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Node Localization Optimization in WSNS by Using Cat Swarm Optimization Meta-Heuristic

Node Localization Optimization in WSNS by Using Cat Swarm Optimization Meta-Heuristic This paper proposed a new localization algorithm called centroid localization algorithm based on cat swarm optimization algorithm (CLA-CSO). In this algorithm, the Centroid Localization Algorithm is combined with the cat swarm optimization meta-heuristic to improve the localization accuracy in WSNs. CLA-CSO algorithm is a range free localization algorithm which consists of two stages. In the first stage, the CLA algorithm is run and the initial positions of unknown sensor nodes are estimated. In the second stage, the CSO meta-heuristic uses the initial positions found by the CLA to generate the cats of the initial population. Finally, the CSO meta-heuristic is run and the final positions of cat are considered as optimal locations of unknown sensor nodes. Simulation results show that the CLA-CSO algorithm gives good results compared with the basic CLA in terms of localization accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Node Localization Optimization in WSNS by Using Cat Swarm Optimization Meta-Heuristic

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References (14)

Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 2, pp. 177–184. © Allerton Press, Inc., 2023.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411623020104
Publisher site
See Article on Publisher Site

Abstract

This paper proposed a new localization algorithm called centroid localization algorithm based on cat swarm optimization algorithm (CLA-CSO). In this algorithm, the Centroid Localization Algorithm is combined with the cat swarm optimization meta-heuristic to improve the localization accuracy in WSNs. CLA-CSO algorithm is a range free localization algorithm which consists of two stages. In the first stage, the CLA algorithm is run and the initial positions of unknown sensor nodes are estimated. In the second stage, the CSO meta-heuristic uses the initial positions found by the CLA to generate the cats of the initial population. Finally, the CSO meta-heuristic is run and the final positions of cat are considered as optimal locations of unknown sensor nodes. Simulation results show that the CLA-CSO algorithm gives good results compared with the basic CLA in terms of localization accuracy.

Journal

Automatic Control and Computer SciencesSpringer Journals

Published: Apr 1, 2023

Keywords: wireless sensor networks; localization optimization; meta-heuristics; cat swarm optimization meta-heuristic

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