VAST-GCN: An Attention-Driven Graph Convolutional Network(GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks

Vehicular Ad-Hoc Networks (VANETs) need smart and flexible communication protocols to deal with fast-moving vehicles and ever-changing network structures. Picking the right cluster head (CH) plays a key role to keep connections stable and cut down on routing work. This paper presents VAST-GCN (Vehicular Attention-based Spatial-Temporal Graph Convolutional Network), a new model that uses attention to make vehicle grouping and CH selection better across different network sizes. VAST-GCN mixes Graph Convolutional Networks (GCNs) with Spatial, Temporal, and Channel Attention systems. We test our approach in vehicle settings with 100, 500, and 1000 vehicles making graph data using real-time info like speed and place. The design has transformer blocks to model time-based features and attention modules to improve space and feature relationships leading to better vehicle data. We group this data using the K-Means method and check it with modularity score, silhouette score, and group density. When we compare results, VAST-GCN does better than regular GCN and MixHopGCN models in cutting down loss making better community structures, and keeping CHs stable when there are few vehicles or they're moving fast. Our model offers a strong and scalable answer for future smart transport systems allowing reliable and efficient V2V talk.