A Mobile Agent Based Distributed Variational Bayesian Algorithm for Flow and Speed Estimation in a Traffic System


IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.12   pp.2934-2942
Publication Date: 2016/12/01
Publicized: 2016/08/24
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016PAP0002
Type of Manuscript: Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
Category: Sensor network
density estimation,  Gaussian mixture model,  mobile agent,  sensor networks,  variational Bayesian,  

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This paper provides a mobile agent based distributed variational Bayesian (MABDVB) algorithm for density estimation in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. In the proposed algorithm, mobile agents move through the routes of the network and compute the local sufficient statistics using local measurements. Afterwards, the global sufficient statistics will be updated using these local sufficient statistics. This procedure will be repeated until convergence is reached. Consequently, using this global sufficient statistics the parameters of the density function will be approximated. Convergence of the proposed method will be also analytically studied, and it will be shown that the estimated parameters will eventually converge to their true values. Finally, the proposed algorithm will be applied to one-dimensional and two dimensional data sets to show its promising performance.