Abstract
The localization of mobile nodes in wireless sensor networks has been formulated as a concave optimization problem. The same has been approached through biologically inspired firefly algorithm (FA) and the artificial bee colony (ABC) algorithm. In the proposed method, a mobile node approximates its distance from multiple anchor nodes. The distance and the coordinates of the anchors are the parameters used by FA and ABC algorithms for the accurate estimation of the location by minimizing the suitably defined localization error. The localization method used here is iterative, and it works in a distributed fashion. A comparison of the performances of FA and ABC algorithms in terms of localization accuracy and computation time has been presented. FA exhibits higher accuracy of localization, while ABC is quicker.
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Kulkarni, V.R., Desai, V. (2019). Computational Intelligence for Localization of Mobile Wireless Sensor Networks. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_34
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DOI: https://doi.org/10.1007/978-981-13-1135-2_34
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