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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| Published in: | Future generation computer systems Vol. 174; p. 108027 |
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| Format: | Journal Article |
| Language: | English |
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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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| Cites_doi | 10.2174/2213275911666181115093050 10.1109/LMWT.2024.3411026 10.1016/j.aej.2024.02.048 10.1137/1.9781611977073.114 10.1109/TIV.2023.3323217 10.1109/ACCESS.2019.2920879 10.1109/TMC.2022.3225239 10.1016/j.comnet.2024.110806 10.1007/BF01726210 10.1109/TVT.2025.3526935 10.1109/TCE.2024.3357530 10.1007/PL00004200 10.1111/j.1540-6261.1961.tb02789.x 10.1007/s10586-024-04271-3 10.1109/TNSM.2024.3409557 10.1145/3490486.3538247 10.1109/TNSM.2024.3426076 10.1016/j.jksuci.2024.102196 10.1007/s12652-020-02623-6 10.1109/TITS.2024.3351635 10.1109/TCDS.2023.3345931 10.1109/TMC.2023.3237938 10.1016/j.comnet.2024.110796 10.1007/s11276-019-01982-z 10.1109/TMC.2023.3323524 10.1109/TGCN.2023.3349273 10.1109/TSC.2021.3075541 10.1109/TVT.2024.3375853 10.1109/TPDS.2021.3099731 |
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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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