Age of Information-Aware Data Collection for Multi IRS-Assisted UAV Networks: A Deep Reinforcement Learning Approach

The use of unmanned aerial vehicles (UAVs) for collecting data from sensors has garnered considerable attention in both academic and industrial fields in recent years. To assess the freshness of the information gathered, the age of information (AoI) metric has emerged as an important tool, particularly in time-sensitive applications such as traffic management or monitoring critical issues like fire hazards. This study explores a scenario where a UAV, supported by multiple intelligent reflecting surfaces (IRSs), periodically gathers data from distributed internet of things devices (IoTDs). To minimize the average AoI across all devices, a comprehensive optimization approach is employed. Key elements such as the UAV’s trajectory, device transmission scheduling, timeslot duration, IRS selection factor, and the phase shift matrix of the selected IRS are jointly optimized. However, the problem’s complexity arises from its mixed-integer nature—IRS selection and device scheduling coefficients require integer values—and the strong interdependence of channel conditions with the UAV’s flight path and IRS phase shifts. These challenges make the problem difficult to solve. To address this, the primary optimization problem is framed as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based approach is then adopted to determine the optimal UAV trajectory, device scheduling, and timeslot duration. Additionally, a location-based method is employed for selecting the suitable IRS, while a closed-form solution is used to design the phase shift matrix of the selected IRS. Results evidence improvements in successful transmission and fairness selection of devices when using multiple IRSs and variable timeslots.