Research Article
Task-Aware Joint Computation Offloading for UAV-Enabled Mobile Edge Computing Systems
@INPROCEEDINGS{10.1007/978-3-030-41114-5_9, author={Junshi Hu and Heli Zhang and Xi Li and Hong Ji}, title={Task-Aware Joint Computation Offloading for UAV-Enabled Mobile Edge Computing Systems}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part I}, proceedings_a={CHINACOM}, year={2020}, month={2}, keywords={UAV Offloading selection Air-offloading Ground-offloading Latency Energy MEC}, doi={10.1007/978-3-030-41114-5_9} }- Junshi Hu
Heli Zhang
Xi Li
Hong Ji
Year: 2020
Task-Aware Joint Computation Offloading for UAV-Enabled Mobile Edge Computing Systems
CHINACOM
Springer
DOI: 10.1007/978-3-030-41114-5_9
Abstract
With the emergence of diverse computation-intensive mobile applications (such as virtual reality), demands for data processing from users are rapidly increasing in mobile edge computing (MEC). However, existing mobile edge servers (MES) are susceptible to propagation delays and loss and fail to provide timely and efficient services. Facing this problem, we focus on applying unmanned aerial vehicles (UAVs) equipped with computing resources to provide mobile edge computing offload services for users. UAV as an MES can guarantee low propagation delay and high reliability due to its maneuverability and short-distance line-of-sight communications. In this paper, we study a joint computing offloading problem consideration of user equipments, ground base stations and aerial UAVs. The system provides two offloading methods. The first offloading method is the air-offloading, where a user equipment can offload computing tasks to UAV-enabled MEC servers. The second offloading method is ground-offloading, where a user equipment can offload computing tasks to existing MESs. The task-aware optimization offloading scheme is proposed and it selects local execution or an offloading method based on the latency and energy constraints. Simulation results show that our proposed offloading selection scheme outperforms benchmark schemes. The results demonstrate that the proposed schemes improve quality of service (QoS) and have low task block rate under latency and energy constraints.
