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Wireless Personal Communications

Erschienen in:

18.03.2023

Optimized Localization Learning Algorithm for Indoor and Outdoor Localization System in WSNs

Erschienen in: Wireless Personal Communications | Ausgabe 1/2023

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Abstract

The localization problem in wireless sensor networks (WSN) has recently received justifiable interest from researchers. Various optimization/learning algorithms are used to determine the accurate node localization. The main aim of this paper is to develop a novel Optimized Localization Learning Algorithm (OLLA) and analyze it with well-noticed localization-based learning algorithms, namely Approximate Point in Triangle (APIT), Localization Algorithm using Expected hop Progress (LAEP), Randomized Approximate Nearest Neighbors (RANN) and Standard-based Particle Swarm Optimization (SPSO). The performance is investigated based on indoor and outdoor scenarios, on specific parameters: absolute localization error, relative localization error, root mean square error, and probability distribution on various anchor nodes. Simulation results show that the proposed learning algorithm OLLA performs well in both scenarios. Besides this, numerous localization-based learning algorithms are discussed, and comparative analyses of well-known learning algorithms with merits and limitations are also presented.

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Titel: Optimized Localization Learning Algorithm for Indoor and Outdoor Localization System in WSNs
Publikationsdatum: 18.03.2023
Erschienen in: Wireless Personal Communications / Ausgabe 1/2023
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-023-10304-8

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