Joint Scheduling and Incentive Mechanism for Spatio-Temporal Vehicular Crowd Sensing

Recent years have witnessed the rising popularity of urban vehicular crowd sensing (UVCS) systems that leverage drivers’ mobile devices equipped with on-board sensors for various urban sensing tasks. Because of the importance of ensuring satisfactory spatio-temporal sensing coverage in such UVCS systems, most existing work has focus on designing efficient scheduling mechanisms to maximize the task completion rate under drivers’ traveling constraints. Different from prior work, we propose Hector, a joint trajectory scheduling and incentive mechanism for spatio-temporal UVCS systems, which concentrates on capturing the interactive effects between scheduling and incentive mechanisms. Technically, we first reduce the dimensions of the original scheduling problem by mapping it into an augmented set cover problem with spatio-temporal constraints. Then, based on reverse combinatorial auctions, we design Hector, whose incentive mechanism with the presence of uncertain future trajectory information makes scheduling and compensation decisions in real-time. Specifically, Hector is <italic>truthful</italic>, <italic>individual rational</italic> and <italic>computationally efficient</italic>. Furthermore, the social cost yielded by Hector is close-to-optimal, and the approximation ratio is <inline-formula><tex-math notation="LaTeX">$H_m$</tex-math><alternatives><mml:math><mml:msub><mml:mi>H</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq1-2960328.gif"/></alternatives></inline-formula>. The advantageous properties of Hector are verified by both rigorous theoretical analysis and extensive simulations based on the real world datasets in the Chinese city Shenzhen which consists of 726,000 taxi trajectories.