Ahsan Kazmi Ahsan.Kazmi@uwe.ac.uk
Senior Lecturer in Computer Science
Ahsan Kazmi Ahsan.Kazmi@uwe.ac.uk
Senior Lecturer in Computer Science
Tri Nguyen Dang
Ibrar Yaqoob
Aunas Manzoor
Rasheed Hussain
Adil Khan
Choong Seon Hong
Khaled Salah
The proliferation of compute-intensive services in next-generation vehicular networks will impose an unprecedented computation demand to meet stringent latency and resource requirements. Vehicular edge or fog computing has been a widely adopted solution to enhance the computational capacity of vehicular networks; however, the computation requirements of these compute hungry applications will surpass the capabilities of such a solution. To address this challenge, the on-board resources of neighboring mobile vehicles can be utilized. However, such resource utilization requires an incentive mechanism to motivate privately owned neighboring vehicles to participate in sharing their resources. In this paper, we propose a contract theory-based incentive mechanism that maximizes the social welfare of the vehicular networks by motivating neighboring vehicles to participate in sharing their resources. The proposed approach enables the Road Side Units (RSUs) to provide appropriate rewards by offering a tailored contract to each resource sharing vehicle based on their contribution and unique characteristics. Moreover, we derive an optimal contract scheme for computational task offloading, taking into account the individual rationality and incentive-compatible constraints. Finally, we perform numerical evaluations to demonstrate the effectiveness of our proposed scheme. The proposed scheme achieves up to 28% higher computing resource utilization, 17.2% lower energy consumption per computing resource utilization, and 17.1% lesser energy consumption per task completed when compared to the linear pricing incentive baseline.
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 7, 2021 |
Online Publication Date | May 25, 2021 |
Publication Date | Jul 8, 2022 |
Deposit Date | Aug 19, 2022 |
Publicly Available Date | Aug 19, 2022 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Print ISSN | 1524-9050 |
Electronic ISSN | 1558-0016 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 23 |
Issue | 7 |
Pages | 8380-8395 |
DOI | https://doi.org/10.1109/tits.2021.3078913 |
Keywords | Computer Science Applications, Mechanical Engineering, Automotive Engineering |
Public URL | https://uwe-repository.worktribe.com/output/9722903 |
Publisher URL | https://ieeexplore.ieee.org/document/9440722 |
Related Public URLs | https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979 |
A novel contract theory-based incentive mechanism for cooperative task-offloading in electrical vehicular networks
(18.4 Mb)
PDF
Licence
http://www.rioxx.net/licenses/all-rights-reserved
Publisher Licence URL
http://www.rioxx.net/licenses/all-rights-reserved
Copyright Statement
This is the author’s accepted manuscript of their article 'A novel contract theory-based incentive mechanism for cooperative task-offloading in electrical vehicular networks' published in IEEE Transactions on Intelligent Transportation Systems ( Volume: 23, Issue: 7, July 2022).
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
See https://www.ieee.org/publications/rights/index.html for more information.
Multiple adversarial domains adaptation approach for mitigating adversarial attacks effects
(2022)
Journal Article
A novel deep reinforcement learning-based approach for task-offloading in vehicular networks
(2022)
Presentation / Conference Contribution
API security in large enterprises: Leveraging machine learning for anomaly detection
(2021)
Presentation / Conference Contribution
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
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