论文标题
众包智能手机感知地铁火车的本地化
Crowdsourced Smartphone Sensing for Localization in Metro Trains
论文作者
论文摘要
基于传统的指纹本地化技术主要依赖于基础架构支持，例如RFID，Wi-Fi或GPS。他们通过战争驱动整个空间来运作，这既耗时又是劳动力密集的。在本文中，我们提出了MLOC，这是一种新型的无基础架构本地化系统，可在地铁线中找到移动用户。它不依赖任何Wi-Fi基础架构，也不需要战争地铁线。利用众包，我们在智能手机上收集加速度计，磁力计和晴雨表读数，并分析这些传感器数据以提取图案。通过高级数据操纵技术，我们为整个地铁线构建了模式图，然后可以将其用于本地化。我们进行现场研究以证明M-LOC的准确性，可伸缩性和鲁棒性。我们在3个具有55个站点的3个地铁线的现场研究结果表明，在旅行3个站点时，M-LOC的准确度为93％，在5个车站旅行时98％的精度为98％。
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.
