Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks

Qi Cheng, Ruixin Niu, Ashok Sundaresan, Pramod Kumar Varshney

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

This chapter focuses on distributed detection and decision fusion problems, which involve the design of decision rules at the local sensors and at the fusion center to optimize detection performance, under either a Neyman-Pearson or a Bayesian criterion. It briefly introduces the fundamentals of detection theory. The chapter covers the conventional distributed detection problem with multiple sensors. It presents several important factors that affect the design of distributed detection algorithms. They are the topology of the sensor networks, relation between sensor observations (conditionally independent versus correlated), optimization criteria, and quantization levels. The chapter discusses the distributed detection in wireless sensor networks (WSNs). Assuming local sensor decision rules to be based on simple binary quantization of sensor observations, a novel method for fusion of correlated decisions using copula theory is discussed. Finally, the summary and some challenging issues for distributed detection are presented in the chapter.

Original languageEnglish (US)
Title of host publicationIntegrated Tracking, Classification, and Sensor Management: Theory and Applications
Publisherwiley
Pages619-660
Number of pages42
ISBN (Electronic)9781118450550
ISBN (Print)9780470639054
DOIs
StatePublished - May 31 2016

Fingerprint

Wireless sensor networks
Fusion reactions
Sensors
Sensor networks
Topology

Keywords

  • Copula theory
  • Decision fusion
  • Distributed detection
  • Multiple sensors
  • Wireless sensor networks (WSNs)

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Cheng, Q., Niu, R., Sundaresan, A., & Varshney, P. K. (2016). Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks. In Integrated Tracking, Classification, and Sensor Management: Theory and Applications (pp. 619-660). wiley. https://doi.org/10.1002/9781118450550.ch16

Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks. / Cheng, Qi; Niu, Ruixin; Sundaresan, Ashok; Varshney, Pramod Kumar.

Integrated Tracking, Classification, and Sensor Management: Theory and Applications. wiley, 2016. p. 619-660.

Research output: Chapter in Book/Report/Conference proceedingChapter

Cheng, Q, Niu, R, Sundaresan, A & Varshney, PK 2016, Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks. in Integrated Tracking, Classification, and Sensor Management: Theory and Applications. wiley, pp. 619-660. https://doi.org/10.1002/9781118450550.ch16
Cheng Q, Niu R, Sundaresan A, Varshney PK. Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks. In Integrated Tracking, Classification, and Sensor Management: Theory and Applications. wiley. 2016. p. 619-660 https://doi.org/10.1002/9781118450550.ch16
Cheng, Qi ; Niu, Ruixin ; Sundaresan, Ashok ; Varshney, Pramod Kumar. / Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks. Integrated Tracking, Classification, and Sensor Management: Theory and Applications. wiley, 2016. pp. 619-660
@inbook{62754013f471448691a9773fabed5e02,
title = "Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks",
abstract = "This chapter focuses on distributed detection and decision fusion problems, which involve the design of decision rules at the local sensors and at the fusion center to optimize detection performance, under either a Neyman-Pearson or a Bayesian criterion. It briefly introduces the fundamentals of detection theory. The chapter covers the conventional distributed detection problem with multiple sensors. It presents several important factors that affect the design of distributed detection algorithms. They are the topology of the sensor networks, relation between sensor observations (conditionally independent versus correlated), optimization criteria, and quantization levels. The chapter discusses the distributed detection in wireless sensor networks (WSNs). Assuming local sensor decision rules to be based on simple binary quantization of sensor observations, a novel method for fusion of correlated decisions using copula theory is discussed. Finally, the summary and some challenging issues for distributed detection are presented in the chapter.",
keywords = "Copula theory, Decision fusion, Distributed detection, Multiple sensors, Wireless sensor networks (WSNs)",
author = "Qi Cheng and Ruixin Niu and Ashok Sundaresan and Varshney, {Pramod Kumar}",
year = "2016",
month = "5",
day = "31",
doi = "10.1002/9781118450550.ch16",
language = "English (US)",
isbn = "9780470639054",
pages = "619--660",
booktitle = "Integrated Tracking, Classification, and Sensor Management: Theory and Applications",
publisher = "wiley",

}

TY - CHAP

T1 - Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks

AU - Cheng, Qi

AU - Niu, Ruixin

AU - Sundaresan, Ashok

AU - Varshney, Pramod Kumar

PY - 2016/5/31

Y1 - 2016/5/31

N2 - This chapter focuses on distributed detection and decision fusion problems, which involve the design of decision rules at the local sensors and at the fusion center to optimize detection performance, under either a Neyman-Pearson or a Bayesian criterion. It briefly introduces the fundamentals of detection theory. The chapter covers the conventional distributed detection problem with multiple sensors. It presents several important factors that affect the design of distributed detection algorithms. They are the topology of the sensor networks, relation between sensor observations (conditionally independent versus correlated), optimization criteria, and quantization levels. The chapter discusses the distributed detection in wireless sensor networks (WSNs). Assuming local sensor decision rules to be based on simple binary quantization of sensor observations, a novel method for fusion of correlated decisions using copula theory is discussed. Finally, the summary and some challenging issues for distributed detection are presented in the chapter.

AB - This chapter focuses on distributed detection and decision fusion problems, which involve the design of decision rules at the local sensors and at the fusion center to optimize detection performance, under either a Neyman-Pearson or a Bayesian criterion. It briefly introduces the fundamentals of detection theory. The chapter covers the conventional distributed detection problem with multiple sensors. It presents several important factors that affect the design of distributed detection algorithms. They are the topology of the sensor networks, relation between sensor observations (conditionally independent versus correlated), optimization criteria, and quantization levels. The chapter discusses the distributed detection in wireless sensor networks (WSNs). Assuming local sensor decision rules to be based on simple binary quantization of sensor observations, a novel method for fusion of correlated decisions using copula theory is discussed. Finally, the summary and some challenging issues for distributed detection are presented in the chapter.

KW - Copula theory

KW - Decision fusion

KW - Distributed detection

KW - Multiple sensors

KW - Wireless sensor networks (WSNs)

UR - http://www.scopus.com/inward/record.url?scp=84975915693&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84975915693&partnerID=8YFLogxK

U2 - 10.1002/9781118450550.ch16

DO - 10.1002/9781118450550.ch16

M3 - Chapter

SN - 9780470639054

SP - 619

EP - 660

BT - Integrated Tracking, Classification, and Sensor Management: Theory and Applications

PB - wiley

ER -