Abstract-Traditional fingerprint based localization techniques mainly rely on infrastructure support such as RFID, Wi-Fi or GPS. They operate by war-driving the entire space which is both time-consuming and labor-intensive. In this paper, we present MLoc, a novel infrastructure-free localization system to locate mobile users in a metro line. It does not rely on any Wi-Fi infrastructure, and does not need to war-drive the metro line. Leveraging crowdsourcing, we collect accelerometer, magnetometer and barometer readings on smartphones, and analyze these sensor data to extract patterns. Through advanced data manipulating techniques, we build the pattern map for the entire metro line, which can then be used for localization. We conduct field studies to demonstrate the accuracy, scalability, and robustness of M-Loc. The results of our field studies in 3 metro lines with 55 stations show that M-Loc achieves an accuracy of 93% when travelling 3 stations, 98% when travelling 5 stations.
Chen et al. REF used MCS to build the pattern map of the metro line, which can then be used for localization.
Crowdsourced smartphone sensing for localization in metro trains
Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014
Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014
Computer Science