www.scopus.com/inward/record.url?scp=85042926047&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042926047&partnerID=8YFLogxK

U2 - 10.2200/S00829ED1V01Y201802COM013

DO - 10.2200/S00829ED1V01Y201802COM013

M3 - Article

AN - SCOPUS:85042926047

VL - 10

SP - 1

EP - 88

JO - Synthesis Lectures on Communications

JF - Synthesis Lectures on Communications

SN - 1932-1244

IS - 1

ER -