Computing Offloading for Energy Conservation in UAV-Assisted Mobile Edge Computing

In the context of large-scale machine-type communication scenarios in 5G, there is a rapid increase in the number and complexity of user equipments' (UE) tasks. However, traditional cloud computing centers far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of unmanned aerial vehicles (UAV), and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the mobility of UAV, the maximum transmission rate of tasks for terminal devices is ensured. Based on this, the IOECA algorithm proposed in this paper is used to obtain the optimal offloading decision variables. The algorithm proposed in this paper effectively reduces the total energy consumption of UE.