Modified C-DRIVE: Clustering based on direction in vehicular environment

Abstract

Efficiency applications in VANETs are focused on increasing the productivity of the road resources by managing the traffic flow and monitoring the road conditions. The performance of most such applications is dependent on an effective density estimation of the vehicles in the surroundings. Of the various methods, clustering demonstrates to be an effective concept to implement this. However due to high mobility a stable cluster, within a vehicular framework, is difficult to implement. In this work, we propose a new clusterhead election policy for direction based clustering algorithm C-DRIVE. This policy facilitates to attain better stability and thus accurate density estimation within the clusters. Simulation results show that the C-DRIVE is rendered stability through new clusterhead election policy by electing fewer clusterheads in the network. This supports for a better accuracy in density estimation with fewer overheads.