Machine Learning with partially labeled Data for Indoor Outdoor Detection

Illyyne Saffar (1, 2) , Marie-Line Alberi-Morel (1) , Kamal Deep Singh (3, 4) , César Viho (2)

1 Nokia Bell Labs [Nozay]
2 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
3 LHC - Laboratoire Hubert Curien
4 UJM - Université Jean Monnet - Saint-Étienne

This paper demonstrates the feasibility of an hybrid/semi-supervised classification method for detecting the environment of an active mobile phone, based on both labeled and unlabeled cellular radio data. Precisely, we provide answers to the following question: what is the environment of the mobile user when it is/was experiencing a mobile service/application: indoor or outdoor? Implementing this method within the mobile network is interesting for mobile operators since it has low complexity, is less human intrusive (minimal intervention of mobile users) and more accurate. The semi-supervised classification algorithm learns to identify the environment using large and real collected 3GPP signals measurements. As compared to existing work, in addition to existing parameters used for classification, we propose to also use a radio metric called Timing Advance. It is computed within the mobile network. We empirically validate the innovative semi-supervised algorithm using new real-time radio measurements, with partial ground truth information, gathered daily, weekly, monthly, from indoor and outdoor locations and from multiple typical and diversified environments crossed by mobile users. The study confirms the effectiveness of the proposed scheme compared to the existing supervised classification methods including SVM and Deep Learning.

Mots clés: crowdsourcing; Indoor Outdoor Detection; 3GPP radio measurement; Machine Learning; real user activity; User Positioning; Environment classification

Domaines: Intelligence artificielle [cs.AI]; Réseaux et télécommunications [cs.NI]

Illyyne Saffar, Marie-Line Alberi-Morel, Kamal Deep Singh, César Viho. Machine Learning with partially labeled Data for Indoor Outdoor Detection. CCNC 2019 - 16th IEEE Consumer Communications & Networking Conference, Jan 2019, Las Vegas, United States. pp.1-7, ⟨10.1109/CCNC.2019.8651736⟩. ⟨hal-02011454⟩