Anonymous crowdsourcing-based WLAN indoor localization

In order to solve the problem of location privacy under big data and improve the user positioning experience, a new concept of anonymous crowdsourcing-based WLAN indoor localization is proposed by employing the Micro-Electro-Mechanical System (MEMS) motion sensors as well as WLAN module in off-the-shelf smartphones. First of all, the crowdsourced motion traces with similar Received Signal Strength(RSS) sequences are assembled into a motion graph. Second, the mobility map is constructed according to traces segmentation and clustering. Third, the pixel template matching is adopted to physically label the pre-constructed mobility map. Finally, the robust Extended Kalman Filter (EKF) is designed to perform localization by matching the newly-collected RSS measurements against the mobility map. The extensive experimental results show that the proposed approach is capable of constructing a physically-labeled mobility map from the sporadically-collected crowdsourced motion traces as well as achieving satisfactory localization accuracy in a cost-efficient manner. Keywords: WLAN localization, Crowdsourcing, Mobility map, Pixel template matching, Robust extended Kalman Filter

Abstract licence: CC BY-NC-ND

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