# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Trust Models  
#### 3.2.1 Classification of Trust Models  
IoV trust management mainly includes the establishment and evaluation of trust models. According to different evaluation objects, trust models can be divided into three categories, as shown in Figure 6: the entity-based trust model (ETM), the data-based trust model (DTM), and the composite trust model (CTM). * _Entity-based Trust Model (ETM)_ The purpose of ETM is to evaluate the credibility of vehicle nodes. Usually, a trust evaluation system can be built by using direct or indirect trust between vehicles, and the trust value of each node can be calculated so as to detect untrustworthy vehicles. [PERSON] [30] et al. put forward a trust model based on the feedback data of users' vehicles  
Figure 6: Classification of trust models. and evaluated the credibility of the first vehicle in the vehicle arrangement scenario, so as to help the user's vehicle to choose a reliable vehicle to follow. In [12], the social attributes of nodes are used, and three social trust relationships, direct neighbor trust, indirect neighbor trust and friend trust, are considered. The trust degree of nodes is calculated by weighted average. In [32], the main difficulty of an entity-based trust model maintaining the trust relationship between vehicles lies in how to collect enough information to evaluate the trust degree of nodes, especially when vehicles have just joined VANET. Additionally, when there is not enough interactive information, or there are few connections between vehicles and short communication links, e.g., in suburbs with sparse vehicles, these factors can make it difficult to evaluate trust effectively. * _Data-based Trust Model (DTM)_ DTM mainly evaluates whether the received data are reliable. The model needs to collect messages from various information sources, such as neighbor nodes and RSU, and filter out untrustworthy data to accurately verify the reliability of the received data. [PERSON] [11] established a distributed trust mechanism based on a direct experience survey between neighboring vehicles according to VANET's characteristics. Every vehicle first detects whether the received data are reliable, and then, it distributes the trust value to all of their neighbors. [PERSON] [24] proposed using the received signal strength (RSS) and the geographic location (GPS) of the vehicle to evaluate the trust level of the received message, combining the Bayesian estimation evaluation algorithm with the determined distance calculation method to provide better evaluation results, thus helping to identify malicious message data. The main disadvantage of DTM is that the trust relationship between vehicles can never be formed, and only a short-term trust of the received data can be established. Because data trust is based on events, it is necessary to build the trust relationship for each event again and again, and the previous trust data have not been used. At the same time, when the number of received messages is insufficient, it is difficult to judge the accuracy of the messages. * _Combined Trust Model (CTM)_ CTM is equivalent to the combination of the above two trust models. This model can not only evaluate the reliability of vehicles but also calculate the reliability of data. Generally, the two trust models are interrelated, that is, the trust value of a node affects the credibility of data to a certain extent, and the trust value of data in turn reflects the credibility of a node. An anti-attack trust management scheme (ART) was proposed by [PERSON] [11]: in this system, malicious attacks can be detected and responded, and the credibility of data and mobile nodes in VANET can be evaluated. Data trust evaluation is based on feelings and the data collected by multiple vehicles in two dimensions of node trust evaluation, namely function trust and recommendation trust. [PERSON] [33] put forward a cluster-based hybrid VANETs trust management scheme, which can not only classify the information in the cluster but also identify malicious vehicles in real time.