Edge Communication Efficiency with GNN in the Internet of Vehicles

Abstract

Vehicular edge plays a central role in ensuring an effective allocation of resources to provide services and applications. Resource allocation and communication in dynamic vehicular environments face numerous challenges in efficiently managing resources and data sharing, specifically managing the intricate balance of connectivity, storage, energy, computing, and cost of resources. These challenges are also affected by mobility, resulting in the demand for precision in communication range, density, and resource availability. Efficient resource allocation is a critical objective within vehicular networks, and to achieve this, intelligence, prediction, optimization, and incentive modelling are often employed. However, challenges persist, such as sporadic connectivity, transmission delays, and the inherent uncertainty of highly dynamic environments. In response to these challenges, this paper introduces the use of graph neural networks (GNNs) to learn hidden spatial and functional patterns in complex vehicular networks. Combining with clustering-based methodologies. This approach enables the intelligent organization of network nodes, reducing transmission delays and enhancing connectivity in dynamic environments. The resulting framework supports predictions and estimates based on evolving communication and mobility patterns. They are further improving the efficiency of connectivity and communications in vehicular edge networks. Using graph neural networks (GNN) and clustering techniques to address connectivity challenges, reduce transmission latency, and manage the inherent unpredictability of rapidly changing vehicular settings, this study is poised to enhance the delivery of services and applications in vehicular networks. It also lays the foundation for prospective research into resource management.