OTMCL: Orientation tracking-based Monte Carlo localization for mobile sensor networks

In this paper we consider a probabilistic approach to the problem of localization in wireless sensor networks (WSNs) consisting of mobile nodes, and propose a distributed algorithm that help unknown nodes determine their positions. Our proposal is a range-free positioning system based on heading data, which can be provided by a variety of orientation-tracking sensors of different precisions, applied to the Monte Carlo sampling technique. Simulation results indicate that the intermix of orientation data with sampling and filtering augments the overall accuracy of the positioning system, while maintaining a low communication cost.