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We investigate the benefits of integrating unmanned aerial vehicles (UAVs)with reconfigurable intelligent surface (RIS) elements to passively relayinformation sampled by Internet of Things devices (IoTDs) to the base station(BS). In order to maintain the freshness of relayed information, anoptimization problem with the objective of minimizing the expected sumAge-of-Information (AoI) is formulated to optimize the altitude of the UAV, thecommunication schedule, and phases-shift of RIS elements. In the absence ofprior knowledge of the activation pattern of the IoTDs, proximal policyoptimization algorithm is developed to solve this mixed-integer non-convexoptimization problem. Numerical results show that our proposed algorithmoutperforms all others in terms of AoI.

Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach