Dynamic Network Edge Analysis for Internet of Vehicles with Graph Neural Networks

The Internet of Vehicles is a true enabler of in-telligent transportation, but it also faces communication and connectivity challenges due to the heterogeneity and high mobility of vehicles. More reliably, defining the vehicular network edge substantially helps cope with the highly dynamic environment of vehicles. Previous approaches have employed intelligence, prediction, optimization, and incentive modelling strategies to reduce communication challenges. These approaches still experience flexibility, scalability, and latency challenges, reducing service quality. Our work integrates Graph Neural Networks (GNNs) and clustering methodologies to define and maintain the vehicular edge. We propose an iterative GNN-based vehicular edge clustering framework with three different iterative learning procedures to facilitate the extraction of intricate temporal, spatial and functional patterns within vehicular networks. The experiments demonstrate that the proposed approach provides a promising solution to incorporate insights from trending communication and mobility features and improve the responsiveness of sporadic connectivity in a dynamic environment.