A High Stability Clustering Scheme for the Internet of Vehicles

In existing research on cluster head selection schemes in the Internet of Vehicles (IoV), designing a stable cluster structure poses a significant challenge. Choosing a centrally-located cluster head that can respond rapidly is crucial for meeting various requirements. To address the aforementioned challenges, this paper introduces a machine learning-based IoV cluster head selection scheme (HSCS). We introduce a new metric termed N-cycle Average Virtual Cluster Delay (XTn) for appropriate cluster head selection. To accommodate the high dynamism of vehicles, a machine learning model is integrated to predict cluster head selection metrics across different periods, and a set of cluster head selection guidelines is formulated. Experimental results demonstrate that our proposed HSCS ensures a relatively low average intra-cluster delay while maintaining a longer cluster head retention time, and it exhibits commendable robustness.