A Novel Multi-Agent RL Approach in Priority-Aware UAV-Assisted Networks for AoI and Energy Consumption Minimization

Unmanned aerial vehicles (UAVs) have been widely employed for emergency communications in Internet of Things (IoT) networks. The temporal freshness of data in disaster rescue, represented by the Age of Information (AoI), holds critical significance. Due to UAVs' limited energy capacity, it is crucial to optimize their energy consumption while ensuring effective assistance in the IoT system's fresh data collection tasks. Therefore, our work sets out to minimize the AoI and energy consumption to meet time-sensitive and priority-aware demands in resource-constrained environments. We formulate a multi-objective optimization problem by jointly optimizing UAV flight trajectory, IoT device transmitting power, and UAV radio frequency (RF) power, with constraints such as IoT devices' priority levels and UAVs' locations. This problem is modeled as a Markov Decision Process (MDP), and we propose a novel reinforcement learning (RL)-based method, termed the MultiAgent Data Collection Energy Transmitting Scheme (MADCET), to solve it. Extensive simulations demonstrate that the proposed scheme significantly outperforms benchmarks by reducing AoI, satisfying priority demands, minimizing energy consumption, and achieving good convergence.