An Overview on Eigen Trick-based Hypergraph Stable Clustering (EtHgSC) Algorithm in VANET

Thic chapter presents the Eigen trick-based Hypergraph Stable Clustering algorithm (EtHgSC), which has a two-fold scheme for stable clustering. A smart city's vehicular communication strategy is important. A significant problem with vehicular communication is scalability. Clustering can help with vehicular ad hoc network (VANET) problems; however, clustering in VANET faces stability problems because of the rapid mobility of the vehicles. This work introduces a novel efficient Eigen trick-based Hypergraph Stable Clustering algorithm (EtHgSC) to achieve high stability for the VANET. There are two schemes in this algorithm for steady CH selection. The cluster generation is handled by us in the first section of the suggested system. The ‘‘Eigen trick” method is used to partition both vertices and hyperedges, which provides an approach for reducing the computational complexity of the clustering. The Cluster Head (CH) is chosen in the second part, taking into account the requirements for keeping a stable connection with most neighbors. Compared to the most common clustering algorithms in the literature, the JCV method follows our proposed EtHgSC method in terms of stability, because the two methods solve the problem of CH stability at junctions by preventing the frequent cluster breakage. In addition to relative speed, neighboring degree, and eccentricity that are used to select the CH, the vehicle time to leave metric is introduced to increase the CH stability. Every vehicle is given a score using the gray relational analysis model, and the CH is chosen based on the vehicle with the highest score. The outcomes demonstrate the superiority of our suggested system with respect to CH lifetime, CM lifetime, and CH change rate. Furthermore, the suggested plan accomplishes a significant decrease in packet latency.