title: Diffusion-based EM algorithm for distributed estimation of Gaussian mixtures in wireless sensor networks.
abstract: Distributed estimation of Gaussian mixtures has many applications in wireless sensor network (WSN), and its energy-efficient solution is still challenging. This paper presents a novel diffusion-based EM algorithm for this problem. A diffusion strategy is introduced for acquiring the global statistics in EM algorithm in which each sensor node only needs to communicate its local statistics to its neighboring nodes at each iteration. This improves the existing consensus-based distributed EM algorithm which may need much more communication overhead for consensus, especially in large scale networks. The robustness and scalability of the proposed approach can be achieved by distributed processing in the networks. In addition, we show that the proposed approach can be considered as a stochastic approximation method to find the maximum likelihood estimation for Gaussian mixtures. Simulation results show the efficiency of this approach.
authors.affiliation: School of Mathematics, Sichuan University, Chengdu, China. wengyang@scu.edu.cn
authors.forename: Yang
authors.initials: Y
authors.lastname: Weng
authors.forename: Wendong
authors.initials: W
authors.lastname: Xiao
authors.forename: Lihua
authors.initials: L
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D003195:Computer Communication Networks; D004058:Diffusion; D004574:Electromagnetic Fields; D004867:Equipment Design; D016013:Likelihood Functions; D015233:Models, Statistical; D016011:Normal Distribution; D015203:Reproducibility of Results; D013269:Stochastic Processes; D059015:Wireless Technology