Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Localization for mobile sensor networks
Proceedings of the 10th annual international conference on Mobile computing and networking
Localization in wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Rendered path: range-free localization in anisotropic sensor networks with holes
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Varying the Sample Number for Monte Carlo Localization in Mobile Sensor Networks
IMSCCS '07 Proceedings of the Second International Multi-Symposiums on Computer and Computational Sciences
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
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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.