Innovative Application of 6G Network Slicing Driven by Artificial Intelligence in the Internet of Vehicles

Abstract

The rapid growth of vehicle networks in the Internet of Vehicles (IoV) needs novel approaches to optimizing network resource allocation and enhancing traffic management. Sixth-generation (6G) network slicing, when paired with artificial intelligence (AI), has enormous potential in this field. The purpose of this research is to investigate the use of AI-driven 6G network slicing (NS) for efficient usage of resources and accurate traffic prediction in IoV systems. A unique network design is suggested, combining data-driven approaches and dynamic network slicing. Data are acquired from vehicular sensors and traffic monitoring systems, and log transformation is used to handle exponential growth patterns like vehicle counts and congestion levels. The Fourier transform (FT) is used to extract frequency-domain information from traffic data, which allows for the detection of periodic patterns, trends, and anomalies such as vehicle velocity and traffic density. The Dipper Throated Optimized Efficient Elman Neural Network (DTO-EENN) is used to forecast traffic and optimize resources. This technology allows the system to predict traffic patterns and dynamically alter network slices to ensure optimal resource allocation while reducing latency. The results show that the suggested AI-driven NS technique increases forecast accuracy and network performance while dramatically reducing congestion levels. The research indicates that AI-driven 6G based NS offers a solid framework for optimizing IoV performance.