Abstract
The localization problem of unknown nodes in wireless sensor networks (WSN) has drawn increasing scholarly attention along together the popularity of meta-heuristic algorithms. To overcome the shortcomings of low accuracy that traditional least square method (LSM) inevitably produces, this paper introduces an improved adaptive genetic algorithm (IAGA) to handle the aforementioned problem and uses a modified evaluation function to reduce the error of distance measurement in a topological structure. The experimental results prove that the IAGA algorithm based on DV-Hop has superior performance in comparison of original DV-Hop and other meta-heuristic algorithms. The conclusion can be drawn that meta-heuristic algorithms have an better superiority over DV-Hop in locating nodes in WSN and the IAGA is more promising than other meta-heuristic algorithms.
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