The current trust computation scheme in Internet of Vehicles, according to the adopted decision logic, can be divided into different approaches based on multi-weight fusion, Bayesian inference (BI), the Dempster–Shafer (D-S) theory, fuzzy logic, and three-valued subjective logic (3VSL), etc.
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 
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 380TB-4PB of data 
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 help vehicles 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 
. 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 
. 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 
. Furthermore, the quality of the data depends on the reputation of the vehicles 
. 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) 
. 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. Therefore, social networks can be established through frequent communication. However, there are still many vehicles that travel irregularly, such as the ride-sharing cars (Uber, Didi, etc.) and transportation vehicles that require driving across districts or cities, making it difficult for them to establish a frequent and stable communication network. The historical database of these kinds of cars will cause a huge waste of computing power and storage space. In VANET, the different preset reputation levels of vehicles, the degree of impact of reported events, the quantified observation values of the reported and verified credibility of events, and the calculation methods of various parameters are all key issues to be considered in designing the model. In addition, characteristics of VANET, such as the real-time and high mobility make traditional solutions difficult to implement.
The emergence of blockchain technology has provided a large number of innovative solutions for the security issues of VANET. Blockchain is a novel exploration of network world operating rules and technologies 
. Following the creation of a new typical cryptocurrency.
Bitcoin was proposed 
, and scholars noticed the advantages of the blockchain in the realization of security. In blockchain-based networks, data are stored in blocks in the forms of distributed ledgers, and are protected cryptographically. Each node can hold a full/partial copy of the blockchain. By combining the reputation evaluation and management model with the blockchain, using its advantages of decentralization, irrevocability, traceability, transparency, autonomy, and anonymity, the problems existing in the model can be well solved. At present, some researchers have tried to combine blockchain technology with the VANET reputation management model, but there are still the following unsolved challenges.
2. Trust Computation
The current trust computation scheme, according to the adopted decision logic, can be divided into different approaches based on multi-weight fusion 
, Bayesian inference (BI) 
, the Dempster–Shafer (D-S) theory 
, fuzzy logic 
, and three-valued subjective logic (3VSL) 
, etc. Bayesian inference describes the uncertainty of data-centered modeling and reasoning based on probabilities and statistics 
, which is more suitable for the quantitative judgement of interactive trust in the VANET scenario.
According to how trust is calculated, it can be divided into direct trust 
and a combination of direct and indirect trust 
. In addition, social metrics 
are also added based on driver behavior.
, Zhang et al. proposed a trust management model based on the trustrank algorithm, which considers both local trust and global trust. Local trust is obtained by applying the Bayesian inference model to past interactions of vehicles. In the evaluation of the system model, new-user attacks, on-off attacks, and collusive attacks are considered.
Fang et al. 
proposed a trust management model using Bayesian inference to prevent on-off attacks. The trust calculation combines weighted direct trust and indirect trust. The attack identification window is defined according to the interaction between the trustor and the trustee, and the vehicle’s credibility is judged according to the number of switches before reaching the highest and lowest scores. If the vehicle exceeds the predetermined threshold, it is marked as malicious. The model is simulated in MATLAB.
Talal et al. 
proposed a blockchain-based decentralized trust model based on Bayesian inference, taking into account the quality of direct interactions between vehicles. It is mainly targeted at new-user attacks, based on punishment strategy to prevent malicious vehicles from gaining higher trust points by frequently leaving and joining the network. Meanwhile, an incentive scheme is proposed to encourage cooperation between vehicles. The scheme performance is analyzed in MATLAB.
In addition, the main calculation factors considered in the calculation are the weight quantization and threshold setting, that is, the determination of whether or not the vehicle is trustworthy according to the trust value calculated. The concept of weights is often applied when aggregating the trustee’s final trust score, where different contribution parameters are assigned different weights according to their contribution/importance in the final trust value calculation. The determination of the exact value of these weights is essential. In 
, two weight calculation methods of three parameters, involving similarity, familiarity, and grouping delivery ratio are proposed, and the simulation verifies that the results produced by the weight calculation methods are more accurate than using the mean value directly.
The threshold setting is designed to identify misbehaving entities in the IoV and detect malicious vehicles by adopting a preset stable threshold. In 
, the vehicle with a trust value greater than the threshold is a trusted vehicle, while a vehicle with a trust value lower than the threshold is a malicious vehicle. However, authors do not consider the dynamic characteristics of VANET. A fixed threshold cannot resist on-off attacks. In 
, Mahmood et al. proposed the adaptive threshold technology to effectively reduce the on-off attack. However, the trust management model is quite computer intensive and has efficiency issues.