QOS aware and secured routing algorithm using machine intelligence in next generation VANET

Abstract

Vehicular ad-hoc networks have been emerging as an inevitable research topic in recent years due to its applications in Intelligent Transportation System. Establishing end-to-end communication and high authenticate information transfer in vehicular networks remains to be a real challenge due to the dynamic topology change, vehicle interspacing, high vehicle density and non-intelligent authenticated routing mechanisms. To address these aforementioned, a unique stochastic based adaptive routing integrated with secured 3D logistic chaotic maps and traffic flow predictions has been developed and referred in this paper as Stochastic Chaos based Adaptive Routing with Prediction (SCARP). The SCARP can perform the good traffic flow predictions using strong deep learning networks which recommends a stochastic based node discovery routing principle. This approach has reduced connectivity loss, minimized delay and as well provides chaotic authentication to form an efficient and safe data transmission among the vehicular nodes. The performance testing of this proposed method is done with a few months of traffic data obtained from the layout Puducherry U.T, India. These experimentations are carried out in SUMO-OMNET++ environment and compared with existing learning and routing algorithms. Simulation results demonstrates that the proposed algorithm has outperformed the other existing algorithms in terms of prediction accuracy, sensitivity, average delay, packet delivery ratio. Also the transmission performance of the proposed algorithm has more robustness with the high intensity DoS attacks.