Energy-Efficient Ground-Air-Space Vehicular Crowdsensing by Hierarchical Multi-Agent Deep Reinforcement Learning with Diffusion Models

The integrated ground-air-space (GAS) communications system can enhance post-disaster rescue and management efforts when traditional networks fail, by navigating unmanned ground vehicles (UGVs) and unmanned arieal vehicles (UAVs) to collaboratively collect sufficient data from point-of-interests (PoIs) in a timely manner. In this paper, we consider the GAS vehicular crowdsensing (VCS) campaign, where UGVs dispatch and callback UAVs periodically across multiple stops in the workzone, to maximize the total collected amount of data, geographic fairness while minimizing the energy consumption simultaneously. Specifically, we propose an energy-efficient, go-directed hierarchical multi-agent deep reinforcement learning (MADRL) method with discrete diffusion models called "gMADRL-VCS", to optimize the high-level goal-conditioned navigation policies of UGVs, and the low-level long-term sensing strategies of UAVs. Extensive experimental results on two real-world datasets in Roma, Italy, and Hong Kong SAR, China show that gMADRL-VCS outperforms baselines in terms of energy efficiency, data collection ratio, energy consumption, and UAV-UGV cooperation factor.