Anonymity and security improvements in heterogeneous connected vehicle networks

The connected vehicle network is an important part of intelligent transportation since it provides a wide variety of online vehicle services and helps to lower the risk of accidents for drivers while also being a prominent example of the IoT inside futuristic smart vertical networks. However, the connected vehicle network's communication protocols reveal a great deal of private data, including individual vehicles' whereabouts and travel plans. Sensitive data, including vehicle positions and trajectories created during communication, must be protected as wireless communication technology by its very nature entails broadcasting and allows for open communication between vehicles. Therefore, a key objective in vehicular network security is to increase the anonymity of vehicle identities within secure services. Using a batch verification technique, we present a novel approach to anonymous identity authentication in this research. The approach uses zero-knowledge proofs and other forms of anonymized authentication to bolster the privacy of IEEE WAVE's security services. It also provides a new way to retrieve lost identification with the help of an independent party. This study primarily contributes to the improvement of privacy in IEEE WAVE security services and applications such as near-field vehicle payment and DSRC security services by proposing an improved anonymous authentication system based on batch verification and zero-knowledge proofs. According to experimental findings, the recommended method has a lower computational overhead than a number of competing systems up until the batch of signatures that have to be checked contains more than eleven. Furthermore, we suggest the optimal batch processing verification cycle, tailored to the scheme's use in the near-field vehicle payment and basic safety message (BSM) use cases of DSRC.