Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing
By Zhenjian Yu, Yanmin Gong, Shimin Gong, Yuanxiong Guo. IEEE Internet of Things Journal, 2020
325 citations recorded by the source corpus.
Mobile edge computing (MEC) is an emerging technology to support resource-intensive yet delay-sensitive applications using small cloud-computing platforms deployed at the mobile network edges. However, the existing MEC techniques are not applicable to the situation where the number of mobile users increases explosively or the network facilities are sparely distributed. In view of this insufficiency, unmanned aerial vehicles (UAVs) have been employed to improve the connectivity of ground Internet of Things (IoT) devices due to their high altitude. This article proposes an innovative UAV-enabled MEC system involving the interactions among IoT devices, UAV, and edge clouds (ECs). The system deploys and operates a UAV properly to facilitate the MEC service provisioning to a set of IoT devices in regions where the existing ECs cannot be accessible to IoT devices due to terrestrial signal blockage or shadowing. The UAV and ECs in the system collaboratively provide MEC services to the IoT devices. For optimal service provisioning in this system, we formulate an optimization problem aiming at minimizing the weighted sum of the service delay of all IoT devices and UAV energy consumption by jointly optimizing UAV position, communication and computing resource allocation, and task splitting decisions. However, the resulting optimization problem is highly nonconvex and thus, difficult to solve optimally. To tackle this problem, we develop an efficient algorithm based on the successive convex approximation to obtain suboptimal solutions. Numerical experiments demonstrate that our proposed collaborative UAV-EC offloading scheme largely outperforms baseline schemes that solely rely on UAV or ECs for MEC in IoT.
Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/c3ecb8b4af89958e85baca2a38dd29963f7697a9
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Citations in the corpus
- Resource Scheduling in Edge Computing: A Survey (2021, background): “It is assumed that the offloaded part of a task is denoted by an offloaded ratio in most existing works [107], [108].”
- UAV assistance paradigm: State-of-the-art in applications and challenges (2020, unknown): “optimization [266], and resource allocation [267].”