Integrated Trajectory, Association, and Fair Resource Allocation for AoI and Energy-Aware Data Collection in Air-Ground Collaborative Networks

Abstract

In air–ground collaborative networks, low-altitude unmanned aerial vehicle (UAV) swarms and high-altitude platform cooperate to ensure reliable and efficient data collection for ground users (GUs) in dynamic and mission-critical environments. Maintaining data freshness, quantified by the age of information (AoI), while minimizing energy consumption is highly challenging due to UAV mobility, heterogeneous agents, and limited resources. This paper formulates a joint optimization problem that simultaneously minimizes the long-term average AoI and energy consumption of UAVs and GUs under realistic constraints, including quality of service (QoS), queue size, and resource limitations. To tackle this complex mixed discrete–continuous problem, we propose a swarming behavior–integrated multi-agent multi-expert soft actor–critic (MA-MESAC) framework that synergizes swarm intelligence with multi-agent deep reinforcement learning. The actor network employs self-attention to model intra-agent dependencies, while the critic network uses cross-attention to capture inter-agent spatial–temporal correlations. We design a composite actor loss function by integrating the SAC-based policy objective with regularization terms that enforce fair resource allocation and physics-informed swarming behavior, ensuring stable and efficient learning. Moreover, we design a multi-objective reward function that explicitly accounts for stringent mobility, QoS, and resource constraints. Simulation results demonstrate that the proposed framework significantly enhances fairness-aware data freshness, energy efficiency, and convergence compared with existing baselines.