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

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...

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Bibliographic Details
Published in:Information fusion 2023-03, Vol.91, p.173-186
Main Authors: Talla-Chumpitaz, Reewos, Castillo-Cara, Manuel, Orozco-Barbosa, Luis, García-Castro, Raúl
Format: Article
Language:English
Subjects:
Convolutional Neural Network
Fingerprinting localisation
Image blurring technique
Image generation
Indoor positioning
Metaheuristic algorithm optimisation
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Summary: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. •Converting tidy data into images with dimensionality reduction algorithms for Bluetooth indoor localisation.•Use of blurring painting technique to emulate signal degradation in the image.•Use of a two-branch convolutional neural network for Bluetooth indoor localisation.•Optimisation of the BLE indoor localisation accuracy using metaheuristic algorithms.
ISSN:1566-2535
1872-6305
DOI:10.1016/j.inffus.2022.10.011