Research on Optimal Computing Offloading Strategy of Internet of Vehicles Based on Reinforcement Learning.
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| Title: | Research on Optimal Computing Offloading Strategy of Internet of Vehicles Based on Reinforcement Learning. |
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| Authors: | Qiu, Shaolin1 bykjsl@163.com, Deng, Shuchao2 dengsc@ahut.edu.cn, Pang, Honglei3 panghl@niit.edu.cn, Wang, Bo4 3181487830@qq.com, Ye, Hao5 18913929898@163.com |
| Source: | IAENG International Journal of Computer Science. May2026, Vol. 53 Issue 5, p2011-2020. 10p. |
| Subjects: | Edge computing, Federated learning, Multiagent systems, Privacy, Mobile communication systems, Reinforcement learning |
| Abstract: | Vehicle edge computing (VEC) effectively alleviates the computational resource limitations of in-vehicle terminals by offloading computation-intensive tasks to edge servers. However, offloading decision-making remains challenging due to highly dynamic network topologies, complex task dependency structures, and multi-dimensional quality-of-service (QoS) constraints. To address these issues, this paper proposes a Federated Multi-Agent Proximal Policy Optimization (Fed-MAPPO) framework for intelligent computation offloading with multi-objective trade-offs. The proposed approach models task dependencies using directed acyclic graphs (DAGs) and formulates the problem as a partially observable Markov decision process (POMDP). A dependency-aware state encoder is designed to jointly capture task topology and network dynamics, while a differential privacy-based secure aggregation mechanism is introduced to protect sensitive vehicular data during federated learning. In addition, a hierarchical attention-based value decomposition network is employed to resolve the credit assignment problem in multi-vehicle collaboration. Experiments conducted on a SUMO/TraCI and OMNeT++ co-simulation platform with realistic traffic scenarios generated from Shanghai taxi trajectories demonstrate that, compared with FedAvg-DQN, Fed-MAPPO improves the task completion rate by 12.7%, reduces average latency by 23.4%, accelerates convergence speed by 35.6%, and maintains performance degradation below 3% under strong privacy constraints (ε = 0.5). These results indicate that the proposed framework provides a scalable and robust paradigm for intelligent offloading in large-scale heterogeneous vehicular networks. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193482051 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Research on Optimal Computing Offloading Strategy of Internet of Vehicles Based on Reinforcement Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qiu%2C+Shaolin%22">Qiu, Shaolin</searchLink><relatesTo>1</relatesTo><i> bykjsl@163.com</i><br /><searchLink fieldCode="AR" term="%22Deng%2C+Shuchao%22">Deng, Shuchao</searchLink><relatesTo>2</relatesTo><i> dengsc@ahut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Pang%2C+Honglei%22">Pang, Honglei</searchLink><relatesTo>3</relatesTo><i> panghl@niit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Bo%22">Wang, Bo</searchLink><relatesTo>4</relatesTo><i> 3181487830@qq.com</i><br /><searchLink fieldCode="AR" term="%22Ye%2C+Hao%22">Ye, Hao</searchLink><relatesTo>5</relatesTo><i> 18913929898@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. May2026, Vol. 53 Issue 5, p2011-2020. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+communication+systems%22">Mobile communication systems</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Vehicle edge computing (VEC) effectively alleviates the computational resource limitations of in-vehicle terminals by offloading computation-intensive tasks to edge servers. However, offloading decision-making remains challenging due to highly dynamic network topologies, complex task dependency structures, and multi-dimensional quality-of-service (QoS) constraints. To address these issues, this paper proposes a Federated Multi-Agent Proximal Policy Optimization (Fed-MAPPO) framework for intelligent computation offloading with multi-objective trade-offs. The proposed approach models task dependencies using directed acyclic graphs (DAGs) and formulates the problem as a partially observable Markov decision process (POMDP). A dependency-aware state encoder is designed to jointly capture task topology and network dynamics, while a differential privacy-based secure aggregation mechanism is introduced to protect sensitive vehicular data during federated learning. In addition, a hierarchical attention-based value decomposition network is employed to resolve the credit assignment problem in multi-vehicle collaboration. Experiments conducted on a SUMO/TraCI and OMNeT++ co-simulation platform with realistic traffic scenarios generated from Shanghai taxi trajectories demonstrate that, compared with FedAvg-DQN, Fed-MAPPO improves the task completion rate by 12.7%, reduces average latency by 23.4%, accelerates convergence speed by 35.6%, and maintains performance degradation below 3% under strong privacy constraints (ε = 0.5). These results indicate that the proposed framework provides a scalable and robust paradigm for intelligent offloading in large-scale heterogeneous vehicular networks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2011 Subjects: – SubjectFull: Edge computing Type: general – SubjectFull: Federated learning Type: general – SubjectFull: Multiagent systems Type: general – SubjectFull: Privacy Type: general – SubjectFull: Mobile communication systems Type: general – SubjectFull: Reinforcement learning Type: general Titles: – TitleFull: Research on Optimal Computing Offloading Strategy of Internet of Vehicles Based on Reinforcement Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qiu, Shaolin – PersonEntity: Name: NameFull: Deng, Shuchao – PersonEntity: Name: NameFull: Pang, Honglei – PersonEntity: Name: NameFull: Wang, Bo – PersonEntity: Name: NameFull: Ye, Hao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 5 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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