RSS-based Monte Carlo localisation for mobile sensor networks

Abstract

Node localisation is a fundamental problem in wireless sensor networks. Many applications require the location information of sensor nodes. Received signal strength (RSS) is a simple and inexpensive approach for localisation purpose. However, the accuracy of RSS measurement is unpredictable owing to the nature of the radio frequency (RF) channel. An RSS-based Monte Carlo localisation scheme is proposed to sequentially estimate the location of mobile nodes, using the log-normal statistical model of RSS measurement. The RSS measurement is treated as the observation model in Monte Carlo method and the mobility feature of nodes as the transition model. Our method is widely applicable because the RSS function is easy to implement on nodes, and the mathematical model for mobile nodes may have non-analytic forms. Simulation results about localisation accuracy and cost show that this scheme is better than other methods.