Grid-Based Monte Carlo Localization for Mobile Wireless Sensor Networks

Localization is an important requirement for wireless sensor networks (WSNs), but the inclusion of GPS receivers in sensor network nodes is often too expensive. Therefore, many solutions focus on static networks and do not consider mobility. In this paper, we analyze the Monte Carlo location (MCL) algorithm and propose an improved method-grid-based MCL. It applies the mobility of nodes to reduce the sampling area and to build an internal grid to predict the behavior of nodes. We investigate the properties of our technology and analyze its performance. The simulation and analysis show that the proposed grid-based MCL not only reduces localization error, but also improves the sampling efficiency.