RT info:eu-repo/semantics/article
T1 WiFiNet: WiFi-based indoor localisation using CNNs
A1 Hernández Parra, Noelia
A1 Parra Alonso, Ignacio
A1 Corrales Sánchez, Héctor
A1 Izquierdo Gonzalo, Rubén
A1 Ballardini, Augusto Luis
A1 Salinas Maldonado, Carlota
A1 García Daza, Iván
K1 Indoor localisation
K1 WiFi
K1 Fingerprinting
K1 Deep Learning
K1 Telecomunicaciones
K1 Telecommunication
AB 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.
PB Elsevier
SN 0957-4174
YR 2021
FD 2021-03-16
LK http://hdl.handle.net/10017/47268
UL http://hdl.handle.net/10017/47268
LA eng
NO Agencia Estatal de Investigación
DS MINDS@UW
RD 16-abr-2024