Distributed Variational Sparse Bayesian Learning for Sensor Networks

Provided by: Institute of Electrical & Electronic Engineers
Topic: Mobility
Format: PDF
In this paper, the authors present a distributed sparse Bayesian Learning (dSBL) regression algorithm. It can be used for collaborative sparse estimation of spatial functions in Wireless Sensor Networks (WSNs). The sensor measurements are modeled as a weighted superposition of basic functions. When kernels are used, the algorithm forms a distributed version of the relevance vector machine. The proposed method is based on a combination of variational inference and loopy belief propagation, where data is only communicated between neighboring nodes without the need for a fusion center. They show that for tree structured networks, under certain parameterization, dSBL coincides with centralized sparse Bayesian Learning (cSBL).

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