Liang Zhao
Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing
Zhao, Liang; Li, Tianyu; Meng, Guiying; Hawbani, Ammar; Min, Geyong; Al-Dubai, Ahmed; Zomaya, Albert
Authors
Tianyu Li
Guiying Meng
Ammar Hawbani
Geyong Min
Prof Ahmed Al-Dubai A.Al-Dubai@napier.ac.uk
Professor
Albert Zomaya
Abstract
Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to reduce the computation cost for the overall system. However, the state-of-the-art solutions have not fully addressed the challenge of large-scale task result feedback with low delay, due to the extremely flexible network structure and complex traffic data. In this paper, we explore the joint task offloading and resource allocation problem with result feedback cost in the VEC. In particular, this study develops a VEC computing offloading scheme, namely, a Lagrange multipliers-based adaptive computing offloading with prediction model, considering multiple RSUs and vehicles within their coverage areas. First, the VEC network architecture employs GAN to establish a prediction model, utilizing the powerful predictive capabilities of GAN to forecast the maximum distance of future trajectories, thereby reducing the decision space for task
.Subsequently, we propose an real-time adaptive model and adjust the parameters in different scenarios to accommodate the dynamic characteristic of the VEC network. Finally, we apply Lagrange Multiplier-based Non-Uniform Genetic Algorithm (LM-NUGA) to make task offloading decision. Effectively, this algorithm provides reliable and efficient computing services. The results from simulation indicate that our proposed scheme efficiently reduces the computation cost for the whole VEC system. This paves the way for a new generation of disruptive and reliable offloading schemes.
Citation
Zhao, L., Li, T., Meng, G., Hawbani, A., Min, G., Al-Dubai, A., & Zomaya, A. (in press). Novel Lagrange Multipliers-Driven Adaptive Offloading for Vehicular Edge Computing. IEEE Transactions on Computers,
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 28, 2024 |
Deposit Date | Aug 29, 2024 |
Print ISSN | 0018-9340 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Keywords | Vehicular Edge Computing, Result Feedback, Task Offloading, Resources Allocation, Swarm Intelligence |
Publisher URL | https://www.computer.org/ |
This file is under embargo due to copyright reasons.
Contact repository@napier.ac.uk to request a copy for personal use.
You might also like
A novel situation specific network security for Wireless Sensor Networks
(2015)
Journal Article
Guest editorial: scalable computing and communications
(2013)
Journal Article
A new dynamic counter-based broadcasting scheme for mobile ad hoc networks.
(2011)
Journal Article
An efficient neighbourhood load routing metric for wireless mesh networks
(2010)
Journal Article
GLBM: A new QoS aware multicast scheme for wireless Mesh network
(2010)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search