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

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 ini...

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Veröffentlicht in:Neural computing & applications Jg. 35; H. 36; S. 25291 - 25306
Hauptverfasser: Peng, Duo, Gao, Yuwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.12.2023
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09026-6