Proceedings Article10.1109/ICASSP.2008.4518162
Group-ordered SPRT for distributed detection
Yingwei Yao
- 12 May 2008
- pp 2525-2528
8
TL;DR: This work considers the problem of distributed detection in a large wireless sensor network and proposes an adaptive data fusion scheme, group-ordered sequential probability ratio test (GO-SPRT), which achieves significant savings in the cost of data fusion.
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Abstract: We consider the problem of distributed detection in a large wireless sensor network. An adaptive data fusion scheme, group-ordered sequential probability ratio test (GO-SPRT), is proposed. This scheme groups sensors according to the informativeness of their data. Fusion center collects sensor data sequentially, starting from the most informative data and terminates the process when the target performance is reached. To analyze the average sample number, we establish the asymptotic equivalence between GO-SPRT, a multinomial experiment, and a normal experiment. Closed-form approximates are obtained. Our analysis and simulations show that, compared with fixed sample size test and traditional sequential probability ratio test (SPRT), the proposed scheme achieves significant savings in the cost of data fusion.
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References
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Fusion of censored decisions in wireless sensor networks
Ruixiang Jiang,Biao Chen +1 more
TL;DR: The fusion of decisions from censoring sensors transmitted over wireless fading channels is investigated and the knowledge of fading channels, either in the form of instantaneous channel envelopes or the fading statistics, is integrated in the optimum and suboptimum fusion rule design.
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TL;DR: The problem of distributed sequential detection in the presence of communication constraints is considered and an algorithm is developed for optimal bandwidth distribution among sensors under a fixed bandwidth constraint.
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Decentralized sequential detection with a fusion center performing the sequential test
TL;DR: In this article, a decentralized sequential detection problem is considered in which each one of a set of sensors receives a sequence of observations about the hypothesis, and each sensor sends a series of summary messages to the fusion center where a sequential test is carried out to determine the true hypothesis.
Energy-efficient detection in sensor networks
TL;DR: For a given resource constraint, randomization over the choice of measurement and over the choices of when to transmit achieves the best performance (in a Bayesian, Neyman-Pearson, and Ali-Silvey sense).
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