In this article, we evaluate the first experience of computation offloading from drones to real fifth-generation (5G) operator systems, including commercial and private carrier-grade 5G networks. A follow-me drone service was implemented as a representative testbed of remote video analytics. In this application, an image of a person from a drone camera is processed at the edge, and image tracking displacements are translated into positioning commands that are sent back to the drone, so that the drone keeps the camera focused on the person at all times. The application is characterised to identify the processing and communication contributions to service delay. Then, we evaluate the latency of the application in a real non standalone 5G operator network, a standalone carrier-grade 5G private network, and, to compare these results with previous research, a Wi-Fi wireless local area network. We considered both multi-access edge computing (MEC) and cloud offloading scenarios. Onboard computing was also evaluated to assess the trade-offs with task offloading. The results determine the network configurations that are feasible for the follow-me application use case depending on the mobility of the end user, and to what extent MEC is advantageous over a state-of-the-art cloud service.

翻译：本文评估了从无人机向真实第五代（5G）运营商系统（包括商用和私有运营商级5G网络）进行首次计算卸载的实践经验。我们实现了一个跟随式无人机服务作为远程视频分析的典型测试平台。在该应用中，无人机摄像头拍摄的人物图像在边缘端进行处理，图像跟踪位移转换为定位指令并回传至无人机，使无人机始终将摄像头对准该人物。通过特征分析明确了处理和通信对服务延迟的贡献。随后，我们在真实非独立组网5G运营商网络、独立组网运营商级5G私有网络，以及（为与先前研究对比）Wi-Fi无线局域网中评估了应用延迟。研究同时考虑了多接入边缘计算（MEC）和云端卸载场景，并评估了机载计算以权衡任务卸载的得失。实验结果确定了适用于跟随式无人机应用场景的网络配置（取决于终端用户移动性），并揭示了MEC相较于现有最优云服务的优势程度。