Cooperative Offloading and Resource Management for UAV-Enabled Mobile Edge Computing in Power IoT System

Abstract

The lack of the computation services in remote areas motivates power Internet of Things (IoT) to apply unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) technology. However, the computation services will be significantly affected by the UAVs' capacities, and distinct power IoT applications. In this paper, we firstly propose a cooperative UAV-enabled MEC network structure in which the UAVs are able to help other UAVs to execute the computation tasks. Then, a cooperative computation offloading scheme is presented while considering the interference mitigation from UAVs to devices. To maximize the long-term utility of the proposed UAV-enabled MEC network, an optimization problem is formulated to obtain the optimal computation offloading decisions, and resource management policies. Considering the random devices' demands and time-varying communication channels, the problem is further formulated as a semi-Markov process, and the deep reinforcement learning based algorithms are proposed in both of the centralized and distributed UAV-enabled MEC networks. Finally, we evaluate the performance of the proposed DRL-based schemes in the UAV-enabled MEC framework by giving numerical results.