Abstract
Wireless sensor networks that are composed of a finite number of spatially distributed autonomous sensors are widely used in different areas with many potential applications. However, in order to be implemented efficiently, especially in poorly accessible terrains, localization challenge should be addressed. Localization refers to determining the unknown target nodes positions by using information about location of anchor nodes, based on different measurements, such as the time and the angle of arrival, time difference of arrival, and so on. This task is considered to be NP-hard by its nature and cannot be addressed with traditional deterministic approaches. In this research we have proposed the improved implementation of swarm intelligence approach, whale optimization algorithm, to address localization challenge in wireless sensor networks. Observed drawbacks of original whale optimization algorithm are overcome in enhanced implementation by incorporating quasi-reflected-based learning algorithm. Proposed metaheuristics is tested using the same network topology and experimental conditions as other advanced metaheuristics which results are published in the most recent computer science literature. Based on simulation results, devised algorithm manages to establish lower localization error than the basic whale optimization algorithm, as well as other outstanding metaheuristics.
Published Version
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