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Distributed probabilistic inferencing in sensor networks using variational approximation
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DOI：10.1016/j.jpdc.2007.07.011.png)
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This paper considers the problem of distributed inferencing in a sensor network. It particularly explores the probabilistic inferencing problem in the context of a distributed Boltzmann machine-based framework for monitoring the network. The paper offers a variational mean-field approach to develop communication-efficient local algorithm for variational inferencing in distributed environments (VIDE). It compares the performance of the proposed approximate variational technique with respect to the exact and centralized techniques. It shows that the VIDE offers a much more communication-efficient solution at very little cost in terms of the accuracy. It also offers experimental results in order to substantiate the scalability of the proposed algorithm. (c) 2007 Elsevier Inc. All rights reserved.
Keyword:
probabilistic inferencing
variational approximation
distributed data mining
sensor network
data mining
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