Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.

翻译：边缘计算通过在地理上接近移动设备和物联网设备的位置分布计算、通信和存储资源，促进了网络边缘的低延迟服务。无人机技术的近进展为军事行动、灾难响应或传统地面网络受限或不可用的偏远地区的边缘计算开辟了新机遇。在此类环境中，无人机可作为空中边缘服务器或中继部署，以提供边缘计算服务。这种计算形式被称为无人机赋能边缘计算，具有移动性、视距通信、灵活性、计算能力和成本效益等独特优势。然而，在无人机赋能边缘计算背景下，无人机、边缘服务器和物联网设备上的资源通常非常有限。因此，高效资源管理成为该领域的关键研究挑战。本文从资源管理视角对无人机赋能边缘计算的现有研究进行了综述。我们提出了无人机赋能边缘计算中资源管理的概念架构、不同协作类型、无线通信模型、研究方向、关键技术及性能指标，并给出了资源管理的分类体系。最后，我们识别并讨论了一些开放研究挑战，这些挑战可推动无人机赋能边缘计算资源管理领域的未来研究方向。