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

Reewos Talla-Chumpitaz, Manuel Castillo-Cara, Luis Orozco-Barbosa, Raúl García-Castro

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

2 Citas (Scopus)


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.

Idioma originalInglés
Páginas (desde-hasta)173-186
Número de páginas14
PublicaciónInformation Fusion
EstadoPublicada - 1 mar. 2023
Publicado de forma externa


Profundice en los temas de investigación de 'A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation'. En conjunto forman una huella única.

Citar esto