WiFiNet: WiFi-based indoor localisation using CNNs

TL;DR

This paper introduces WiFiNet, a CNN-based indoor localisation system that improves accuracy and reduces processing time compared to existing methods, leveraging WiFi signals and deep learning techniques.

Contribution

The paper presents WiFiNet, a novel CNN architecture tailored for WiFi-based indoor localisation, and demonstrates its effectiveness over traditional algorithms.

Findings

WiFiNet reduces mean localisation error by 33%.

WiFiNet outperforms SVM in accuracy and processing time.

The approach is effective in medium-sized environments with 30 positions and 113 access points.

Abstract

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.