Dual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks

In this paper, we consider a mobile wireless sensor network where both sensor nodes and the seeds are moving. We propose and analyze two variations of the Monte Carlo localization (MCL) algorithms, namely: dual MCL and mixture MCL, for mobile sensor networks. We conduct simulation experiments to evaluate the performance of these two algorithms by varying the number of seeds, number of nodes, number of samples, velocity of nodes, and radio pattern degree of irregularity. Results show that both dual MCL and mixture MCL are more accurate than the original MCL algorithm. In terms of the trade off between the computational time and estimated location accuracy, the mixture MCL has a better performance than both dual MCL and the original MCL algorithms.