EtHgSC

This chapter presents the Eigen trick-based Hypergraph Stable Clustering algorithm (EtHgSC), which has a two-fold scheme for stable clustering. The communication strategy of vehicles in a smart city is crucial, but vehicular communication faces issues with scalability. Clustering can help with vehicular ad hoc network (VANET) problems, but clustering in VANET faces stability problems due to the rapid mobility of the vehicles. This work introduces a novel and efficient EtHgSC algorithm that uses the “Eigen trick” method to partition both vertices and hyperedges. This partitioning reduces the computational complexity of clustering and helps achieve high stability for VANET.

The suggested system has two schemes for steady Cluster Head (CH) selection. The first section of the suggested system handles cluster generation. The CH is selected in the second part, taking into account the need for stable connections with most neighbors. The JCV method follows our proposed EtHgSC method in terms of stability, as the two methods solve the problem of CH stability at junctions by preventing frequent cluster breakage.

Additionally, the suggested plan introduces a vehicle time-to-leave metric to increase 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 in terms of CH lifetime, CM lifetime, and CH change rate. Furthermore, the suggested plan results in a significant decrease in packet latency.

Author(s) Details:

Mays Kareem Jabbar,
Faculty of Engineering, University of Misan, Al Amarah City, Misan Province, 62001, Iraq and CES_Lab, Ecole National d’Ingénieurs de Sfax (ENIS), Sfax University, Tunisia.

Hafedh Trabelsi,
CES_Lab, Ecole National d’Ingénieurs de Sfax (ENIS), Sfax University, Tunisia.

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