Comparative study of supervised learning and metaheuristic algorithms for the development of bluetooth-based indoor localization mechanisms

Jesus Lovon-Melgarejo, Manuel Castillo-Cara, Oscar Huarcaya-Canal, Luis Orozco-Barbosa, Ismael Garcia-Varea

Producción científica: Contribución a una revistaArtículo (Contribución a Revista)revisión exhaustiva

22 Citas (Scopus)

Resumen

The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.

Idioma originalInglés
Número de artículo8642816
Páginas (desde-hasta)26123-26135
Número de páginas13
PublicaciónIEEE Access
Volumen7
DOI
EstadoPublicada - 2019
Publicado de forma externa

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