Multi-Task-Oriented UAV Crowd Sensing with Charging Budget Constraint

Jiahui Sun, Guiyun Fan, Haiming Jin, Yiwen Song, Tianyuan Liu, Chenhao Ying, Yuan Luo, Jie Li

Abstract: Nowadays, unmanned aerial vehicles (UAVs) are widely applied in crowd sensing. For UAV-enabled crowd sensing (UAVCS) systems, the sensing outcome and charging cost are two primary concerns. To achieve a satisfactory sensing outcome under the charging budget, we exploit joint moving, sensing, and charging scheduling of UAVs, as they all have critical impacts on such two objectives. However, the dynamically generated sensing targets and the variety of sensing tasks a UAVCS system may face make farsighted scheduling of UAVs rather challenging. To this end, we propose a novel multi-task constrained multi-agent reinforcement learning (MARL) method to help UAVs make distributed moving, sensing, and charging decisions. Specifically, we design a multi-task MARL framework to learn a single generic policy for a large collection of tasks, and propose a primal-dual training algorithm that alternates between improving the overall sensing outcome and reducing each task's constraint violation. Theoretically, we show that our algorithm provably converges, and analyze the optimality gap and constraint violation of the trained policy on unseen tasks. Extensive experiments on an incident dataset in New York City demonstrate that our method outperforms strong baselines in sensing outcome maximization and budget satisfaction, and also generalize well to unseen tasks.