LAUSR

The prosperity of Internet of Things (IoT) brings forth the deployment of large-scale sensing systems such as smart cities. To enable the collaboration tasks among distributed devices, time synchronization is crucial. However, due to the long-range and device heterogeneity, accurate time synchronization for a large-scale IoT network is challenging. Existing GPS or NTP solutions either require an outdoor environment or only have low and unstable accuracy. In this paper, we propose AirSync, a novel synchronization method that leverages the widely existed aircraft signals, ADS-B, to synchronize large-scale IoT networks with nodes even in indoor environments. But ADS-B messages have no time stamp and cannot provide a reference time. We leverage the continuity of aircraft movements to estimate the aircraft traveling time. Then devices that observe common aircraft moving segments can calculate their time offset. To obtain the time skew, we propose a combined aircraft linear regression method. We also design a transitive synchronization for devices that cannot observe common aircraft. Besides, we also design a duty-cycled ADS-B message collection method for resource-limited IoT devices. We implement a prototype of AirSync and evaluate its performance in various real-world environments. The results show that AirSync can obtain the sub-ms accuracy.