A Clustering Model Based on Density Peak Clustering and the Sparrow Search Algorithm for VANETs

Cluster-based models have numerous application scenarios in vehicular ad-hoc networks (VANETs) and can greatly help improve the communication performance of VANETs. However, the frequent movement of vehicles can often lead to changes in the network topology, thereby reducing cluster stability in urban scenarios. To address this issue, we propose a clustering model based on the density peak clustering (DPC) method and sparrow search algorithm (SSA), named SDPC. First, the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads (CHs). Then, the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter. Finally, cluster maintenance strategies are considered to tackle the changes in the clusters’ organizational structure. To verify the performance of the model, we conducted a simulation on a real-world scenario for multiple metrics related to clusters’ stability. The results show that compared with the APROVE and the GAPC, SDPC showed clear performance advantages, indicating that SDPC can effectively ensure VANETs’ cluster stability in urban scenarios.