Advancing VANET stability: enhanced cluster head selection with iTTM and weighted CRITIC

Vehicle ad hoc networks (VANETs) have garnered considerable attention for their potential to enhance road safety and facilitate advanced driver assistance systems. A fundamental aspect of VANET is the formation of stable clusters and cluster heads (CH) for improved network performance. Due to the dynamic nature of VANET and the different mobility of the vehicles, maintaining CH stability is significant. To address this issue, this study presents the network as connecting hypergraphs. The proposed approach uses an improved tensor-trace maximization-based spectral clustering algorithm (iTTM) and eigen heuristics to generate an optimal. The suggested clustering approach is followed by the CH selection based upon the four vehicle attributes: modularized link lifetime, connectivity level, relative speed, and consensus trust score. The multi-decision CRITIC approach will decide the CH in each cluster using those four attributes. These metrics have improved throughput and reduced packet delay to improve network performance. Simulation results, conducted in Delhi's Connaught Place using SUMO, demonstrate the proposed method's superiority in CH stability, throughput, and packet delay compared to existing algorithms. The state-of-the-art comparison is done on the criteria of CH stability, which comes out to be 84% in the proposed case and 82% in the previous work. The evaluation is done with recently published research on the CH rate of change and to validate the lower switching frequency in the network. The network is evaluated with different average speeds of the vehicles, and superior performance is noted in the proposed approach with the increase in lesser switching frequency with higher average speed than state-of-the-art.