AIIV-Net: AI-Integrated Intent-Based Vehicular Networking Framework for Dynamic IoT Resource Orchestration in 6G-V2X Environments

Internet of Things (IoT) and vehicle-to-vehicle (V2V) communication technologies are rapidly increasing, and an optimal resource allocation and network performance goal would be achieved using an intelligent, efficient, and scalable communication framework. The proposed intent-based V2V networking framework that includes AI in the paper will handle the flaws found in communication and the management of resources during V2V networks that are facilitated by the Internet of Things (IoT). The framework develops on the concepts of Intent-Based Networking (IBN) and Artificial Intelligence (AI), a new solution that addresses the issue of communication efficacy, resource management , and network optimization in highly dynamic heterogeneous systems. IBN enables the abstraction of the network coordinates and policies, and then resources can be allocated in a more dynamic and adaptable way depending on the various demands of V2V interactions. The help of AI as a part of this scheme makes it possible to involve intelligent decision-making, which allows the system to make predictions regarding network conditions and how to distribute and manage resources in a manner that provides a consistent communication channel between automated vehicles and IoT devices. The suggested framework has the capability of dynamically responding to traffic conditions, environmental conditions, and shifting communication loads, hence improving V2V communication performance within smart cities and connected surroundings. A large number of simulations and tests to evaluate its performance are carried out to demonstrate how the AI and IBN-included V2X communication system will optimize traffic flow and safety in ITS in urban areas. As compared to generic V2V communication frameworks, where latency and energy consumption are approximately 150 ms and 150 J to 100 J and 100 J, respectively, the proposed IBN-based V2X framework consumes considerably less energy (50 ms and 50 J) in terms of latency in the energy consumption.