Affiliations: [a] Department of Computer Science and Engineering, UIT, Bhopal, M.P., India
| [b] Department of Computer Application, UIT, Bhopal, M.P., India
| [c] Department of Information Technology, SGSITS, Indore, M.P., India
Correspondence:
[*]
Corresponding author. Ms. Poorva Shukla, Department of Computer Science and Engineering, UIT, Bhopal, M.P., India. E-mail: purvashukla@gmail.com.
Abstract: Recently, Vehicular Ad-hoc Network (VANET) has been one of the emerging fields of research. Many researchers are doing their research on various challenges of VANET. Congestion or blockage has become a critical issue in intelligent transportation systems, and this problem may arise daily due to the usage of smart technology in VANET. So we need some mechanism which controlscongestion. This paper present the trustworthy, long-lasting and consistent block chain congestion control mechanism using the heterogeneity of Dullening Nural Network (DNN), Q-Learning, and Software Define Network (SDN) model for an accurate result, fixed infrastructure, together with a correct prediction of congestion when it occurs at the edge of the network and give the fast and correct decision of congestion w.r.t VANET trust, Quality of service (QOS) and other vehicles current request. The focus of our research is on distributed SDN Technology and block chain technology for the development of smart cities and linked vehicles. So we proposed an inexpensive mechanism with low latency and a low bandwidth block chain system. Based on the Simulation result, our proposed architecture gives 82% and 98% reliability and efficiency gain in a congestion environment compared to traditional approaches. This paper aims to increase throughput, Packet Delivery Ratio (PDR), energy consumption time, and less end-to-end delay and routing overhead during communication.
Keywords: Edge computing, blockchain system, DNN, Q-learning, SDN
