# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 1 Introduction  
IoV is a typical application scenario for IoT technology. With the help of the next generation of information and communication technologies and the rapid development of various high-tech devices equipped with intelligent vehicles, such as GPS, radar, and on-board equipment, vehicle nodes can be connected to everything (V2X) through various types of networks to build intelligent transportation systems (ITSs) and realize the vision of the smart city. VANET [1] can be seen as a subset of IoV, which mainly focuses on short-time, real-time, and short-distance communication formed by the interconnection between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). In VANET, complex and dynamic data generated by vehicles, human beings, and the environment are communicated and processed in real-time. Data include traffic conditions, traffic accidents, road construction, congestion, etc. It is estimated that about 380 TB-4 PB of data [2] can be generated annually for the VANET network worldwide, which includes plenty of sensitive information. Due to sensitivity, the security and reliability of the data content is critical to the performance and quality of the delivered services. Reliable information can helpvehicles find the fastest route, avoid collisions, etc. Unreliable data can even lead to serious traffic accidents. Meanwhile, VANET uses wireless and heterogeneous media for transmission and is more vulnerable to malicious attacks. Some researchers have carried out comprehensive surveys for VANET security, and pointed out new security concerns and issues in VANET that urgently need to be solved [3,4]. Therefore, the protection of VANET data security is crucial. Traditional security solutions, such as key pairs, digital certificates, and public key infrastructure (PKI), refer to secure communication schemes for other types of networks and are mainly oriented to entity authentication. They are only applicable to identify malicious entities [5,6]. Nevertheless, in VANET, vehicle nodes are essentially controlled by people, and the entity security of vehicle nodes cannot guarantee the reliability of the data given by the nodes. The consequences of untrustworthy information sent by the perpetrator through legally certified vehicles will be severe. At the same time, due to the high mobility and short-term connectivity of VANET, if there are no appropriate countermeasures, it may be too late for victims to find that the message received is malicious. Malicious vehicles may have been disconnected from the network after committing crimes. Therefore, the protection of vehicle network security should focus specifically on the data. In order to eliminate internal attacks from VANET, researchers use reputation management models to evaluate the reliability of interactions based on the behaviors and information propagated in the network. As mentioned in previous studies, reputation is the general view of the public on the credibility of others [7]. Furthermore, the quality of the data depends on the reputation of the vehicles [8]. Therefore, the reliability of information given by vehicles can be evaluated by quantifying the reputation of vehicles. Trust refers to the measure of the credibility of the interactive content of nodes, and it is the subjective expectation of one node to another node based on its own experience and preference. Trust can be seen as a short-term evaluation, while reputation is a long-term concept based on trust. Previously, researchers extended the social network to the vehicle network, forming the vehicle social network (VSN) [9,10]. In a VSN, vehicle nodes are connected to each other through mutual interaction, and a tightly coupled self-organizing virtual network is formed. It can be used to manage trust between nodes. In practical VANET scenarios, the establishment of the VSN has a track to follow. A large number of vehicles maintain a relatively stable trajectory while driving to/from work, school, gym, supermarket, etc.