Baidya Tanmay, Nabi Ahmadun, Moh Sangman
Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea.
Sensors (Basel). 2024 Mar 13;24(6):1837. doi: 10.3390/s24061837.
Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.
最近，无人机（UAV）与边缘计算的集成已成为一种很有前景的范式，可为偏远、受灾和海上地区的物联网（IoT）应用提供计算支持。在无人机辅助的边缘计算中，卸载决策在优化整体系统性能方面起着核心作用。然而，轨迹直接影响卸载决策。一般来说，物联网设备利用地面在无人机辅助的边缘服务器上卸载计算密集型任务。无人机根据任务生成率规划其轨迹。因此，研究人员正试图优化卸载决策以及轨迹，并且正在进行大量研究以确定轨迹对卸载决策的影响。在本次综述中，我们通过关注设计概念、操作特性和突出特点来回顾现有的轨迹感知卸载决策技术。此外，还根据设计原则和操作特性对它们进行了比较。讨论了开放问题和研究挑战以及未来方向。
