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 Kahnan 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.