Version 1
: Received: 19 December 2023 / Approved: 20 December 2023 / Online: 20 December 2023 (12:20:37 CET)
How to cite:
Yin, R.; Tian, H. Computing Offloading for Energy Conservation in UAV-Assisted Mobile Edge Computing. Preprints2023, 2023121528. https://doi.org/10.20944/preprints202312.1528.v1
Yin, R.; Tian, H. Computing Offloading for Energy Conservation in UAV-Assisted Mobile Edge Computing. Preprints 2023, 2023121528. https://doi.org/10.20944/preprints202312.1528.v1
Yin, R.; Tian, H. Computing Offloading for Energy Conservation in UAV-Assisted Mobile Edge Computing. Preprints2023, 2023121528. https://doi.org/10.20944/preprints202312.1528.v1
APA Style
Yin, R., & Tian, H. (2023). Computing Offloading for Energy Conservation in UAV-Assisted Mobile Edge Computing. Preprints. https://doi.org/10.20944/preprints202312.1528.v1
Chicago/Turabian Style
Yin, R. and Hongxian Tian. 2023 "Computing Offloading for Energy Conservation in UAV-Assisted Mobile Edge Computing" Preprints. https://doi.org/10.20944/preprints202312.1528.v1
Abstract
The rapid growth of equipment tasks in 5G large-scale machine-type communication scenarios presents challenges due to user equipment’s (UE) limited computing power and power constraints. To address these limitations, task offloading to unmanned aerial vehicles (UAVs) has gained attention. However, traditional cloud computing centres far from the UE fail to meet latency requirements. Mobile Edge Computing (MEC) emerges as a solution by deploying computing servers at the edge of the cellular network to enhance service responsiveness. However, traditional MEC solutions need more flexibility. This paper explores the advantages of UAVs, including flexibility and rapid deployment, and considers UAV power constraints. It proposes a joint optimization approach for user offloading strategies and UAV trajectories to minimize the overall energy consumption of UE. By leveraging the benefits of UAVs and MEC, the proposed method aims to improve task execution efficiency in energy-constrained 5G machine-type communication scenarios.
Keywords
Mobile edge computing; Unmanned aerial vehicle; Computation offloading; Deep deterministic policy gradient
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
