Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization (\(\mathbf {P}_{\mathbf {T}}\)), resource allocation at UAV (\(\mathbf {P}_{\mathbf {R}}\)) and offloading decisions at IoT devices (\(\mathbf {P}_{\mathbf {O}}\)) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

最近，无人飞行器（UAV）充当了空中移动边缘计算（MEC）节点，以帮助受电池限制的物联网（IoT）设备减轻计算和数据收集的负担，并延长操作寿命。但是，物联网设备只能向UAV寻求计算或缓存帮助，并且在文献中很少提及UAV的协作卸载服务。此外，物联网设备具有多个相互独立的任务，这使得协作卸载策略设计更具挑战性。因此，我们考虑了用于计算或缓存的多个任务，研究了启用了无人机的MEC网络。考虑到时间敏感型任务的体验质量（QoE）要求，我们旨在通过共同优化轨迹来最大程度地降低IoT设备的总能耗，无人机上的通信和计算资源分配，以及物联网设备上的任务卸载决策。由于此问题具有高度非凸的目标函数和约束，因此我们首先将原始问题分解为三个子问题，称为轨迹优化（\（\ mathbf {P} _ {\ mathbf {T}} \）），UAV的资源分配（\（\ mathbf {P} _ {\ mathbf {R}} \））和物联网设备的卸载决策（\ （\ mathbf {P} _ {\ mathbf {O}} \）），然后提出一种基于块坐标下降法的迭代算法来按顺序处理它们。数值结果表明，协同卸载可以有效降低物联网设备的能耗，同时满足不同类型的卸载服务，并满足物联网设备对时间敏感任务的QoE要求。