Mobile-agent-based distributed variational Bayesian algorithm for density estimation in sensor networks

• Published in: IET science, measurement & technology Vol. 11; no. 7; pp. 861 - 870
• Main Authors: Mozaffari, Mohiyeddin, Safarinejadian, Behrouz
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.10.2017
• Subjects: Bayes methods; belief networks; distributed sensor networks; distributed sensors; Gaussian mixture model; Gaussian processes; global sufficient statistics; mixture models; mobile‐agent‐based distributed variational Bayesian algorithm; model order selection; probability density estimation; Research Article; Gaussian processes; distributed sensors; Bayes methods; model order selection; global sufficient statistics; mixture models; belief networks; mobile-agent-based distributed variational Bayesian algorithm; distributed sensor networks; Gaussian mixture model; probability density estimation
• ISSN: 1751-8822; 1751-8830
• DOI: 10.1049/iet-smt.2016.0260

This study considers the problem of probability density estimation and model order selection in distributed sensor networks. For this purpose, a mobile-agent-based distributed variational Bayesian algorithm is proposed. It is assumed that the measurements can be statistically modelled by a common Gaussian mixture model. In the proposed algorithm, the problems of model order selection and probability density estimation will be considered simultaneously using mobile agents and the variational concept. Initially, considering a component number greater than the true one, the variational Bayesian algorithm will be executed in different nodes. In other words, the mobile agents move through different routes in the network and compute the local sufficient statistics. Afterwards, the global sufficient statistics will be updated using these values and finally the parameters of the probability density function will be calculated. This procedure will be repeated until convergence is reached. At this moment, the component whose mixture probability is lower than a threshold value will be removed. The mentioned steps will continue until the true component number is reached. Convergence of the proposed method will also be analytically studied. Finally, the proposed algorithm will be applied to synthetic and also real-world data sets to show its promising performance.