Distributed probabilistic inferencing in sensor networks using variational approximation

Sourav Mukherjee, Hillol Kargupta
2008 Journal of Parallel and Distributed Computing  
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
more » ... e 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.
doi:10.1016/j.jpdc.2007.07.011 fatcat:ajajzhmplfdzfnxf54amrqpabq