Frustratingly Simplified Deployment in WLAN Localization by Learning from Route Annotation

Recently wireless LAN (WLAN) localization systems are gaining popularity in pervasive computing, machine learning and sensor networks communities, especially indoor scenarios where GPS coverage is limited. To accurately predict location, a large amount of fingerprints composed of received signal strength values is necessary. Moreover, standard supervised or semi-supervised approaches also require location information to each fingerprint, where annotation work is rather tedious and time consuming. To reduce the efforts and time required to build calibration data, we present a novel calibration methodology "route-annotation” and a self-training algorithm for learning from route information effectively. On the proposed calibration methodology, an annotator walks around while measuring fingerprints, then occasionally stops to annotate fingerprints with route from previous location to current location. This calibration reduces work time even compared to partially annotation, while routes have richer information for learning. The proposed learning algorithm comprises following two iterative steps: 1) inferring locations of each fingerprint under route constraints and 2) updating parameters. Experimental results on real-world datasets demonstrate learning from route-annotated data is comparable to state-of-the-art supervised and semi-supervised approaches trained with large amount of calibration data.