Article: A dynamic routing algorithm for VANET with graph neural networks and deep reinforcement learning Journal: International Journal of Sensor Networks (IJSNET) 2025 Vol.48 No.2 pp.119 - 134 Abstract: In vehicular ad hoc networks (VANET), the direct vehicle-to-vehicle communication provides a necessary supplement to the data transmission of intelligent transportation systems. However, the transient and volatility of VANET topology bring challenges to establishing an efficient and reliable end-to-end routing. To this end, a dynamic routing algorithm for VANET integrating graph neural networks and deep reinforcement learning is proposed. Firstly, the network topology is delineated according to the routing request, and graph features are extracted based on the routing establishment objective. Then, the routing relay selection problem is modelled as a Markov decision process, and the network topology information is learned using graph neural networks and solved using a deep Q-learning algorithm framework. Specifically, in order to better evaluate actions, a new fuzzy logic-based reward function is present. Simulation results show that the algorithm has better performance in terms of average end-to-end delay, hop count and packet delivery rate compared to other algorithms. Inderscience Publishers - linking academia, business and industry through research

Title: A dynamic routing algorithm for VANET with graph neural networks and deep reinforcement learning

Authors: Xiang Bi; Lingjie Huang; Benhong Zhang; Zhen Chen; Zengwei Lyu

Addresses: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China ' School of Computer Science and Technology, Anhui University, Hefei, Anhui, 230601, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

Abstract: In vehicular ad hoc networks (VANET), the direct vehicle-to-vehicle communication provides a necessary supplement to the data transmission of intelligent transportation systems. However, the transient and volatility of VANET topology bring challenges to establishing an efficient and reliable end-to-end routing. To this end, a dynamic routing algorithm for VANET integrating graph neural networks and deep reinforcement learning is proposed. Firstly, the network topology is delineated according to the routing request, and graph features are extracted based on the routing establishment objective. Then, the routing relay selection problem is modelled as a Markov decision process, and the network topology information is learned using graph neural networks and solved using a deep Q-learning algorithm framework. Specifically, in order to better evaluate actions, a new fuzzy logic-based reward function is present. Simulation results show that the algorithm has better performance in terms of average end-to-end delay, hop count and packet delivery rate compared to other algorithms.

Keywords: vehicular ad hoc networks; VANET; routing; multi-objective optimisation; fuzzy logic; graph neural networks; deep reinforcement learning.

DOI: 10.1504/IJSNET.2025.146775

International Journal of Sensor Networks, 2025 Vol.48 No.2, pp.119 - 134

Received: 13 Dec 2023
Accepted: 13 Dec 2024
Published online: 17 Jun 2025 *

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Title: A dynamic routing algorithm for VANET with graph neural networks and deep reinforcement learning

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