An Intelligent Trust-Based Dissemination Framework for VANETs Using Reinforcement Learning and Edge-Assisted Validation
Keywords:
Trust management, Critical data dissemination, Reinforcement learning, Edge-assisted validation, Malicious node detection, Low-latency communication, Intelligent transportation systems, VANETs
Abstract
Vehicular ad hoc networks are a key communication component
of intelligent transportation systems, where timely and reliable
dissemination of critical information is essential for safety and traffic efficiency.
Nevertheless, rapid topology changes, intermittent connectivity,
and malicious node behavior can degrade dissemination reliability and
increase communication delay. This paper presents a trust-based dissemination
framework that combines context-aware trust evaluation, reinforcement
learning-based dissemination control, and edge-assisted validation
to improve secure and efficient data exchange among vehicular
nodes. Local trust is computed from forwarding reliability, message consistency,
plausibility assessment, historical cooperation, and delay-aware
behavior. Based on these trust observations and network conditions,
a Q-learning agent adaptively selects forwarding, cautious forwarding,
rerouting, holding, or isolation actions. To further enhance decision accuracy
and reduce false positives, nearby roadside edge units perform
regional corroboration and suspicious-event validation for uncertain or
suspicious events. The proposed framework is intended to enhance dissemination
reliability, reduce end-to-end delay, and improve resilience
against malicious behavior in dynamic vehicular environments. Overall,
it offers a scalable and intelligent approach for secure, reliable, and
low-latency vehicular communication.
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and Its Applications. Communications in Computer and Information Science,
vol. 2308, pp. 178–189. Springer, Cham (2025). https://doi.org/10.1007/
978-3-031-84394-5_13
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for node-centric misbehavior detection in dynamic behavior-homogeneous clusters.
Applied Sciences 15(4), 2020 (2025). https://doi.org/10.3390/app15042020
21. Zhang, D., Yu, F.R., Yang, R., Tang, H.: A deep reinforcement learning-based trust
management scheme for software-defined vehicular networks. In: Proceedings of
the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks
and Applications (DIVANet ’18). pp. 1–7. ACM (2018). https://doi.org/10.
1145/3272036.3272037
22. Zhang, D., Yu, F.R., Yang, R., Zhu, L.: Software-defined vehicular networks with
trust management: A deep reinforcement learning approach. IEEE Transactions
on Intelligent Transportation Systems 23(2), 1400–1414 (2022). https://doi.org/
10.1109/TITS.2020.3025684
trust management in VANETs: A review of current research and future
directions. ACM Computing Surveys 56(10), 1–39 (2024). https://doi.org/10.
1145/3656166
2. Ather, D., Kanday, R., Rakhra, M., Kler, R., Aggarwal, G., Jairath, K.:
Adaptive intrusion detection systems: Leveraging machine learning for real-timethreat mitigation. In: 2025 International Conference on Networks and Cryptology
(NETCRYPT). pp. 1743–1746. IEEE (2025). https://doi.org/10.1109/
NETCRYPT65877.2025.11102773
3. Ather, D., Kaur, H., Rakhra, M., Kler, R., Aggarwal, G., Jairath, K.: Data privacy
in edge computing: Securing solutions for distributed systems. In: 2025 International
Conference on Networks and Cryptology (NETCRYPT). pp. 1747–1752.
IEEE (2025). https://doi.org/10.1109/NETCRYPT65877.2025.11102306
4. Byeon, H., Seno, M.E., Srivastava, A.K., AlGhamdi, A., Keshta, I., Soni, M.,
Prasad, K.D.V., Abdurakhimova, D., Bhatt, M.W.: Trust management scheme
for securing vehicular ad hoc networks against malicious nodes and false message
anomaly. Transactions on Emerging Telecommunications Technologies 36(4),
e70110 (2025). https://doi.org/10.1002/ett.70110
5. Chaudhary, N., Ather, D., Kler, R., Dubey, R., Saxena, U., Singh, G.: Adaptive
QoS-aware routing for IoT networks using deep reinforcement learning. In:
2025 International Conference on Intelligent & Innovative Practices in Engineering
& Management (IIPEM). pp. 1–5. IEEE (2025). https://doi.org/10.1109/
IIPEM65914.2025.11548315
6. Che, H., Duan, Y., Li, C., Yu, L.: On trust management in vehicular ad hoc
networks: A comprehensive review. Frontiers in the Internet of Things 1, 995233
(2022). https://doi.org/10.3389/friot.2022.995233
7. Dong, S., Su, H., Xia, Y., Zhu, F., Hu, X., Wang, B.: A comprehensive survey
on authentication and attack detection schemes that threaten it in vehicular adhoc
networks. IEEE Transactions on Intelligent Transportation Systems 24(12),
13573–13602 (2023). https://doi.org/10.1109/TITS.2023.3297527
8. Gupta, A., Sagar, A.K.: A trust-based framework for enhancing security and reliability
in vehicular ad-hoc networks (VANETs). In: 2024 International Conference
on Computing, Sciences and Communications (ICCSC). pp. 1–6. IEEE (2024).
https://doi.org/10.1109/ICCSC62048.2024.10830436
9. Khan, R.L., Singh, R., Vijay, R., Kumar, R., Singh, A., Ather, D.: Evaluating
the impact of different routing protocols on VANET performance. In: 2023 12th
International Conference on System Modeling & Advancement in Research Trends
(SMART). pp. 308–314. IEEE (2023). https://doi.org/10.1109/SMART59791.
