The comparative analysis of velocity and density in VANET using prediction-based intelligent routing algorithms

Recently, VANETs are getting more attraction in both academic and industry settings. One of the challenging issues in this domain is routing algorithms. They become even more challenging, when they get benefit from intelligent solutions to predict the most stable node in the network to communicate with. There are several contributing factors which have influence on this process; including density, velocity, location, and distance. To the best of our knowledge, density, and velocity have the most impact on the precision of an intelligent routing algorithm for VANET. In this paper, we investigate how density along with velocity can affect two major classes of intelligent prediction-based routing algorithms in vehicular networks. These types of algorithms are divided into velocity-based and density-based groups. Different scenarios have been performed through NS2 in order to realize how each category of algorithms can be affected by both velocity and density at the same time. The obtained results are then illustrated in graphs based on delay and packet delivery ratio as routing performance indicators.