Deep learning indoor localization from tidy data into synthetic images

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 has been developed that simulates the diffusion behaviour of the wireless signal by transforming tidy data into synthetic images. For this transformation, we have used the technique used in painting known as blurring technique, 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 has been implemented to configure and optimize our solution with the combination of different transmission power levels. The results reported in this work are close to 94% of accuracy photometry, which clearly shows the great potential of this novel technique to the development of more accurate indoor  localisation systems.