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Diffusion-Based EM Algorithm for Distributed Estimation of Gaussian Mixtures in Wireless Sensor Networks
School of Mathematics, Sichuan University, Chengdu 610064, China
Institute for Infocomm Research, 138632, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
* Author to whom correspondence should be addressed.
Received: 25 April 2011; in revised form: 24 May 2011 / Accepted: 10 June 2011 / Published: 14 June 2011
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.
Keywords: diffusion; distributed processing; EM algorithm; consensus; wireless sensor networks
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Cite This Article
MDPI and ACS Style
Weng, Y.; Xiao, W.; Xie, L. Diffusion-Based EM Algorithm for Distributed Estimation of Gaussian Mixtures in Wireless Sensor Networks. Sensors 2011, 11, 6297-6316.
Weng Y, Xiao W, Xie L. Diffusion-Based EM Algorithm for Distributed Estimation of Gaussian Mixtures in Wireless Sensor Networks. Sensors. 2011; 11(6):6297-6316.
Weng, Yang; Xiao, Wendong; Xie, Lihua. 2011. "Diffusion-Based EM Algorithm for Distributed Estimation of Gaussian Mixtures in Wireless Sensor Networks." Sensors 11, no. 6: 6297-6316.