Machine Learning-Powered Blockchain in Vehicular Ad-Hoc Networks

A significant increase in vehicles connected to the vehicular ad-hoc network is causing impacts due to the amount of network traffic. To handle this increase, there is a move from centralised to decentralised technology. Changing requires a new approach to trust between connected vehicles when exchanging information. Malicious connections present a risk to traffic safety and management. Deploying blockchain technology builds trust between vehicles partially addressing the problem. A gap is found in analysis, detection, and management of security vulnerabilities when malicious behaviour occurs. Combining machine learning with blockchain technology is a potential solution. There are several ways this technology can be implemented each offering different advantages and disadvantages. The purpose of this research is to determine how the use of machine learning and blockchain may be combined to deliver a safer vehicular ad-hoc network and aims to provide recommendations on the best implementation approach. A literature review is performed using Q1 research papers and the combination of blockchain and machine learning is found to be too broad. The report concludes stating a standout solution was not found and more research is needed. Future work is recommended to consider volumes is also highlighted as it is unclear how several solutions will work in a regional and rural setting.