Minimizing Long-Term Energy Consumption in RIS-Assisted UAV-Enabled MEC Network

In recent years, Unmanned Aerial Vehicles (UAVs) are increasingly becoming flight-based communicative and computing platforms, but the scarcity of communication resources can significantly hinder their performance and scalability. Therefore, this paper proposes a Reconfigurable Intelligent Surface (RIS)-assisted UAV-enabled Mobile Edge Computing (MEC) network, aiming to reduce long-term energy consumption while maintaining system stability by jointly optimizing computing resources, time slot allocation, transmit power, RIS phase angles, and UAV trajectory. By applying the Lyapunov method, we transform the long-term stochastic optimization problem into manageable deterministic online subproblems, and obtain approximate optimal solutions using successive convex approximation, penalty functions, and convex optimization techniques. Simulation results show that compared to the baseline scheme, the proposed scheme approximately reduces energy consumption by 10%, improves system stability by approximately 16%, and maintains computational efficiency.