论文标题

一项关于航空移动边缘计算的综合调查:挑战,最新和未来的方向

A Comprehensive Survey on Aerial Mobile Edge Computing: Challenges, State-of-the-Art, and Future Directions

论文作者

Song, Zhengyu, Qin, Xintong, Hao, Yuanyuan, Hou, Tianwei, Wang, Jun, Sun, Xin

论文摘要

在物联网(IoT)的视野的驱动下,对物联网用户的计算资源的需求不断增加,以支持各种应用程序。移动边缘计算(MEC)被认为是一种有希望的解决方案,可以解决渴望资源的移动应用程序与资源约束的物联网用户之间的冲突。另一方面,为了在无线网络中提供无处不在的可靠连接性,无人驾驶汽车(UAV)可以通过利用其固有属性(例如按需部署,高巡航高度,高巡航高度,以及在三维(3DDD)中的可控机动性,可以将其作为有效的空中平台来利用。因此,据信以无人机为基础的空中MEC是双赢的解决方案,可在各种环境中促进具有成本效益和节能的沟通和计算服务。在本文中,我们提供了有关支持无人机的空中MEC的全面调查。首先,讨论了对空中MEC的相关优势和研究挑战。然后,我们对最近的研究进展进行了全面的综述,该研究由不同的领域进行了分类,包括无人机轨迹的联合优化,计算卸载和资源分配,无人机部署,任务调度和负载平衡,空中MEC与其他技术之间的相互作用,以及机器学习(ML)驱动的优化。最后,总结了一些重要的研究指示在未来的工作中应得的更多努力。

Driven by the visions of Internet of Things (IoT), there is an ever-increasing demand for computation resources of IoT users to support diverse applications. Mobile edge computing (MEC) has been deemed a promising solution to settle the conflict between the resource-hungry mobile applications and the resource-constrained IoT users. On the other hand, in order to provide ubiquitous and reliable connectivity in wireless networks, unmanned aerial vehicles (UAVs) can be leveraged as efficient aerial platforms by exploiting their inherent attributes, such as the on-demand deployment, high cruising altitude, and controllable maneuverability in three-dimensional (3D) space. Thus, the UAV-enabled aerial MEC is believed as a win-win solution to facilitate cost-effective and energy-saving communication and computation services in various environments. In this paper, we provide a comprehensive survey on the UAV-enabled aerial MEC. Firstly, the related advantages and research challenges for aerial MEC are discussed. Then, we provide a comprehensive review of the recent research advances, which is categorized by different domains, including the joint optimization of UAV trajectory, computation offloading and resource allocation, UAV deployment, task scheduling and load balancing, interplay between aerial MEC and other technologies, as well as the machine-learning (ML)-driven optimization. Finally, some important research directions deserved more efforts in future work are summarized.

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