Machine Learning with partially labeled Data for Indoor Outdoor Detection

Abstract : 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.
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Communication dans un congrès
IEEE Annual Consumer Communications & Networking Conference (CCNC), Jan 2019, Las Vegas, United States
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https://hal.archives-ouvertes.fr/hal-02011454
Contributeur : Kamal Singh <>
Soumis le : mardi 12 février 2019 - 14:37:51
Dernière modification le : jeudi 14 février 2019 - 15:18:04

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  • HAL Id : hal-02011454, version 1

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Illyyne Saffar, Marie-Line Alberi-Morel, Kamal Deep Singh, César Viho. Machine Learning with partially labeled Data for Indoor Outdoor Detection. IEEE Annual Consumer Communications & Networking Conference (CCNC), Jan 2019, Las Vegas, United States. 〈hal-02011454〉

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