Strategy-proof mechanism based on dwarf mongoose optimization for task offloading in vehicle computing
Along with intelligent vehicle (IV) development, IVCs can be used as mobile computing platforms to provide users with various services. The aim of this paper is to design an efficient task offloading mechanism to maximize group efficiency in vehicle computing. Considering that sensing data inherentl...
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| Veröffentlicht in: | Future generation computer systems Jg. 174; S. 108027 |
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| Sprache: | Englisch |
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01.01.2026
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| ISSN: | 0167-739X |
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| Abstract | Along with intelligent vehicle (IV) development, IVCs can be used as mobile computing platforms to provide users with various services. The aim of this paper is to design an efficient task offloading mechanism to maximize group efficiency in vehicle computing. Considering that sensing data inherently support multi-user sharing, we introduce a resource-sharing model in which multiple users share sensing resources. To provide a scalable service, we propose auction-based dynamic pricing. To achieve a balance between quality and efficiency, the efficient task offloading mechanism proposed in this study is based on dwarf mongoose optimization. The initialization algorithm generates random, best-fit, and greedy allocations based on probability. Convergence characteristics are improved using a new scouting algorithm and a new babysitter algorithm, both of which also contribute to maintaining population diversity. We demonstrate that the proposed mechanism achieves strategy-proofness, group strategy-proofness, individual rationality, budget balance, and consumer sovereignty. The novelty consists in our showing how to design the strategy-proof mechanism based on swarm optimization. Furthermore, the approximate ratio of the proposed mechanism is analyzed. Experimental verifications are conducted to show the proposed mechanism shows good performance in different environments. |
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| AbstractList | Along with intelligent vehicle (IV) development, IVCs can be used as mobile computing platforms to provide users with various services. The aim of this paper is to design an efficient task offloading mechanism to maximize group efficiency in vehicle computing. Considering that sensing data inherently support multi-user sharing, we introduce a resource-sharing model in which multiple users share sensing resources. To provide a scalable service, we propose auction-based dynamic pricing. To achieve a balance between quality and efficiency, the efficient task offloading mechanism proposed in this study is based on dwarf mongoose optimization. The initialization algorithm generates random, best-fit, and greedy allocations based on probability. Convergence characteristics are improved using a new scouting algorithm and a new babysitter algorithm, both of which also contribute to maintaining population diversity. We demonstrate that the proposed mechanism achieves strategy-proofness, group strategy-proofness, individual rationality, budget balance, and consumer sovereignty. The novelty consists in our showing how to design the strategy-proof mechanism based on swarm optimization. Furthermore, the approximate ratio of the proposed mechanism is analyzed. Experimental verifications are conducted to show the proposed mechanism shows good performance in different environments. |
| ArticleNumber | 108027 |
| Author | Liu, Jun Liu, Xi |
| Author_xml | – sequence: 1 givenname: Xi orcidid: 0000-0002-8011-5419 surname: Liu fullname: Liu, Xi email: lxghost@126.com organization: Key Laboratory of Intelligent Sensor and System Design, School of Information Engineering, Qujing Normal University, Sanjiang Avenue, Qujing, 655011, Yunnan, PR China – sequence: 2 givenname: Jun orcidid: 0000-0001-7757-3841 surname: Liu fullname: Liu, Jun email: liujunxei@126.com organization: School of Education, Yunnan College of Business Management, Qujing, 650106, Yunnan, PR China |
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| Keywords | Clock auction Group strategy-proof Vehicle computing Dwarf mongoose optimization Task offloading |
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| SubjectTerms | Clock auction Dwarf mongoose optimization Group strategy-proof Task offloading Vehicle computing |
| Title | Strategy-proof mechanism based on dwarf mongoose optimization for task offloading in vehicle computing |
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