Machine learning based cluster formation in vehicular communication

Nowadays vehicular communication has become a widespread phenomenon, which will cause spectrum scarcity. By utilizing the cognitive radio in vehicular communication can be an effective solution for communication between vehicles. However, it requires robust sensing model for its efficient usage. Hence, vehicles sense the spectrum and deliver their sensed information to the eNodeB. For spectrum sensing, numerous number of vehicles can bring up overhead for the eNodeB. Grouping the vehicles into clusters is one of the most effective method to lower the burden for eNodeB. We have proposed a novel clustering method to enhance the performance of vehicular communication. The proposed method has formed the clusters using artificial intelligence. Our proposed method achieves the highest performance by forming a best group of cluster heads and by selecting finest cluster members using machine learning. The maximized throughput is achieved using proposed method for vehicular communication. Moreover, the clusters are formed in such a way that highest energy efficiency is attained.