Edge Computing

This paper focuses on energy-efficient scheduling for multiple UAV-assisted MEC. It optimizes UAV trajectories, energy renewal, and application placement to maximize long-term energy efficiency. The approach uses a triple learner based reinforcement learning method and demonstrates superiority through simulations.

This paper focuses on an energy efficient scheduling problem for multiple unmanned aerial vehicles (UAVs) that assist in mobile edge computing. The goal is to maximize the long-term energy efficiency of the UAVs by optimizing their trajectory planning, energy renewal, and application placement. The paper proposes a triple learner based reinforcement learning approach to address the problem, which includes a trajectory learner, an energy learner, and an application learner. Simulations show that the proposed solution outperforms existing approaches.