Intelligent Task Offloading and Energy Allocation in the UAV-Aided Mobile Edge-Cloud Continuum

Abstract

The arrival of big data and the Internet of Things (IoT) era greatly promotes innovative in-network computing techniques, where the edge-cloud continuum becomes a feasible paradigm in handling multi-dimensional resources such as computing, storage, and communication. In this article, an energy constrained unmanned aerial vehicle (UAV)-aided mobile edge-cloud continuum framework is introduced, where the offloaded tasks from ground IoT devices can be cooperatively executed by UAVs acts as an edge server and cloud server connected to a ground base station (GBS), which can be seen as an access point. Specifically, a UAV is powered by the laser beam transmitted from a GBS, and can further charge IoT devices wirelessly. Here, an interesting task offloading and energy allocation problem is investigated by maximizing the long-term reward subject to executed task size and execution delay, under constraints such as energy causality, task causality, and cache causality. A federated deep reinforcement learning (FDRL) framework is proposed to learn the joint task offloading and energy allocation decision while reducing the training cost and preventing privacy leakage of DRL training. Numerical simulations are conducted to verify the effectiveness of our proposed scheme as compared to three baseline schemes.