Monte Carlo Localization of Mobile Sensor Networks Using the Position Information of Neighbor Nodes

Localization is a fundamental problem in wireless sensor networks. Most existing localization algorithm is designed for static sensor networks. There are a few localization methods for mobile sensor networks. However, Sequential Monte Carlo method (SMC) has been used in localization of mobile sensor networks recently. In this paper, we propose a localization algorithm based on SMC which can improve the location accuracy. A new method is used for sample generation. In that, samples distributes uniformly over the area from which samples are drawn instead of random generation of samples in that area. This can reduces the number of required samples; besides, this new sample generation method enables the algorithm to estimate the maximum location error of each node more accurately. Our algorithm also uses the location estimation of non-anchor neighbor nodes more efficiently than other algorithms. This can improve the localization estimation accuracy highly.

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