DelFin: A Deep Learning Based CSI Fingerprinting Indoor Localization in IoT Context

Brieuc Berruet (1) , Oumaya Baala (1) , Alexandre Caminada (1) , Valery Guillet (2)

1 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
2 Orange Labs

Many applications in Internet of Things (IoT) require an ubiquitous localization to provide their services. Whereas the global navigation satellite systems are mainly used in outdoor environment, multiple solutions based on mobile sensors or wireless communication infrastructures exist for indoor localization. One of them is the fingerprinting approach which consists in collecting the signals at known locations in a studied area and estimating the locations of new incoming signals thanks to the collected database. This approach interests many researches due to its connection with machine learning concepts. In this paper we propose to implement a deep learning architecture for a fingerprinting localization based on Wi-Fi channel frequency responses in IoT context. Our solution, DelFin reduces the median and 90-th percentile localization errors up to 50% and 47% respectively compared to other fingerprinting methods. DelFin has been tested with different spatial distributions of training locations in the studied area and still performed the best results.

Domaines: Technologies Émergeantes [cs.ET]; Calcul parallèle, distribué et partagé [cs.DC]; Génie logiciel [cs.SE]; Informatique ubiquitaire; Cryptographie et sécurité [cs.CR]; Système multi-agents [cs.MA]; Modélisation et simulation

Brieuc Berruet, Oumaya Baala, Alexandre Caminada, Valery Guillet. DelFin: A Deep Learning Based CSI Fingerprinting Indoor Localization in IoT Context. International Conference on Indoor Positioning and Indoor Navigation, Sep 2018, Nantes, France. ⟨hal-02182829⟩