A Coverage-aware Task Allocation Method for UAV-assisted Mobile Crowd Sensing

Mobile crowd sensing campaigns usually require spatial-temporal sensing coverage. However, there are numerous circumstances where human participants are unable or unwilling to reach the target regions, such as traffic jams or accidents. Furthermore, human participants may not prefer to be interrupted frequently or leak private information too much. All of these are salient challenges that affect task achievement. To solve these problems, we propose in this paper a method that employs human participants and UAVs to sense data together. For human participants, the proposed method takes their sensing and reporting tolerance into consideration, which predicts their trajectories based on the sole clue of their start and destination locations and allocates tasks following the Pareto optimal theory. In the meantime, the UAVs sense data efficiently from areas that are not sensed by human participants or other UAVs. We evaluate the proposed method using simulation and a small-scale practical experiment. Extensive experiments are used to verify the method's efficiency.