A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation

Talla Chumpitaz, Reewos, Castillo Cara, Manuel, Orozco Barbosa, Luis y García Castro, Raúl . (2022) A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation. Information Fusion

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Título A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation
Autor(es) Talla Chumpitaz, Reewos
Castillo Cara, Manuel
Orozco Barbosa, Luis
García Castro, Raúl
Materia(s) Informática
Resumen The growing interest in the use of IoT technologies has generated the development of numerous and diverse applications. Many of the services provided by the applications are based on knowledge of the localisation and profile of the end user. Thus, the present work aims to develop a system for indoor localisation prediction using Bluetooth-based fingerprinting using Convolutional Neural Networks (CNN). For this purpose, a novel technique was developed that simulates the diffusion behaviour of the wireless signal by transforming tidy data into images. For this transformation, we implemented the technique used in painting known as blurring, simulating the diffusion of the signal spectrum. Our proposal also includes the use and a comparative analysis of two dimensional reduction algorithms, PCA and t -SNE. Finally, an evolutionary algorithm was implemented to configure and optimise our solution with the combination of different transmission power levels. The results reported in this work present an accuracy of close to 94%, which clearly shows the great potential of this novel technique in the development of more accurate indoor localisation systems .
Palabras clave Indoor positioning
Fingerprinting localisation
Metaheuristic algorithm optimisation
Image blurring technique
Convolutional Neural Network
Image generation
Editor(es) Elsevier
Fecha 2022-10-17
Formato application/pdf
Identificador bibliuned:557-Jmcastillo-0004
http://e-spacio.uned.es/fez/view/bibliuned:557-Jmcastillo-0004
DOI - identifier https://doi.org/10.1016/j.inffus.2022.10.011
ISSN - identifier 1566-2535 eISSN 1872-6305
Nombre de la revista Information Fusion
Número de Volumen 91
Página inicial 173
Página final 186
Publicado en la Revista Information Fusion
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Information Fusion (2023) 91 p.173-186 , está disponible en línea en el sitio web del editor: Elsevier https://doi.org/10.1016/j.inffus.2022.10.011
Notas adicionales The copyrighted version of this article, first published in Information Fusion (2023) 91 p.173-186, is available online at the publisher's website: Elsevier https://doi.org/10.1016/j.inffus. 2022.10.011

 
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Creado: Wed, 28 Feb 2024, 17:47:41 CET