Distributed Target Detection in Sensor Networks Using Scan Statistics

Guerriero, Marco; Willett, Peter; Glaz, Joseph

doi:10.1109/tsp.2009.2017567

Cited by 60 publications

(36 citation statements)

References 37 publications

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“…The full content can be found in [12,16]. In [12], the authors also demonstrate the expression when the X i,j conforms to Poisson distribution.…”

Section: Scan Statisticsmentioning

confidence: 91%

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Scan statistics with local vote for target detection in distributed system

Luo

2017

EURASIP J. Adv. Signal Process.

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Target detection has occupied a pivotal position in distributed system. Scan statistics, as one of the most efficient detection methods, has been applied to a variety of anomaly detection problems and significantly improves the probability of detection. However, scan statistics cannot achieve the expected performance when the noise intensity is strong, or the signal emitted by the target is weak. The local vote algorithm can also achieve higher target detection rate. After the local vote, the counting rule is always adopted for decision fusion. The counting rule does not use the information about the contiguity of sensors but takes all sensors' data into consideration, which makes the result undesirable. In this paper, we propose a scan statistics with local vote (SSLV) method. This method combines scan statistics with local vote decision. Before scan statistics, each sensor executes local vote decision according to the data of its neighbors and its own. By combining the advantages of both, our method can obtain higher detection rate in low signal-to-noise ratio environment than the scan statistics. After the local vote decision, the distribution of sensors which have detected the target becomes more intensive. To make full use of local vote decision, we introduce a variable-step-parameter for the SSLV. It significantly shortens the scan period especially when the target is absent. Analysis and simulations are presented to demonstrate the performance of our method.

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“…The full content can be found in [12,16]. In [12], the authors also demonstrate the expression when the X i,j conforms to Poisson distribution.…”

Section: Scan Statisticsmentioning

confidence: 91%

“…Moreover, Guerriero [12] puts the scan statistics to the signal processing community firstly. The detection is carried out in a mobile fusion center as a mobile agent (MA) which successively counts the number of binary decisions reported by local sensors lying inside its moving field of view.…”

Section: Introductionmentioning

confidence: 99%

Scan statistics with local vote for target detection in distributed system

Luo

2017

EURASIP J. Adv. Signal Process.

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“…These positive decisions are mainly due to the sensing noise. This problem was tackled by using the scan statistic (SS) detector proposed in [10]. The SS test statistic is given by…”

Section: Fusion Rules For Single Cluster Wsnsmentioning

confidence: 99%

“…However, the CR suffers from the problem of spurious detection in large WSN. This problem was tackled by using the scan statistic (SS) detector in [10] and [11]. In SS, a moving FC travels across the ROI and scans the SNs.…”

Section: Introductionmentioning

confidence: 99%

Optimal fusion rule for distributed detection in clustered wireless sensor networks

Aldalahmeh

Ghogho

McLernon

et al. 2016

EURASIP J. Adv. Signal Process.

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We consider distributed detection in a clustered wireless sensor network (WSN) deployed randomly in a large field for the purpose of intrusion detection. The WSN is modeled by a homogeneous Poisson point process. The sensor nodes (SNs) compute local decisions about the intruder's presence and send them to the cluster heads (CHs). A stochastic geometry framework is employed to derive the optimal cluster-based fusion rule (OCR), which is a weighted average of the local decision sum of each cluster. Interestingly, this structure reduces the effect of false alarm on the detection performance. Moreover, a generalized likelihood ratio test (GLRT) for cluster-based fusion (GCR) is developed to handle the case of unknown intruder's parameters. Simulation results show that the OCR performance is close to the Chair-Varshney rule. In fact, the latter benchmark can be reached by forming more clusters in the network without increasing the SN deployment intensity. Simulation results also show that the GCR performs very closely to the OCR when the number of clusters is large enough. The performance is further improved when the SN deployment intensity is increased.

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“…The fusion threshold bounds derived in [9] using Chebyshev's inequality ensure a higher hit rate and lower false alarm rate without requiring a prior probability of target presence. In [10][11][12][13][14], the scan statistics is introduced to improve the detected performances. The performances of different approaches, the Chair-Varshney rule, generalized likelihood ratio test, and Bayesian view, are compared through simulations in [11].…”

Section: Introductionmentioning

confidence: 99%

An algorithm based on logistic regression with data fusion in wireless sensor networks

Liu

Luo

Qin

et al. 2017

J Wireless Com Network

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A decision fusion rule using the total number of detections reported by the local sensors for hypothesis testing and the total number of detections that report "1" to the fusion center (FC) is studied for a wireless sensor network (WSN) with distributed sensors. A logistic regression fusion rule (LRFR) is formulated. We propose the logistic regression fusion algorithm (LRFA), in which we train the coefficients of the LRFR, and then use the LRFR to make a global decision about the presence/absence of the target. Both the fixed and variable numbers of decisions received by the FC are examined. The fusion rule of K out of N and the counting rule are compared with the LRFR. The LRFA does not depend on the signal model and the priori knowledge of the local sensors' detection probabilities and false alarm rate. The numerical simulations are conducted, and the results show that the LRFR improves the performance of the system with low computational complexity.

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Distributed Target Detection in Sensor Networks Using Scan Statistics

Cited by 60 publications

References 37 publications

Scan statistics with local vote for target detection in distributed system

Scan statistics with local vote for target detection in distributed system

Optimal fusion rule for distributed detection in clustered wireless sensor networks

An algorithm based on logistic regression with data fusion in wireless sensor networks

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