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Existing localization algorithms for mobile sensor networks are usually based on the Sequential Monte Carlo (SMC) method. They either suffer from low sampling efficiency or require high beacon density to achieve high localization accuracy. Although papers can be found for solving the above problems separately, there is no solution which addresses both issues. In this paper, we propose an energy efficient algorithm, called WMCL, which can achieve both high sampling efficiency and high localization accuracy in various scenarios. In existing algorithms, a technique called bounding-box is used to improve the sampling efficiency by reducing the scope from which the candidate samples are selected. WMCL can further reduce the size of a sensor node's bounding-box by a factor of up to 87 percent and, consequently, improve the sampling efficiency by a factor of up to 95 percent. The improvement in sampling efficiency dramatically reduces the computational cost. Our algorithm uses the estimated position information of sensor nodes to improve localization accuracy. Compared with algorithms adopting similar methods, WMCL can achieve similar localization accuracy with less communication cost and computational cost. Our work has additional advantages. First, most existing SMC-based localization algorithms cannot be used in static sensor networks but WMCL can work well, even without the need of experimentally tuning parameters as required in existing algorithms like MSL*. Second, existing algorithms have low localization accuracy when nodes move very fast. We propose a new algorithm in which WMCL is iteratively executed with different assumptions on nodes' speed. The new algorithm dramatically improves localization accuracy when nodes move very fast. We have evaluated the performance of our algorithm both theoretically and through extensive simulations. We have also validated the performance results of our algorithm by implementing it in real deployed static sensor networks. To - he best of our knowledge, we are the first to implement SMC-based localization algorithms for wireless sensor networks in real environment.