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

• Published in: Neural computing & applications Vol. 35; no. 36; pp. 25291 - 25306
• Main Authors: Peng, Duo, Gao, Yuwei
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2023; Springer Nature B.V
• Subjects: Accuracy; Adaptive algorithms; Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Iterative algorithms; Localization; Mapping; Mold; Probability and Statistics in Computer Science; S.I.: Evolutionary Computation based Methods and Applications for Data Processing; Sensors; Slime; Special Issue on Evolutionary Computation based Methods and Applications for Data Processing; Wireless sensor networks; Anisotropic wireless sensor networks; Chaotic mapping; Self adaptive adjustment; Slime mold optimization algorithm
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-09026-6

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.