AI-VASNet: An AI-Driven Adaptive Data Dissemination Framework for Vehicular Ad Hoc and Sensor Networks

Asia Sultana; Khaleel Ur Rahman Khan; V. Kamakshi Prasad

doi:10.48084/etasr.18350

Authors

Asia Sultana JNTU, Hyderabad, India
Khaleel Ur Rahman Khan ACE Engineering College, Hyderabad, India
V. Kamakshi Prasad JNTU, Hyderabad, India

Volume: 16 | Issue: 3 | Pages: 35324-35333 | June 2026 | https://doi.org/10.48084/etasr.18350

Received: 24 February 2026 | Revised: 30 March 2026 and 5 April 2026 | Accepted: 6 April 2026 | Online: 18 April 2026

Corresponding author: Asia Sultana

Abstract

Vehicular Ad Hoc and Sensor Networks (VASNETs) operate under high node mobility, rapidly changing network topology, and stringent latency constraints, making reliable data dissemination particularly challenging. Conventional routing protocols struggle to maintain stable communication in dense traffic conditions and under intermittent connectivity caused by frequent topology variations. To address these limitations, this paper introduces AI-VASNet, a hybrid adaptive routing framework that integrates Reinforcement Learning (RL) for adaptive forwarding decisions, Federated Learning (FL)-based link reliability inference, fuzzy Quality-of-Service (QoS) prioritization for traffic differentiation, and instability-aware neighbor filtering to reduce unreliable communication links. Unlike existing approaches that apply these mechanisms independently, AI-VASNet tightly combines them into a unified decision framework, enabling intelligent packet forwarding while preserving data privacy through decentralized learning. Simulation results demonstrate that AI-VASNet achieves up to an 18% improvement in Packet Delivery Ratio (PDR), reduces end-to-end latency by approximately 23%, and improves throughput by nearly 20% compared with six representative routing protocols, under high node density and mobility conditions. These findings indicate that AI-VASNet provides an adaptive and scalable routing solution for next-generation Vehicle-to-Everything (V2X) communication and Intelligent Transportation Systems (ITS).

Keywords:

data dissemination, Federated Learning (FL), Reinforcement Learning (RL), Vehicular Ad Hoc and Sensor Networks (VASNETs)

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References

J. Wang, C. Jiang, K. Zhang, T. Q. S. Quek, Y. Ren, and L. Hanzo, "Vehicular Sensing Networks in a Smart City: Principles, Technologies and Applications," IEEE Wireless Communications, vol. 25, no. 1, pp. 122–132, Feb. 2018.

N. H. Hussein, C. T. Yaw, S. P. Koh, S. K. Tiong, and K. H. Chong, "A Comprehensive Survey on Vehicular Networking: Communications, Applications, Challenges, and Upcoming Research Directions," IEEE Access, vol. 10, pp. 86127–86180, 2022.

B. Saoud et al., "Artificial Intelligence, Internet of things and 6G methodologies in the context of Vehicular Ad-hoc Networks (VANETs): Survey," ICT Express, vol. 10, no. 4, pp. 959–980, Aug. 2024.

J. Hao, R. Naja, and D. Zeghlache, "Adaptive federated reinforcement learning for critical realtime communications in UAV assisted vehicular networks," Computer Networks, vol. 247, June 2024, Art. no. 110456.

A. S. Ali, A. A. Al-Habob, S. Naser, L. Bariah, O. A. Dobre, and S. Muhaidat, "Deep Reinforcement Learning for Energy-Efficient Data Dissemination Through UAV Networks," IEEE Open Journal of the Communications Society, vol. 5, pp. 5567–5583, 2024.

R. A. Nazib and S. Moh, "Reinforcement Learning-Based Routing Protocols for Vehicular Ad Hoc Networks: A Comparative Survey," IEEE Access, vol. 9, pp. 27552–27587, 2021.

J. Guo et al., "TROVE: A Context-Awareness Trust Model for VANETs Using Reinforcement Learning," IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6647–6662, July 2020.

P. Upadhyay et al., "An improved deep reinforcement learning routing technique for collision-free VANET," Scientific Reports, vol. 13, no. 1, Dec. 2023, Art. no. 21796.

O. Sarker, H. Shen, and M. A. Babar, "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), Exeter, United Kingdom, 2023, pp. 585–594.

