Distributed variational Bayesian algorithms for Gaussian mixtures in sensor networks

This paper presents a distributed variational Bayesian (DVBA) algorithm for density estimation and clustering in sensor networks. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. The variational approach allows the simultaneous estimate of the component parameters and the model complexity. The DVBA algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is also investigated. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve promising performance of this algorithm.