Adaptive Clustering Based Hybrid VANET Accommodating Adaptive Data Rate for Performance Enhancement

Abstract

The Vehicular Ad-hoc Network (VANET) is the backbone of today’s Intelligent Transportation System (ITS). The classes of applications for communication between vehicles range from time critical safety applications to delay tolerant Internet connectivity applications. The communication performance depends on how efficiently and timely the data delivery takes place in the network. Fast topology change and frequent disruptions due to highly mobile nodes of VANETs are the main challenges of VANETs. This thesis implements adaptive clustering of vehicles to improve the data delivery performance of the system. The design of cluster has significant impact on performance, which requires the analysis of physical layer channel condition and MAC operation at data link layer. This thesis analyzes the different propagation model along with the adaptive data rate on physical layer channel condition on real time traffic scenario. The 3G network is also deployed to send the sensitive packet without delay and to improve the system performance. The system performance is evaluated on the basis of throughput and packet delivery ratio. The throughput and packet delivery ratio are found to be better for Nakagami model than Rayleigh model. The clustering based VANET has been found to perform better than normal VANET in case of large number of nodes. The algorithm development and simulation are carried out in NS3 with real time traffic mobility scenario generated by Simulation of Urban Mobility (SUMO).