  Different technologies have been proposed to provide indoor localisation:
magnetic field, bluetooth , WiFi, etc. Among them, WiFi is the one with the
highest availability and highest accuracy. This fact allows for an ubiquitous
accurate localisation available for almost any environment and any device.
However, WiFi-based localisation is still an open problem.
  In this article, we propose a new WiFi-based indoor localisation system that
takes advantage of the great ability of Convolutional Neural Networks in
classification problems. Three different approaches were used to achieve this
goal: a custom architecture called WiFiNet designed and trained specifically to
solve this problem and the most popular pre-trained networks using both
transfer learning and feature extraction.
  Results indicate that WiFiNet is as a great approach for indoor localisation
in a medium-sized environment (30 positions and 113 access points) as it
reduces the mean localisation error (33%) and the processing time when compared
with state-of-the-art WiFi indoor localisation algorithms such as SVM.
