Multi-Modal Trajectory Planning for Emergency-Oriented Air-Ground Collaborative Sensing and Communication

To obtain real-time situational awareness of the world, air-ground collaborative sensing and communication provide a promising solution to form a pervasive cognitive communications and networking system. However, existing schemes struggle to cope with emergencies where ground base stations and Internet of Things devices are temporarily out-of-service. Motivated by this, we envision a novel emergency-oriented air-ground collaborative sensing and communication network where multi-modal cognitive entities (i.e., static/dynamic ground/aerial nodes) cooperatively collect data from IoT devices and simultaneously perform sensing functionality. In such a novel network, an optimization for joint trajectory planning and resource allocation is established to minimize both data transmission task delay and sensing task delay under the constraints of boundary, moving distance, accessible region, and energy consumption for network nodes. To tackle the problem, we propose a transfer learning-based deep reinforcement learning (DRL) framework where three advanced DRL algorithms are included. Such a framework can rapidly adapt to potentially updated environments by facilitating knowledge transfer across tasks for emergency rescue activities. The proposed framework outperforms three state-of-the-art baselines. Moreover, the newly introduced auxiliary cognitive entities facilitate the improvement of sensing and communication functionalities, and the proposed transfer learning-based scheme boosts convergence in fast-changing environments.