Bayesian Data Fusion for Distributed Target Detection in Sensor Networks

Guerriero, Marco; Svensson, Lennart; Willett, Peter

doi:10.1109/tsp.2010.2046042

Cited by 72 publications

(59 citation statements)

References 13 publications

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“…First, we validate Corollaries 1 and 2 by simulation. Figure 3 shows the results of a Monte Carlo simulation with 10 5 runs to produce the simulated and theoretical distribution of defined in (19) or that of CR in (11). The exact Poisson distribution given in Corollary 1 fits the simulation perfectly for both H 0 and H 1 .…”

Section: Simulation Resultsmentioning

confidence: 92%

“…An intruder at location x 0 ∈ A leaves a signature signal sensed by the SNs. Similar to [8,11], this signature is assumed to decay with distance according to a power law. Thus, the intruder's parameters are given in the vector θ = [ P 0 , x 0 ] T , where P 0 is the intruder's signal power.…”

Section: Sensing and Sensor Network Modelmentioning

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%

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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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Section: Simulation Resultsmentioning

confidence: 92%

Section: Sensing and Sensor Network Modelmentioning

confidence: 99%

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Optimal fusion rule for distributed detection in clustered wireless sensor networks

Aldalahmeh

Ghogho

McLernon

et al. 2016

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…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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“…The BN has been applied in many application areas including computational molecular biology 20 , computer vision 21 , relational databases 19 , text processing 11 , image processing 46 and sensor fusion 5 . In the BN classification problem, a Bayesian network classifier (BNC) from a given set of labeled training instances that are represented by a tuple of attribute variables should be constructed in order to predict the distribution of the class variable.…”

Section: Introductionmentioning

confidence: 99%

Locally Weighted Learning: How and When Does it Work in Bayesian Networks?

Wu¹,

Wu²,

Pan³

et al. 2015

IJCIS

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Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes, which may deteriorate the classification performance. One major approach to mitigate the BNC's primary weakness (the attributes independence assumption) is the locally weighted approach. And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure. However, we do not know whether or how effective it works for improving the performance of the complex BNC. In this paper, we first do a survey on the complex structure models for BNCs and their improvements, then carry out a systematically experimental analysis to investigate the effectiveness of locally weighted method for complex BNCs, e.g., tree-augmented naive Bayes (TAN), averaged one-dependence estimators AODE and hidden naive Bayes (HNB), measured by classification accuracy (ACC) and the area under the ROC curve ranking (AUC). Experiments and comparisons on 36 benchmark data sets collected from University of California, Irvine (UCI) in Weka system demonstrate that locally weighting technologies just slightly outperforms unweighted complex BNCs on ACC and AUC. In other words, although locally weighting could significantly improve the performance of NB (a BNC with simple structure), it could not work well on BNCs with complex structures. This is because the performance improvements of BNCs are attributed to their structures not the locally weighting.

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Bayesian Data Fusion for Distributed Target Detection in Sensor Networks

Cited by 72 publications

References 13 publications

Optimal fusion rule for distributed detection in clustered wireless sensor networks

Optimal fusion rule for distributed detection in clustered wireless sensor networks

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

Locally Weighted Learning: How and When Does it Work in Bayesian Networks?

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