T. Akyildiz and H. Mahdavifar, "Deep Reinforcement Learning-Aided Strategies for Big Data Offloading in Vehicular Networks." arXiv, Dec. 19, 2025.

J. Wu, M. Fang, H. Li, and X. Li, "RSU-Assisted Traffic-Aware Routing Based on Reinforcement Learning for Urban Vanets," IEEE Access, vol. 8, pp. 5733–5748, 2020.

H. Al-Maliki, H. A. A. AL-Asadi, Z. A. Abduljabbar, and V. O. Nyangaresi, "Reliable Vehicular Ad Hoc Networks for Intelligent Transportation Systems based on the Snake Optimization Algorithm," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18631–18639, Dec. 2024.

S. Ye, L. Xu, Z. Xu, and F. Wang, "A deep reinforcement learning-based intelligent QoS optimization algorithm for efficient routing in vehicular networks," Alexandria Engineering Journal, vol. 107, pp. 317–331, Nov. 2024.

X. Liu, B. S. Amour, and A. Jaekel, "A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET," Applied Sciences, vol. 13, no. 6, Mar. 2023, Art. no. 3640.

W. Zhang, X. Yang, Q. Song, and L. Zhao, "V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning," International Journal of Computers Communications & Control, vol. 16, no. 1, Jan. 2021, Art. no. 4123.

D. Chen, T. Deng, J. Jia, S. Feng, and D. Yuan, "Mobility-aware decentralized federated learning with joint optimization of local iteration and leader selection for vehicular networks," Computer Networks, vol. 263, May 2025, Art. no. 111232.

X. Qiang, Z. Chang, C. Ye, T. Hämäläinen, and G. Min, "Split Federated Learning Empowered Vehicular Edge Intelligence: Concept, Adaptive Design, and Future Directions," IEEE Wireless Communications, vol. 32, no. 4, pp. 90–97, Aug. 2025.

R. Valente, C. Senna, P. Rito, and S. Sargento, "Embedded Federated Learning for VANET Environments," Applied Sciences, vol. 13, no. 4, Feb. 2023, Art. no. 2329.

M. Piran, R. M. Garimella, and P. Babu, "Vehicular Ad Hoc and Sensor Networks: Principals and Challenges," International Journal of Ad hoc, Sensor & Ubiquitous Computing, vol. 2, no. 2, pp. 38–49, June 2011.

R. Al-Qassas and M. Qasaimeh, "An empirical evaluation of link quality utilization in ETX routing for VANETs," PeerJ Computer Science, vol. 10, Sept. 2024, Art. no. e2259.

J. Cao, Y. Bian, C. He, F. Liu, D. Xu, and Y. Guo, "Evaluation of Connectivity Reliability of VANETs Considering Node Mobility and Multiple Failure Modes," Sensors, vol. 25, no. 19, Oct. 2025, Art. no. 6073.

V. P. Chellapandi, L. Yuan, C. G. Brinton, S. H. Żak, and Z. Wang, "Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges," IEEE Transactions on Intelligent Vehicles, vol. 9, no. 1, pp. 119–137, Jan. 2024.

J. Lansky, A. M. Rahmani, and M. Hosseinzadeh, "Reinforcement Learning-Based Routing Protocols in Vehicular Ad Hoc Networks for Intelligent Transport System (ITS): A Survey," Mathematics, vol. 10, no. 24, Dec. 2022, Art. no. 4673.

S. K. Basha and T. N. Shankar, "Fuzzy logic based forwarder selection for efficient data dissemination in VANETs," Wireless Networks, vol. 27, no. 3, pp. 2193–2216, Apr. 2021.

Y. L. Morgan, "Notes on DSRC & WAVE Standards Suite: Its Architecture, Design, and Characteristics," IEEE Communications Surveys & Tutorials, vol. 12, no. 4, pp. 504–518, 2010.

N. Iturbe-Olleta, J. Bilbao, O. Iparraguirre, J. Mendizabal, and A. Brazalez, "An adjusted propagation model for ITS-G5 communications for improving the location of RSUs in real V2I deployments," Vehicular Communications, vol. 45, Feb. 2024, Art. no. 100716.

S. Sharma, S. Kour, and H. Sarangal, "Modeling and simulation of VANET routing protocols under realistic mobility patterns," Scientific Reports, vol. 16, no. 1, Feb. 2026, Art. no. 9130.