A Distributed Variational Bayesian Algorithm for Density Estimation in Sensor Networks


IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.5   pp.1037-1048
Publication Date: 2009/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1037
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Computation and Computational Models
sensor networks,  clustering,  density estimation,  mixture of Gaussians,  variational approximations.,  

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In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. This paper develops a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This 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 then investigated. Finally, to verify the performance of DVBA, we perform several simulations of sensor networks. Simulation results are very promising.