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Top1. Introduction
It is estimated that the number of registered vehicles will reach 2 billion within the next 10 to 20 years (Jia et al, 2015). With the rapid increasement in the number of vehicles, the development of Vehicular Networks has attracted more and more attentions in recent years. However, the security issues of Vehicular Networks still exist and hinder its development (Contreras-Castillo et al., 2017; Indu, 2019; Lo and Tsai, 2007). In order to improve the security of the whole process of information transmission and sharing, the best method is to ensure the reliability of the devices in Vehicular Networks, such as forming a trusted execution environment (Gurung et al., 2013). Blockchain technology has shown great potential to solve these problems (Yang et al.,2019; Huang et al.,2020; Kang et al.,2019).
Nevertheless, the characteristics of the Vehicular Networks are different in the cryptocurrency. Firstly, Bitcoin and Ethereum can only handle 7 and 15 transactions per second respectively. They onlymeet the need of the reputation transactions in Vehicular Networks. Second, the participants in cryptocurrency’s transactions are deterministic and the transactions can be confirmed by the signatures of every participants. In contrast, the transactions in Vehicular Networks are the addition or subtraction of reputation score based on the vehicle’s action of sending or forwarding messages, so they are determined by the neighbors’ feedbacks about the message (Moreira, E.,2019). Lastly, the cryptocurrency transactions should exist permanently but of the vehicles’ reputation could be deleted because of some reasons such as the scrappage.
Considering the security issues and the abovementioned problems of the Vehicular Networks, we proposed a Vehicular Networks reputation system on Hyperledger Fabric2.0 (Fabric, H.,2019). In our system, a vehicle gets a reputation score from the actions verified by its neighbors and is punished by the selfish or malicious actions through lowering its reputation scores. Given in the scenario of a crash, a vehicle broadcasts this message to other vehicles and RSUs. RSU receives such message and opinions from other vehicles on this message, then calculate the accuracy score and decide whether it is true. If the affirmative score from the neighbors exceeds a certain threshold, a certain reputation score will be given. RSU will reduce reputation scores when the vehicles do some selfish or malicious actions leading the negative opinions from the neighbors exceed a certain threshold. After confirmation, these transactions will be recorded in the Blockchain through the Hyperledger Fabric project.
In Vehicular Network, some selfish nodes are reluctant to report events and evaluate the message it received because of resources or power constraint. This will severely hinder the development of the Intelligent Transportation System. Therefore, we design an effective incentive mechanism to encourage vehicles to send a message as soon as a road-related event is found and to make an objective evaluation of the authenticity of the message. Indu et al. (2019) speculated that a reputation system that combines data-based and entity-based would be best suited for Vehicular Networks. To improve the accuracy of verifying whether the message is correct or not, we combine the data-based method with the entity-based method together. The reputation score used to show the trustworthiness of a vehicle will create a very secure environment in Vehicular Network, but there is no specific calculation method of reputation value so far, we propose an AIMD (Additive Increase Multiplicative Decrease) method to calculate the reputation score. To preserve the privacy information of user’s identity, we store and share the reputation scores and related behaviors of vehicles in a distributed Consortium Blockchain.
The rest of the paper is structured as follows: A review of related works on reputation system in Vehicular Networks and the Blockchain technology in Vehicular Networks’ security is presented in Sections II-a, II-b respectively. Section III describes the system model. Analysis on the system performance and security issues has been presented in section IV. Section V presents the details of the experiment. Moreover, the conclusion is contained in Section VI.