Adaptive Multi-Agent Graph Neural Network (AMAGNN) for Congestion Control in VANET

Similar approaches are very useful in Vehicular Ad Hoc Networks in which: a flood of traffic and events occurs, especially in Intelligent Transportation Systems (ITS), as congestion control can have a huge impact in efficient traffic field and communication. The existing congestion control mechanisms are not suitable for this type of dynamic network topology and vehicle mobility. In response to these challenges, we propose an Adaptive Multi-Agent Graph Neural Network (AMAGNN), which utilizes graph neural networks (GNNs) and multi-agent reinforcement learning (MARL) for congestion control in vehicular ad hoc networks (VANETs). Institute, “AMAGNN models VANETs as dynamic graphs, with vehicles being intelligent agents that cooperatively learn to optimize data propagation and alleviate network congestion. Optimizes for scalability and adaptability by dynamically responding to network topology changes and traffic density variation. Results show that the proposed AMAGNN model outperforms current practices in congestion control for VANETs on all the key performance metrics. It attains Packet Delivery Ratio (92.5%), lowest delay (28.4 ms), and maximum throughput (275.6 kbps) which shows reliability and efficiency in communication. It also achieves the lowest congestion level (0.35) and average waiting time (0.31 s), highlighting its ability to reduce traffic and delay. AMAGNN achieves the best performance, compared with methods such as GCN-RL, DQN Adaptive, and MA-PPO, highlighting the effectiveness of adaptive multi-agent graph neural networks in the ever-changing vehicular environment.