Distributed Data Clustering Using Mobile Agents and EM Algorithm

In this paper, mobile agents are used for density estimation and data clustering in sensor networks. It has been assumed that sensor measurements can be statistically modeled by a common Gaussian mixture model. Here, a new data clustering method will be proposed for sensor networks in which some agents will move in different routes and collect the local necessary sufficient statistics by executing expectation maximization (EM) algorithm locally in each node. Then, the mobile agents will combine the local sufficient statistics in a fusion center and calculate a global sufficient statistics vector. This process will be repeated until convergence is reached. This algorithm not only executes the EM algorithm in a distributed manner but also reduces the number of iterations of the EM algorithm and increases its convergence rate. 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 method will be applied to synthetic and real data sets in order to show its promising performance.