Abstract

A localization method for mobile sensor networks is presented in this paper. In order to reduce localization errors, the artificial bee colony (ABC) algorithm is used to refine the estimated coordinates. The fundamental concept of the ABC algorithm is to find better feasible solution (food source) from food sources in the specific space. Using the idea, this paper study integrates the ABC algorithm, boundary error correction and the improved Monte Carlo localization (IMCL) algorithm to further improve the accuracy of positioning. The IMCL estimates the node coordinates and then the ABC refines the location. From the simulation results, it is seen that the proposed algorithm can effectively reduce the localization error. Comparing to the other Monte Carlo based localization algorithm MCL, MCB, and IMCL, the resulted average estimation error is at most 0.44 times of communication range. It outperforms other three algorithms.

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