The varied types of Vehicular Networks Communication models (VNCM) circumscribe the decision-makers into a chaotic decision-making environment. The choice-making of VNCM is purely dependent on the attributes of traffic volume, vehicle density, road type, weather conditions, and application demands. However, the attribute of management techniques with the attribute values of dynamic routing, load balancing, and congestion control is more significant in choosing the desirable communication model. This challenging decision-making problem shall be resolved by applying the machine learning approach. This chapter proposes a matching model with supervised data that associates VNCM with management techniques and other core attributes. Supervised machine learning algorithms are applied to develop a matching model that best associates the VNCM with the management techniques considering vehicular efficiency. The performance metrics are computed to determine the most promising algorithm and to validate the consistency of the model. The results of the decision model facilitate the choice-making of the VNCM and also serve as the foundation for the construction of a predictive model.
Authors: N. Anitha, A. Manshath, Shaik Imran, B. Ranjitha, Manish Thakur, Mohit Tiwari
DOI: https://doi.org/10.4018/979-8-3693-6422-2.ch004
Publish Year: 2025
