Leading the Way: Reducing network traffic in vehicular Ad Hoc networks through cluster leader algorithms

Abstract

The escalating data traffic from the growing number of connected vehicles equipped with sensors leads to significant challenges for communication resources and the shared service infrastructure of Vehicular Ad hoc Networks (VANETs). To tackle these challenges, traditional clustering algorithms such as K-means, Fuzzy C-means and DBSCAN have been used to group vehicles into manageable clusters. By organizing vehicles into clusters, these clustering algorithms select a representative subset of vehicles within each cluster to handle data communication, minimizing redundant transmissions and ensuring efficient data dissemination, thereby significantly reducing network congestion. However, relying solely on a subset for data transmission may be insufficient, as this approach can still generate substantial data. Furthermore, if the subset is geographically dispersed, it can lead to a loss of accuracy in data representation and communication. To address these limitations, the Leader Election Algorithm for Representation Identification in Cluster (LEADER) is introduced to designate a representative leader within each cluster, enhancing data transmission. LEADER aims to establish a message control mechanism within VANETs, optimizing data transmission and reducing communication overload. The experimental performance evaluation demonstrated that LEADER reduced network traffic data by up to 45% on average, while maintaining accuracy in representing groups.