Performance Analysis of Distributed Detection in a Random Sensor Field

Niu, Ruixin; Varshney, Pramod K.

doi:10.1109/tsp.2007.906770

Cited by 142 publications

(75 citation statements)

References 19 publications

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“…For a WSN with randomly deployed sensors, Niu & Varshney [22,23] investigate the performance of such a counting rule, where the fusion centre employs the total number of detections reported by local sensors for hypothesis testing.…”

Section: (C) Robust and Composite Distributed Detectionmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

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107

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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Section: (C) Robust and Composite Distributed Detectionmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

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107

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“…In [12] the counting decision fusion rule, which is based on the total number of local detections from local sensors, was proposed: according to [11] at the fusion center the system-level decision is made by first counting the number of detections made by local sensors and then comparing it with a threshold T :…”

Section: The Counting Rule Testmentioning

confidence: 99%

“…In [11] a fusion rule that uses the total number of detections ("1"s) transmitted from the local sensors as the test statistic was proposed for the case where the total number of sensors in the SN was known. In [12] the authors studied the case of random deployment of sensors within the SN: even the number of sensors was unknown and was modeled as a random variable, and via this prior information the system level decision at the fusion center compared number of detections made by the local sensors to a threshold.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Target Detection in Sensor Networks Using Scan Statistics

Guerriero

Willett

Glaz

2009

IEEE Trans. Signal Process.

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We introduce a sequential procedure to detect a target with distributed sensors in a two dimensional region. The detection is carried out in a mobile fusion center which successively counts the number of binary decisions reported by local sensors lying inside its moving field of view. This is a two-dimensional scan statistic -an emerging tool from the statistics field that has been applied to a variety of anomaly detection problems such as of epidemics or computer intrusion, but that seems to be unfamiliar to the signal processing community. We show that an optimal size of the field of view exists. We compare the sequential two-dimensional scan statistic test and two other tests. Results for system level detection are presented.

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“…In a typical distributed detection problem [18]- [20], each sensor individually forms its own discrete messages based on its local measurement and then reports to a fusion center, and there is, in general, no direct communication among the sensors. In certain models [21], a sensor may indirectly obtain information about other sensors, but this is achieved by feedback from a common fusion center.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios

Huang

2010

IEEE Trans. Veh. Technol.

211

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Abstract-In cognitive radio (CR) networks, secondary users can cooperatively sense the spectrum to detect the presence of primary users. In this paper, we propose a fully distributed and scalable cooperative spectrum-sensing scheme based on recent advances in consensus algorithms. In the proposed scheme, the secondary users can maintain coordination based on only local information exchange without a centralized common receiver. Unlike most of the existing decision rules, such as the OR-rule or the 1-out-of-N rule, we use the consensus of secondary users to make the final decision. Simulation results show that the proposed consensus scheme can have significant lower missing detection probabilities and false alarm probabilities in CR networks. It is also demonstrated that the proposed scheme not only has proven sensitivity in detecting the primary user's presence but also has robustness in choosing a desirable decision threshold.

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Performance Analysis of Distributed Detection in a Random Sensor Field

Cited by 142 publications

References 19 publications

Distributed inference in wireless sensor networks

Distributed inference in wireless sensor networks

Distributed Target Detection in Sensor Networks Using Scan Statistics

A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios

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