Edge Clustering and Communication Efficiency with GNNs in Internet of Vehicles

Vehicular edge networks are pivotal in delivering services and applications that rely heavily on efficient resource allocation. Various strategies utilize intelligence, prediction, optimization, and incentive modelling to ensure optimal functioning within these networks. Despite the advancements, vehicular networks face persistent challenges that inhibit efficient resource allocation and communication. The most notable challenges are sporadic connectivity, transmission delays, and inherent uncertainty due to highly dynamic environments. In light of these challenges, integrating graph neural networks (GNNs), which learn hidden spatial and functional patterns of complex vehicular networks with clustering methodologies, emerges as a promising solution. By harnessing the power of GNNs and clustering, this approach provides an opportunity for more intelligent organization of the network nodes to reduce transmission delays and to improve resource allocation in highly dynamic environments. It creates a holistic environment that supports predictions and estimates based on trending communication and mobility features.