# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 4 Design of the DBREMM  
### Global Description and System Specific Settings  
In VANET, various types of information are exchanged in V2V and V2I, including traffic jams, road construction, accidents, collisions, weather alerts, etc. Due to the wireless and heterogeneous communication mode in VANET, information transmission can never be error-free. Therefore, many malicious attacks, including forgery, tampering, discarding, replay, etc., are difficult to distinguish from interference and noise. Quantifying the trust value of vehicle nodes is a good way to solve the data security problem of VANET. When vehicle nodes provide event reports, the trust value of nodes will be generated according to their report contents, which will be permanently stored in the reputation chain after being confirmed by consensus, and gradually accumulate into the historical reputation of the nodes. In special scenarios (for example, when traffic is heavy or in remote areas that lead to few interactions of nodes), the vehicle with the highest historical reputation can be directly selected to request for certain services. As trust is generated in a single interaction, it is not universal. In addition, the vehicle node is essentially controlled by human beings, so social factors should also be taken into account. Therefore, in our reputation evaluation model, the new global reputation \(R_{i}^{t}\) is defined as:  
\[R_{i}^{t}=\mu_{dec}R_{i}^{t-1}+(1-\mu_{dec})T_{i}+\mu_{adj}R_{i}^{social} \tag{1}\]  
where \(R_{i}^{t}\) is the latest updated reputation value, \(R_{i}^{t-1}\) is the accumulated reputation value of the previous period, \(T_{i}\) is the quantized trust value obtained through the reporting event \(W_{i}\), \(R_{i}^{social}\) is the measure of social factors, and \(\mu_{dec}\) and \(\mu_{adj}\) are the quantized weight. Specific values and calculation methods of each function will be described in the corresponding sections.  
Figures 2 and 3 show the overall workflow and detailed framework of the reputation evaluation scheme processes to implement the DBREMM, respectively. The specific calculation process of direct trust and indirect trust is discussed in Sections 4.2 and 4.3. Section 4.4 introduces the calculation of overall trust and the updating scheme of the reputation value, and Sections 4.5 and 4.6 introduce the double-layer blockchain architecture and the process of storing information in the chain. Firstly, we define some qualifications and initial assumptions of the scheme as follows.  
1. In our proposed system, the TA and RSU are trusted and will not be attacked. All digital certificates provided by the TA and information provided by the RSU are true and accurate;
2. Each vehicle node has a unique pseudonym authenticated by the TA, as well as a key pair and digital certificate. In each communication between vehicle nodes and the RSU, the pseudonym of nodes has been authenticated. That is, in this paper, the entity security of nodes is not considered, but the data security of information given by the nodes is focused upon;
3. Since the scheme designed in this paper is for VANET and does not emphasize specific usage scenarios, the difference of vehicles and the different type of each event are not set. The setting of various threshold values and weight factors mentioned in the subsequent formulas in this section are preset within a reasonable range without special settings.  
The key notations used in this paper are listed in Nomenclature, at the end of the article. Other notations that have only been used a few times will be explained when they appear.  
Figure 3: Detailed frameworks for the DBREMM.  
Figure 2: Overall workflow for the DBREMM.