Optimized Multi-Scale Mixed Dense Graph Convolution Network and Blockchain based Efficient Authentication Approach for Secure Data Sharing in Internet of Vehicles

As computation and communication technology advance quickly, the Internet of vehicles (IoV) is used more and more to fulfil the vision of intelligent transportation systems. However, a central unit for data processing and storage as well as mediators for wireless transmission are necessary for many Internet of Vehicle applications. Sensitive data leaks, steep expenses, and delays may result from this. In this article, Enhanced Multi-Scale Mixed Dense Graph Convolution Network and Blockchain depend Efficient Authentication Approach for Secure Information Sharing in Internet of Vehicles (MMDGCN-EAA-IoV) is proposed. The input images are collected from the real time images from AI cameras, sensors etc. Then, the input images are fed into Banyan Tree Growth Optimization (BTGO) for registering the identified vehicles in Road Side Unit. After registration, critical events are detecting and validating using MMDGCN. Fair proof-of-reputation consensus (FPoR) is used for preventing the privacy preservation of unauthorized access. The proposed MMDGCN-EAA-IoV technique is applied on Python. Then act of the suggested technique is compared by other current methods. The suggested technique reaches $\mathbf{1 6. 2 8 \%}, \mathbf{3 0. 7 8 \%}$ and 25.29% lower authentication latency when comparing with existing techniques such as secure IoV with decentralized consensus blockchain mechanism (SIoV-DC-BCM), blockchain-based lightweight authentication and key agreement scheme for internet of vehicles (BC-LAK-IoV) and authentication scheme for unmanned aerial vehicles based internet of vehicles networks (ASU-AV-IoVN) respectively.