AoI-Aware Intelligent Platform for Energy and Rate Management in Multi-UAV Multi-RIS System

Recently, unmanned aerial vehicles (UAVs) have demonstrated exemplary performance in various scenarios, such as search and rescue, smart city services, and disaster response applications. UAVs can facilitate wireless power transfer (WPT), resource offloading, and data collection from ground IoT devices. However, employing UAVs for such applications poses several challenges, including limited flight duration, constrained energy resources, and the age of information of the data collected. To address these challenges, we employ a UAV swarm to maximize energy harvesting (EH) and data rates for IoT devices by optimizing UAV paths and integrating reconfigurable intelligent surfaces (RIS) technology. We tackle critical constraints, including UAV energy consumption, flight duration, and data collection deadlines, by formulating an optimization problem to find optimal UAV paths and RIS phase shifts. Given the complexity of the problem, its combinatorial nature, and the challenges of obtaining an optimal solution through conventional optimization methods, we decompose the problem into two sub-problems, employing deep reinforcement learning (DRL) to optimize EH and particle swarm optimization (PSO) to optimize RIS phase shifts. Our extensive simulations show that the proposed solution outperforms competitive algorithms, including Brute-Force-PSO, AC-PSO, and PPO-PSO algorithms, providing a robust solution for modern IoT applications.