Blockchain-based distributed software-defined vehicular networks: a dueling deep q-learning approach
Abstract
Vehicular ad hoc networks (VANETs) have become an essential part in smart transportation systems of modern cities. However, because of dynamicity and infrastructure-less of VANETs, the ever increasing number of network security issues become obstacles for the realization of smart cities. Software-defined VANETs have provided a reliable way to manage VANETs dynamically and securely. However, the traditionally centralized control plane makes it vulnerable to malicious nodes and results in performance degradation. Therefore, a distributed control plane is necessary. How to reach a consensus among multiple controllers under complex vehicular environment is an essential problem. In this paper, we propose a novel blockchain-based distributed software-defined VANET framework (block-SDV) to establish a secure architecture to overcome the above issues. The trust features of blockchain nodes, the number of consensus nodes, trust features of each vehicle, and the computational capability of the blockchain are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space and reward function. Since it is difficult to be solved by traditional methods, we propose a novel dueling deep Q-learning (DDQL) with prioritized experience replay approach. Simulation results are presented to show the effectiveness of the proposed block-SDV framework.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent works introduced efficient security analysis tools to investigate and prevent threat potential in order to ensure trustful smart contract execution on blockchain.
“Particularly, recent works [301], [302] introduced efficient security analysis tools to investigate and prevent threat potential in order to ensure trustful smart contract execution on blockchain.”
From Blockchain for 5G and beyond networks: A state of the art survey (2020) · cites this paper as [302]
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120 · see citing works
Practical byzantine fault tolerance and proactive recovery
Castro, M., & Liskov, B. (2002) · ACM Transactions on Computer Systems · doi:10.1145/571637.571640 · Cited by 2,475 · see citing works
Onix: a distributed control platform for large-scale production networks
Koponen, T., Casado, M., Gude, N. S., Stribling, J., Poutievski, L., Zhu, M., Ramanathan, R., Iwata, Y., Inoue, H., Hama, T., et al. (2010) · doi:10.5555/1924943.1924968 · Cited by 1,247 · see citing works
HyperFlow: a distributed control plane for OpenFlow
Tootoonchian, A., & Ganjali, Y. (2010) · Cited by 899 · see citing works
A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges
Xie, J., Tang, H., Huang, T., Yu, F. R., Xie, R., Liu, J., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2899617 · Cited by 773 · see citing works
Self-citationIntegrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges
Yang, R., Yu, F. R., Si, P., Yang, Z., & Zhang, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2894727 · Cited by 688 · see citing works
Self-citationA Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT
Sharma, P. K., Chen, M.-Y., & Park, J. H. (2018) · IEEE Access · doi:10.1109/access.2017.2757955 · Cited by 676 · see citing works
Project Adam: building an efficient and scalable deep learning training system
Chilimbi, T. M., Suzue, Y., Apacible, J., & Kalyanaraman, K. (2014) · Operating Systems Design and Implementation · doi:10.5555/2685048.2685094 · Cited by 614 · see citing works
Survey on VANET security challenges and possible cryptographic solutions
Mejri, M. N., Ben‐Othman, J., & Hamdi, M. (2014) · Vehicular Communications · doi:10.1016/j.vehcom.2014.05.001 · Cited by 559 · see citing works
Distributed SDN Control: Survey, Taxonomy, and Challenges
Bannour, F., Souihi, S., & Mellouk, A. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2782482 · Cited by 454 · see citing works
Making Byzantine fault tolerant systems tolerate Byzantine faults
Clement, A., Wong, E. L., Alvisi, L., Dahlin, M., & Marchetti, M. (2009) · Cited by 381 · see citing works
Wei, Y., Yu, F. R., Song, M. N., & Han, Z. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2878435 · Cited by 321 · see citing works
Self-citationHe, Y., Zhang, Z., Yu, F. R., Zhao, N., Yin, H., Leung, V. C. M., & Zhang, Y. (2017) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2017.2751641 · Cited by 288 · see citing works
Self-citationRBFT: Redundant Byzantine Fault Tolerance
Aublin, P.-L., Mokhtar, S. B., & Quéma, V. (2013) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2013.53 · Cited by 286 · see citing works
Liu, M., Yu, F. R., Teng, Y., Leung, V. C. M., & Song, M. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2885266 · Cited by 233 · see citing works
Self-citationQiu, C., Yu, F. R., Yao, H., Jiang, C., Xu, F., & Zhao, C. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2871394 · Cited by 195 · see citing works
Self-citationVirtualization for Distributed Ledger Technology (vDLT)
Yu, F. R., Liu, J., He, Y., Si, P., & Zhang, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2829141 · Cited by 126 · see citing works
Self-citationCache-Enabled Adaptive Video Streaming Over Vehicular Networks: A Dynamic Approach
Guo, Y., Yang, Q., Yu, F. R., et al. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2817210 · Cited by 81 · see citing works
Self-citationThe importance of experience replay database composition in deep reinforcement learning
de Bruin, T., Kober, J., Tuyls, K., & Babuška, R. (2015) · neural information processing systems · Cited by 77 · see citing works
Alzamzami, O., & Mahgoub, I. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2867177 · Cited by 70 · see citing works
IEEE 802.11e Enhanced Distributed Channel Access (EDCA) Throughput Analysis
Wu, H., Wang, X., Zhang, Q., et al. (2006) · International Conference on Communications · doi:10.1109/icc.2006.254731 · Cited by 64 · see citing works
Trust management for secure cognitive radio vehicular ad hoc networks
He, Y., Yu, F. R., Wei, Z., et al. (2018) · Ad Hoc Networks · doi:10.1016/j.adhoc.2018.11.006 · Cited by 56 · see citing works
Self-citationTyagi, P., & Dembla, D. (2014) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2014.6968313 · Cited by 49 · see citing works
Blockchain-Based Distributed Software-Defined Vehicular Networks via Deep Q-Learning
Qiu, C., Yu, F. R., Xu, F., et al. (2018) · doi:10.1145/3272036.3272040 · Cited by 34 · see citing works
Self-citationA Trust-Based Routing Framework in Energy-Constrained Wireless Sensor Networks
Weifang, C., Xiangke, L., Chang-xiang, S., et al. (2006) · Lecture notes in computer science · doi:10.1007/11814856_46 · Cited by 27 · see citing works
Impact of node mobility on single-hop cluster overlap in vehicular ad hoc networks
Abboud, K., & Zhuang, W. (2014) · ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) · doi:10.1145/2641798.2641832 · Cited by 19 · see citing works
Connected Vehicles for Intelligent Transportation Systems [Guest editorial]
Yu, F. R. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2565738 · Cited by 18 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Blockchain for 5G and beyond networks: A state of the art survey
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102693 · Cited by 412 · see citing works
Manogaran, G., Saravanan, V., & Hsu, C.-H. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3058452 · Cited by 16 · see citing works
- Authors
- Zhang, D., Yu, F. R., & Yang, R.
- Affiliations
- Carleton University (CA)
- Beijing University of Technology (CN)
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2019-12
- Volume
- 5
- Issue
- 4
- Pages
- 1086-1100
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W2978169795
- Cited by
- 96
- References
- 30
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 10 · 2023: 11 · 2022: 22 · 2021: 18 · 2020: 23 · 2019: 1
- FWCI
- 5.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- vehicular ad hoc networks, software-defined networking, blockchain, distributed control plane, prioritized experience replay, Markov decision process, malicious node detection, network security, smart transportation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
