Wireless sensor network cluster locations: A probabilistic inference approach

A wireless sensor network refers to a spatially distributed autonomous sensor system to monitor physical or environmental conditions and to cooperatively pass their data through the network to a main location. With many industrial and consumer applications, the study of of wireless sensor network has attracted much research attention recently. In this paper, we study the sensor network cluster location problem. We hope to divide the sensors into different clusters according to pairwise affinities and select a number of sensors to act as the headers to serve neighbouring sensors in the same cluster. The detection of such optimal sensor headers is an NP-hard problem and approximate solutions have to sought if tractability is to be ensured. We propose a fast solution based on the recent advances in probabilistic inference. In our experimental studies, we have verified the potential of the solution for large-scale sensor networks.