Bi-linear mapping integrated machine learning based authentication routing protocol for improving quality of service in vehicular Ad-Hoc network

Abstract

Recently, most of the critical problems of real-time applications have been solved by one of the fast and most hastily growing technological tools, Machine Learning. The novel architectures of Vehicular Ad-hoc Networks reduce traffic congestion and accidents since the number of electric vehicles is increasing daily. There is a need for an efficient and secured routing protocol for better implementation of VANET. Machine learning algorithms help in achieving such efficiency and security. This paper develops a Novel Machine Learning based Authentication Routing protocol by integrating the Long-Short-Term-Memory model with the Bi-Lear Mapping algorithm to analyze the routing data and authenticate the network users. The bi-Linear Mapping model assigns and compares the node's private and public keys, whereas LSTM analyses the routing table information. The proposed model is simulated in NS3 software, and the performance is verified. The results show that the proposed model obtained better throughput, PDR, energy efficiency, authentication which is better than the earlier methods.

Keywords

• Vanet
• Mlar
• Blm
• V-2-V
• Authentication
• Routing protocol