## 2 Related Work  
VANETs have recently been deployed to increase road safety and traffic control efficiency. Vehicles collaborate in VANETs to communicate warning messages and road conditions, considerably improving road safety. In [7], a new architecture was proposed to mitigate many VANET attacks while maintaining data privacy and security. The suggested architecture uses timestamps and hashing techniques to keep the messages transmitted fresh. The trustworthiness of any node that injects false data into the VANET will be reduced significantly. In [8], the authors proposed a trust-based framework that allows users to ignore false messages from misbehaving nodes. A reputation system for platoon head vehicles is developed, in which data are collected from the VANET's nodes, and an iterative filtering technique is used to reject false feedback from misbehaving nodes. A new VANET antiattack trust scheme was introduced in [9], where nodes can use it to evaluate the trustworthiness of new nodes in VANETs. Bayesian inference is used to calculate the node's local trust based on the node's previous interactions. After that, a small group of seed nodes is selected to evaluate the global trust of all the nodes in the VANET. In [10], the proposed security system uses a Software-Defined Vehicular Network to develop a trust management architecture for VANETs. Specifically, trust between vehicles and trust between nodes and infrastructure are used to model and evaluate the trustworthiness of the node. The authors proposed an AI-enabled trust management system (AIT) in [11] to address the security challenges for VANETs. Nearby vehicles validate each message in the VANET using deep learning algorithms. The RSU excludes untrustworthy vehicles after validating the node's authenticity using the blockchain technique. A novel security paradigm was proposed in [12] to enable vehicles to judge whether data are trustworthy. Furthermore, the Pretty Good Privacy (PGP) framework handles the certificates and associated trust level. The authors proposed a new authentication scheme for cluster-based VANETs in [13]. Nodes in VANETs are divided into clusters, and the trust degree is computed for selecting the cluster head. The sender digitally signs the messages, which are then encrypted using a public/private key provided by a trustworthy authority and decrypted by the receiver. The authors in [14] proposed a new trust-based security scheme for securing message exchange in a VANET. The VANET Grouping Algorithm (VGA) was presented to group nodes into clusters and elect cluster heads. After dividing the VANET into clusters, a trust management scheme is used to evaluate the reputations of vehicles. In [15], a new trust management-based scheme was proposed. The trust degree for each node is computed for authentication purpose. The authors in [16] described a new framework for efficiently delivering messages between vehicles and RSUs. Blockchain technology is adopted in the proposed framework to support data integrity, traceability, and reliability features that traditional reputation systems fail to provide. In [17], the authors suggested a novel dynamic entity-centric trust model. Trust is calculated based on weight dependence, the type of applications, and node authority levels. The results showed that the proposed trust model improves the security of the GPSR routing protocol with minimal delay and increases the success rate of data delivery. The authors in [18] proposed a new trust management system based on federated learning. The trust level for each node is computed firstly to improve the accuracy of the collected data. A state-of-the-art protocol was proposed in [19] for securing communication in VANETs. Elliptic curve cryptography is adopted in the proposed system to assure communication security at no additional expense. Policy rules were used in [20] to assess the trustworthiness of data and IoT devices in smart cities based on reporting history and data context.