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Localization algorithm for anisotropic wireless sensor networks based on the adaptive chaotic slime mold algorithm

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Abstract

Considering the poor localization accuracy of anisotropic localization algorithms, an adaptive chaotic slime mold algorithm called TSMA is proposed to optimize node localization in wireless sensor networks (WSNs). The adaptive chaos mechanism is first applied to the slime mold algorithm (SMA) to initialize the population using the tent map of the chaotic map with the goal of increasing the diversity of the population. Then, global and local search capabilities can be combined by setting an adaptive chaotic oscillation factor during the iterative algorithm optimization. A new localization algorithm combining PDM and TSMA is proposed in the anisotropic localization environment of WSNs. The localization performance of PDM–TSMA is further improved due to the use of anchor node screening and a feasible domain-limiting strategy. According to the simulation results, the proposed algorithm improves the localization performance by 28% and 46% on average in three different environments.

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Acknowledgements

The authors thank The National Natural Science Foundation of China (62265010; 62061024), Supported by Gansu University Innovation Fund Project (No. 2020A-021) for financial support.

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DP was contributed to conception or design of the work, critical revision of the article, drafting the article. YG was contributed to conception or design of the work, data collection, data analysis and interpretation, drafting the article.

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Correspondence to Duo Peng.

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Peng, D., Gao, Y. Localization algorithm for anisotropic wireless sensor networks based on the adaptive chaotic slime mold algorithm. Neural Comput & Applic 35, 25291–25306 (2023). https://doi.org/10.1007/s00521-023-09026-6

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