An efficient model for vehicular ad hoc networks using machine learning and high-performance computing

Vehicular ad hoc networks (VANETs) are recent advancements that permit vehicles to communicate with infrastructure and other vehicles, improving road safety and traffic efficiency. One of the difficulties in constructing and maintaining VANETs deals with the consequences of blockage, it may 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) model is developed to identify the impact of vehicle obstacles in VANETs. The proposed optimizable tree ML model showed better results in comparison to the other existing models. The results of the proposed model are superior as compared with other existing models in terms of nine performance measures namely, recall, specificity, balanced accuracy, accuracy, error rate, precision, F1 score, FNR and FPR. The values of these nine performance matrices for the proposed optimizable tree ML model are 0.99, 0.99, 0.99, 0.99, 0.01, 0.99, 0.99, 0.01, and 0.01 respectively.