2023.10428409
10. Lee, M., Atkison, T.: VANET applications: Past, present, and future. Vehicular
Communications 28, 100310 (2021). https://doi.org/10.1016/j.vehcom.2020.
100310
11. Li, W., Song, H.: ART: An attack-resistant trust management scheme for securing
vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation
Systems 17(4), 960–969 (2016). https://doi.org/10.1109/TITS.2015.2494017
12. Mazhar, S., Rakib, A., Pan, L., Jiang, F., Anwar, A., Doss, R., Bryans, J.: Stateof-
the-art authentication and verification schemes in VANETs: A survey. Vehicular
Communications 49, 100804 (2024). https://doi.org/10.1016/j.vehcom.2024.
100804
13. Meneguette, R., De Grande, R., Ueyama, J., Rocha Filho, G.P., Madeira, E.: Vehicular
edge computing: Architecture, resource management, security, and challenges.
ACM Computing Surveys 55(1), 1–46 (2021). https://doi.org/10.1145/
3485129
14. Mundhe, P., Verma, S., Venkatesan, S.: A comprehensive survey on authentication
and privacy-preserving schemes in VANETs. Computer Science Review 41, 100411
(2021). https://doi.org/10.1016/j.cosrev.2021.100411
15. Qi, J., Zheng, N., Xu, M., Chen, P., Li, W.: A hybrid-trust-based emergency
message dissemination model for vehicular ad hoc networks. Journal of Information
Security and Applications 81, 103699 (2024). https://doi.org/10.1016/j.jisa.
2024.103699
16. Raj, D., Ather, D., Sagar, A.K.: Advancing vehicular ad-hoc network solutions
in emerging economies: A comparative analysis of V2V protocols through simulation
studies. SN Computer Science 5(8), 1077 (2024). https://doi.org/10.1007/
s42979-024-03411-1
17. Rawat, G.S., Singh, K., Shariq, M., Das, A.K., Chaudhry, S.A., Lorenz, P.:
BTC2PA: A blockchain-assisted trust computation with conditional privacypreserving
authentication for connected vehicles. IEEE Transactions on Intelligent
Transportation Systems 26(1), 1134–1148 (2025). https://doi.org/10.
1109/TITS.2024.3488184
18. Sarker, O., Shen, H., Babar, M.A.: Reinforcement learning based neighbour selection
for VANET with adaptive trust management. In: 2023 IEEE 22nd International
Conference on Trust, Security and Privacy in Computing and Communications
(TrustCom). pp. 585–594. IEEE (2023). https://doi.org/10.1109/
TrustCom60117.2023.00091
19. Singh, R., Kumar, R., Singh, A., Vijay, R., Khan, R.L., Ather, D.: AIdriven
VANETs for IoT-enabled transportation systems. In: Artificial Intelligence
and Its Applications. Communications in Computer and Information Science,
vol. 2308, pp. 178–189. Springer, Cham (2025). https://doi.org/10.1007/
978-3-031-84394-5_13
20. Xu, X., Zhu, W., Fu, X., Yang, G., Jin, L., Yu, W., You, L.: Hybrid trust model
for node-centric misbehavior detection in dynamic behavior-homogeneous clusters.
Applied Sciences 15(4), 2020 (2025). https://doi.org/10.3390/app15042020
21. Zhang, D., Yu, F.R., Yang, R., Tang, H.: A deep reinforcement learning-based trust
management scheme for software-defined vehicular networks. In: Proceedings of
the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks
and Applications (DIVANet ’18). pp. 1–7. ACM (2018). https://doi.org/10.
1145/3272036.3272037
22. Zhang, D., Yu, F.R., Yang, R., Zhu, L.: Software-defined vehicular networks with
trust management: A deep reinforcement learning approach. IEEE Transactions
on Intelligent Transportation Systems 23(2), 1400–1414 (2022). https://doi.org/
10.1109/TITS.2020.3025684
Published
2026-10-29
How to Cite
Anurag Gupta, & Anil Kumar Sagar. (2026). An Intelligent Trust-Based Dissemination Framework for VANETs Using Reinforcement Learning and Edge-Assisted Validation. MATRIX Academic International Online Journal Of Engineering And Technology, 9(2), 1-15. Retrieved from https://www.maiojet.com/index.php/matrix/article/view/117
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