Distributed density estimation in sensor networks based on variational approximations

作     者：Safarinejadian, Behrooz Menhaj, Mohammad B. 

作者机构：Shiraz Univ Technol Dept Elect Engn Shiraz 71555313 Iran Amirkabir Univ Technol Dept Elect Engn Tehran 15914 Iran 

出 版 物：《INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE》 (国际系统科学杂志)

年 卷 期：2011年第42卷第9期

页      面：1445-1457页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：sensor networks peer-to-peer networks distributed density estimation mixture of Gaussians variational Bayesian algorithm 

摘      要：This article presents a peer-to-peer (P2P) distributed variational Bayesian (P2PDVB) algorithm for density estimation and clustering in sensor networks. It is assumed that measurements of the nodes can be statistically modelled by a common Gaussian mixture model. The variational approach allows the simultaneous estimate of the component parameters and the model complexity. In this algorithm, each node independently calculates local sufficient statistics first by using local observations. A P2P averaging approach is then used to diffuse local sufficient statistics to neighbours and estimate global sufficient statistics in each node. Finally, each sensor node uses the estimated global sufficient statistics to estimate the model order as well as the parameters of this model. Because the P2P averaging approach only requires that each node communicate with its neighbours, the P2PDVB algorithm is scalable and robust. Diffusion speed and convergence of the proposed algorithm are also studied. Finally, simulated and real data sets are used to verify the remarkable performance of proposed algorithm.