RISC

With the trend of vehicles becoming increasingly connected and potentially autonomous, vehicles are being equipped with rich sensing and communication devices. Various vehicular services based on shared real-time sensor data of vehicles from a eet have been proposed to improve the urban eciency, e.g., HD-live map, and trac accident recovery. However, due to the high cost of data uploading (e.g., monthly fees for a cellular network), it would be impractical to make all well-equipped vehicles to upload real-time sensor data constantly. To better utilize these limited uploading resources and achieve an optimal road segment sensing coverage, we present a real-time sensing task scheduling framework, i.e., RISC, for Resource-Constraint modeling for urban sensing by scheduling sensing tasks of commercial vehicles with sensors based on the predictability of vehicles’ mobility patterns. In particular, we utilize the commercial vehicles, including taxicabs, buses, and logistics trucks as mobile sensors to sense urban phenomena, e.g., trac, by using the equipped vehicular sensors, e.g., dash-cam, lidar, automotive radar, etc. We implement RISC on a Chinese city Shenzhen with one-month real-world data from (i) a taxi eet with 14 thousand vehicles; (ii) a bus eet with 13 thousand vehicles; (iii) a truck eet with 4 thousand vehicles. Further, we design an application, i.e., track suspect vehicles (e.g., hit-and-run vehicles), to evaluate the performance of RISC on the urban sensing aspect based on the data from a regular vehicle (i.e., personal car) eet with 11 thousand vehicles. The evaluation results show that compared to the state-of-the-art solutions, we improved sensing coverage (i.e., the number of road segments covered by sensing vehicles) by 10% on average.