An Efficient Model for Vehicular Ad Hoc Networks using Machine Learning and High- Performance Computing

Abstract Vehicular Ad Hoc Networks (VANETs) are recent advancement that permits vehicles to communicate with one another and with infrastructure, improving road safety and traffic efficiency. One of the difficulties in constructing and maintaining VANETs is dealing with the consequences of blockage, which can occur when buildings, trees, or other obstructions block radio signals between vehicles. However, the presence of vehicles as obstacles can severely impact the performance of VANETs. In this paper, an efficient machine learning (ML)-based technique is used to identify the impact of vehicle obstacles in VANETs. The proposed Tree-based models showed better results in comparison to the state-of-the-art models in all the tests conducted. The findings of the proposed model outperform the existing models and demonstrate that the proposed models can precisely predict and classify data, which makes it an important tool for various applications where accurate classification is crucial.