Conference paper

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.

This paper focuses on the joint optimization of sensor activation and mobile charging scheduling in industrial wireless rechargeable sensor networks (IWRSNs). The goal is to minimize energy consumption while meeting task requirements, sensor charging deadlines, and the mobile charger vehicle’s energy capacity. The proposed solution combines deep reinforcement learning and a marginal product-based approximation algorithm. Simulation results show the superiority of this solution compared to other methods.