## 2 Literature Review
The authors of [31] proposed using a federated learning agent misbehavior detection system (FLEMDS) and vehicle selection strategies to protect 6G-enabled vehicles against Sybil attacks. Due to the government's work on AI approaches, Sybil successfully stopped searching locally in cars. To increase the accuracy of the analysis, FLEMDS used a three-stage collection model at three different locations. FLEMDS uses state-of-the-art learning and software-defined cloud computing to reduce learning and discovery latency. FLEMDS involves sharing information for Sybil detection; there may be privacy concerns regarding the exchange of sensitive vehicle data. The author in [32] proposed an effective safety message transmission strategy that concentrates on metropolitan settings with high vehicle densities and mobility. The proposed system can reduce packet loss by considering active group pages and names from the active group control system. The distribution of safety information in the vehicle-to-vehicle environment is divided into group issues, common issues, as well as general safety messages. Due to the per-vehicle work request method and the RSU planning method, the system reduces work requests and messages sent from vehicles to RSUs on the construction site. For distribution on highway VANETs, the author of [33] proposed using a clustering method known as the optimal path routing protocol (OPRP) for warning messages. In a high-mobility environment, OPRP depends on mobility measurements to support cluster building, save transmission overhead, and maintain message authenticity. Additionally, the protocol considers cluster head communication to cut down on transmissions. For a steady and protracted cluster life, the cluster head is also selected using the median strategy based on an odd or even number of cars. With different traffic volumes and speeds, OPRP is contrasted with well-known plans. While cluster headscan help reduce transmission overhead, the communication between cluster heads and their member vehicles can introduce additional latency and overhead. The effectiveness of this communication depends on the selection of cluster heads and their proximity to the cluster members. In [30], the authors suggested a lower acceptance and publication statement. They compared the overhead of their method in terms of authentication and messaging with existing methods and analyzed the privacy and security of their method. However, as the number of messages received by the RSU in the network increases, the RSU's computational and communication costs related to authentication and propagation also increase. The effectiveness of authentication depends on a certain level of trust in the network. These trusts must be clearly defined and acknowledged. The author of [32] developed a Q-learning-based approach to determine the ideal locations and minimal number for rebroadcast zones. The combination of V2I broadcasts and vehicle-to-vehicle (V2V) rebroadcasts from these calculated zones enables the distribution of AMs across a wide region, even when there are places with weak wireless connections. The performance results demonstrate that the suggested Q-learning-based placement achieves great information coverage with minimal delivery delays. Additionally, collisions and pointless repeated rebroadcasts are prevented, conserving network resources. VANETs are characterized by dynamic network topologies due to vehicle mobility. The author of [33] proposed a method for disseminating data that uses a time barrier mechanism to reduce the number of overhead messages that may otherwise burden the network. The notion of a super-node to distribute the messages promptly forms the basis of the suggested solution. Additionally, the time barrier approach is modified to address this issue to prevent superfluous broadcasts, which may also result in the broadcast storm problem. Only the furthest vehicle can rebroadcast the message because it may go a greater distance.