Probability density estimation in sensor networks based on distributed mixture of factor analyzers, mobile agents and stochastic sensor selection

Abstract

This paper considers the problem of distributed probability density estimation of high-dimensional data in sensor networks. In order to describe and analyze high-dimensional observations, a mixture of factor analyzers can be used instead of Gaussian mixture model. Due to high communication costs between sensor nodes in centralized algorithms, use of these algorithms is not affordable. In this paper, a distributed estimation algorithm is presented based on the mixture of factor analyzers, mobile agents and stochastic sensor selection. In the proposed algorithm, at the beginning of each iteration, a mobile agent is assigned to each independent route of the network which consists of several sensor nodes based on a stochastic sensor selection scheme. The mobile agents calculate local sufficient statistics vector in each sensor node and update global sufficient statistics. At the end of each iteration, the parameters of the mixture model are computed by using global sufficient statistics. Convergence analysis of the proposed distributed algorithm is also presented. Finally, the performance of the proposed algorithm is evaluated by using numerical simulations. Simulation results show the promising performance of the proposed distributed algorithm.