Mobility Adaptive Density Connected Clustering Approach in Vehicular Ad Hoc Networks

Abstract

Clustering is one of the popular topology management approaches that can positively influence the performance of networks. It plays significant role in VANETs. However, VANETs having highly mobile nodes lead to dynamic topology and hence, it is very difficult to construct stable clusters. More homogeneous environment produces more stable clusters. Homogeneous neighbourhood for a vehicle is strongly driven by density and standard deviation of average relative velocity of vehicles in its communication range. So, we propose Mobility Adaptive Density Connected Clustering Algorithm (MADCCA), a density based clustering algorithm. The Cluster Heads (CHs) are selected based on the standard deviation of average relative velocity and density matrices in their neighbourhood. Vehicle, which is having more homogeneous environments, will become the cluster heads and rest of the vehicles in their communication range will be the Cluster Members (CMs). The simulation results demonstrates the better performance of MADCCA over other clustering algorithms new ALM and MOBIC.