A High Stability Clustering Scheme for the Internet of Vehicles
Chen Chen, Jiabao Si, Huan Li, Wei Han, Neeraj Kumar, Stefano Berretti, Shaohua Wan
Abstract
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.