auSense: Collaborative Airspace Sensing by Commercial Airplanes and Unmanned Aerial Vehicles

In recent years, a large number of commercial airplanes equipped with sensors have been contributing to collecting the atmospheric composition and meteorological data. Based on a worldwide commercial airplane dataset, we identify an inherent limitation of such an airplane-based sensing system as airplanes fly through airspace with spatiotemporal dynamic. As a result, the sensed data is coarse-grained and incomplete. Moreover, naive methods such as installing sensors on more airplanes, cannot effectively address the issue. In this paper, we propose a collaborative airspace sensing strategy auSense to improve the sensing performance. In auSense, UAVs (Unmanned Aerial Vehicles) are employed to sense the airspace when the airspace is not sensed by airplanes. Particularly, auSense leverages the existing worldwide commercial airplane tracking infrastructure and fine-grained trajectory data to estimate whether and when one airspace will be sensed by airplanes. Then auSense schedules the UAV paths to improve the sensing performance. We implement auSense in different airspaces based on a 1-month airplane trajectory data. The experiments demonstrate the superior performance of our data-driven UAV and commercial airplane collaborative strategy over the ground truth and other benchmarks.