arrow
Return

Joint Computation Offloading and Trajectory Optimization for Edge Computing UAV: A KNN-DDPG Algorithm

delete2024-10-09
delete2
delete
OA
AI
Y
Yiran Lu
C
Chi Xu *
Y
Yitian Wang
DOI:10.3390/drones8100564delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Unmanned aerial vehicles (UAVs) are widely used to improve the coverage and communication quality of wireless networks and assist mobile edge computing (MEC) due to their flexible deployments. However, the UAV-assisted MEC systems also face challenges in terms of computation offloading and trajectory planning in the dynamic environment. This paper employs deep reinforcement learning to jointly optimize the computation offloading and trajectory planning for UAV-assisted MEC system. Specifically, this paper investigates a general scenario where multiple pieces of user equipment (UE) offload tasks to a UAV equipped with a MEC server to collaborate on a complex job. By fully considering UAV and UE movement, computation offloading ratio, and blocked relations, a joint computation offloading and trajectory optimization problem is formulated to minimize the maximum computational delay. Due to the non-convex nature of the problem, it is converted into a Markov decision process, and solved by the deep deterministic policy gradient (DDPG) algorithm. To enhance the exploration capability and stability of DDPG, the K-nearest neighbor (KNN) algorithm is employed, namely KNN-DDPG. Moreover, the prioritized experience replay algorithm, where the constant learning rate is replaced by the decaying learning rate, is utilized to enhance the converge. To validate the effectiveness and superiority of the proposed algorithm, KNN-DDPG is compared with the benchmark DDPG algorithm. Simulation results demonstrate that KNN-DDPG can converge and achieve 3.23% delay reduction compared to DDPG.
Keywords:
unmanned aerial vehicle
mobile edge computing
computation offloading
deep deterministic policy gradient
K-nearest neighbor

Journal

D
Drones
IF:
4.8
Papers:
3.9K
Citations:
8.3K

Organization

S
Shenyang University of Chemical Technology
Scholars:
3.3K
Papers: 1.9K
Citations: 2.4K
C
chinese academy of sciences
Scholars:
56.7W
Papers: 45.0W
Citations: 704
Cited Papers

Cited Papers

Rural Non-Farm Engagement and Agriculture Commercialization in Ghana: Complements or Competitors?
err2018-01-01
err0
PREAI
errPaul Kwame Nkegbe; Abdelkrim Araar; Benjamin Abu; Yazidu Ustarz; Hamdiyah Alhassan; Edinam Dope Setsoafia; Shamsia Abdul-Wahab
errShare
errSave
Power Control in UAV-Supported Ultra Dense Networks: Communications, Caching, and Energy Transfer
err2018-06-01
err142
errOAAI
errWang, Haichao; Ding, Guoru; Gao, Feifei; Chen, Jin; Wang, Jinlong; Wang, Le
errShare
errSave
UAV-Aided Offloading for Cellular Hotspot
err2018-06-01
err259
errOAAI
errLyu, Jiangbin; Zeng, Yong; Zhang, Rui
errShare
errSave
ARTIFICIAL INTELLIGENCE EMPOWERED EDGE COMPUTING AND CACHING FOR INTERNET OF VEHICLES
err2019-06-01
err206
PREAI
errDai, Yueyue; Xu, Du; Maharjan, Sabita; Qiao, Guanhua; Zhang, Yan
errShare
errSave
err
IF0
err
err0
errOAAI
err
errShare
errSave
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems
err2019-04-01
err394
PREAI
errHu, Qiyu; Cai, Yunlong; Yu, Guanding; Qin, Zhijin; Zhao, Minjian; Li, Geoffrey Ye
errShare
errSave
A Survey on Mobile Edge Computing: The Communication Perspective
err2017-01-01
err2.6K
errOAAI
errMao, Yuyi; You, Changsheng; Zhang, Jun; Huang, Kaibin; Letaief, Khaled B.
errShare
errSave
A Deep Reinforcement Learning-Based Intelligent Maneuvering Strategy for the High-Speed UAV Pursuit-Evasion Game
errDRONES
IF4.8
err2024-07-09
err2
errOAAI
errYan, Tian; Liu, Can; Gao, Mengjing; Jiang, Zijian; Li, Tong
errShare
errSave
researcher View more