RTS Assisted Mobile Localization: Mitigating Jigsaw Puzzle Problem of Fingerprint Space with Extra Mile

With the development of Location Based Services (LBSs), both academic researchers and industries have paid more attention to GPS-less mobile localization on mobile phones. The majority of the existing localization approaches have utilized signal-fingerprint as a metric for location determinations. However, one of the most challenging issues is the problem of uncertain fingerprints for building the fingerprint map, termed as the jigsaw puzzle problem. In this paper, for more accurate fingerprints of the mobile localization, we investigate the changes of Received Signal Strength Indication (RSSI) from the connected cell-towers over time along the mobile users' trajectories, termed as RSSI Time Series (RTS). Thus, we propose an RTS Assisted Localization System (RALS), which is a GPS-less outdoor mobile localization system. For localization, an RTS map is built on the back-end server, which consists of RTS harvested from the mobile phones, by the way of crowd sensing. The jigsaw puzzle problem slows down the map construction solely by the regular unintentional users with short-distance trajectories, and affects its efficiency. To speed up the map construction, we propose employing a few advanced intentional users with additional long-distance trajectories, at a higher cost than the regular user, this is called extra mile. Our extensional experiments verify the effectiveness of our localization system.