Multi-Task-Oriented Emergency-Aware UAV Crowdsensing: A Hierarchical Multi-Agent Deep Reinforcement Learning Approach

Integrated sensing and communication (ISAC) has emerged as a transformative paradigm, merging the capabilities of sensing and communication to enhance efficiency and enable advanced applications. Mobile crowdsensing (MCS), as a important example of ISAC, leverages unmanned vehicles such as UAVs to continuously gather and transmit environmental data, supporting critical applications like traffic monitoring, urban congestion management, and accident investigation. In this paper, we focus on multi-task-oriented UAV crowdsensing (UCS), where diverse tasks—such as surveillance and emergency response—each have distinct age-of-information (AoI) requirements. We introduce a novel metric, the “valid task handling index,” to evaluate the performance of handling multiple tasks effectively. Our proposed hierarchical multi-agent deep reinforcement learning (MADRL) framework, DRL-MTUCS, integrates seamlessly with multi-agent actor-critic reinforcement learning methods. It features dynamically weighted queues for UAV goal assignment, enabling efficient management of multiple emergency tasks, and a low-level UAV execution module with a self-balancing intrinsic reward mechanism. This ensures all tasks are completed within their individual AoI constraints. Extensive experiments and trajectory visualizations validate the superior performance and robustness of DRL-MTUCS compared to six baselines across varying conditions, including the number of UAVs, surveillance task AoI thresholds, and emergency task image blur requirements.