Blockchain Intelligence for Internet of Vehicles: Challenges and Solutions
Abstract
With the development of communication and networking technologies, the Internet of Vehicles (IoV) has become the foundation of smart transportation. The development of blockchain and Machine Learning (ML) has contributed to the pervasiveness of the IoV, and they can effectively address the current issues of decentralisation, cyber security and data privacy in the IoV. In this article, blockchain and ML in the IoV are both reviewed, and corresponding technologies to support blockchain intelligence in the IoV are summarized. Importantly, blockchain intelligence is proposed as a key to integrate blockchain and ML, combining the advantages of both to drive the rapid development of the IoV. We discuss general frameworks, issuses, requirements and advantages for the implementation of blockchain intelligence in the IoV. Driven by its advantages, we summarize solutions of blockchain intelligence in the IoV from four aspects, including reliable interaction, network security and data privacy, trustworthy environment and scalability. Finally, a summary of current unresolved issues and challenges of blockchain intelligence in the IoV is presented, which provides guidelines for the future development of the IoV.
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Blockchain can introduce incentive mechanisms to encourage honest participation and fair behavior among contributors.
“It can also introduce incentive mechanisms to encourage honest participation and fair behavior among contributors [151].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [151]
Blockchain employs cryptographic techniques to secure data, ensuring that once data is recorded, it cannot be altered without consensus from the network, providing a robust mechanism against tampering and unauthorized modifications.
“This ensures that once data is recorded, it cannot be altered without consensus from the network, providing a robust mechanism against tampering and unauthorized modifications [151].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [151]
Blockchain can enhance FL by providing a secure and decentralized framework for aggregating model updates from different parties.
“Furthermore, blockchain can enhance FL by providing a secure and decentralized framework for aggregating model updates from different parties.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [151]
Cites
A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues
Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., & Markakis, E. K. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2962586 · Cited by 852 · see citing works
A Blockchain-Based Decentralized Federated Learning Framework with Committee Consensus
Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., & Yan, Q. (2021) · IEEE Network · doi:10.1109/mnet.011.2000263 · Cited by 645 · see citing works
DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., & Luo, W. (2019) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2019.2952332 · Cited by 610 · cited 3× · see citing works
“Third, blockchain motivates users to participate in collaboration with integrity to create trustworthy networks [78].”
BackgroundInfluentialSelf-citationTowards Fair and Privacy-Preserving Federated Deep Models
Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2996273 · Cited by 250 · cited 3× · see citing works
“The authors in [127] propose a local credibility mutual evaluation mechanism to guarantee credible decision-making, ensure fairness, and motivate participants based on different contributions.”
BackgroundInfluentialLuong, N. C., Xiong, Z., Wang, P., & Niyato, D. (2018) · 2018 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2018.8422743 · Cited by 229 · see citing works
Traffic Jam Probability Estimation Based on Blockchain and Deep Neural Networks
Hassija, V., Gupta, V., Garg, S., & Chamola, V. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2988040 · Cited by 77 · see citing works
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Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“Additionally, techniques related to authentication, authorization, and access control, aimed at securing user identities or access to data, are out of scope.”
Influential
- Authors
- Wang, X., Zhu, H., Ning, Z., Guo, L., & Zhang, Y.
- Affiliations
- Chongqing University of Posts and Telecommunications (CN)
- University of Oslo (NO)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2023
- Volume
- 25
- Issue
- 4
- Pages
- 2325-2355
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W4385834563
- Cited by
- 131
- References
- 6
- Citations by year
- 2026: 28 · 2025: 46 · 2024: 45 · 2023: 12
- FWCI
- 30.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- Natural Science Foundation of China (61971084, 62025105, 62001073, 62221005, 62272075)
- National Natural Science Foundation of Chongqing (cstc2021ycjhbgzxm0039, cstc2021jcyj-msxmX0031, CSTB2022BSXM-JCX0109, CSTB2022BSXM-JCX0110)
- Science and Technology Research Program for Chongqing Municipal Education Commission (CXQT21019, KJZDM202200601)
- Support Program for Overseas Students to Return to China for Entrepreneurship and Innovation (cx2021003, cx2021053)
- EU Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie (101008297, 824019)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- blockchain, Internet of Vehicles, machine learning, decentralization (distributed systems), cybersecurity, data privacy, network security, scalability, smart transportation
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