Monte Carlo Localization for path-based mobility in mobile wireless sensor networks

Localization is a mandatory requirement in Wireless Sensor Networks (WSNs). Many solutions focus on static networks and do not account for mobility. In this paper we present an extension of the Monte Carlo Localization method, which exploits the mobility behavior of certain applications in WSNs to reduce the localization error. Our approach called PO-MCL maps regular traveled paths of nodes to an internal grid, which is used to predict the node's behavior in the absence of seed nodes. We show by excessive simulations that our approach is able to reduce the localization error by up to 50 %. 1