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WSN node localization algorithm of sparrow search based on elite opposition-based learning and Levy flight

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Abstract

In order to improve localization accuracy of localization algorithm based on received signal strength indication in wireless sensor network (WSN), a WSN node localization algorithm based on sparrow search improved by elite opposition-based learning and Levy flight (SSELF) is proposed. Firstly, the sparrow population is initialized by using the chaotic map to enhance population diversity and accelerate algorithm convergence speed. Secondly, the elite opposition-based learning strategy is applied to increase the diversity of the sparrow population and improve global search capability. Then, the Levy flight strategy is adopted to enhance the ability to jump out of local optimal solution. Finally, the SSELF is used to estimated the positions of unknown nodes.The simulation results demonstrate that the proposed SSELF outperforms the four comparison algorithms in terms of localization accuracy.

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Acknowledgements

This work was supported by Hunan Provincial and Municipal Joint Natural Science Foundation of China (2021JJ50093); National Natural Science Foundation of China (No.11875164); Key Research and Development Projects of Hunan Province (2018SK2055).

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  1. School of Resource and Environment and Safety Engineering, University of South China, Hengyang, 421001, China

    Xiuwu Yu, Wei Peng & Yong Liu

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  1. Xiuwu Yu
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  2. Wei Peng
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Correspondence to Wei Peng.

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Yu, X., Peng, W. & Liu, Y. WSN node localization algorithm of sparrow search based on elite opposition-based learning and Levy flight. Telecommun Syst 84, 521–531 (2023). https://doi.org/10.1007/s11235-023-01062-w

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