## 2 Literature Review
A prediction system for a vehicle-to-everything network for the distribution of safety-based emergency messages was presented. Here, the hybrid Markov chain process with an inverse index model is used as the starting point of the vehicle trajectory prediction system. Depending on the vehicle's data connection, and to increase the time it takes to send the warning message, the proposed system can switch between V2V and V2I connections. Finally, the findings show that the vehicle's trajectory density information may be properly predicted. In [26], entities were not managed properly and were placed randomly, leading to a high packet loss ratio and computational complexities. The author in [27] suggested using an optimization approach for message distribution in VANET. Information distribution in VANET networks requires a good strategy to protect against situations affected by accidents or storms and to ensure high service levels. In this study, a network using cars as nodes was used. The goal is to send emergency notifications immediately. The identification method based on particle swarm optimization is both efficient and safe. In addition, improvements have been made in the method in terms of efficiency, packet loss, reducing end-to-end delay time, and reducing energy consumption. A particle swarm optimization algorithm is used to understand the optimal path but the algorithm cannot control negative edges; it performs the search blindly, affecting the visualization performance. In [28], the author suggested using dynamic clustering to distribute emergency messages to the best IoV location. To address the broadcast storm issue, this plan was built on a dynamic clustering technique that also used a novel cluster head selection mechanism. The section method also makes it possible to use the most stable vehicles as cluster heads, allowing for the efficient transmission of emergency information by preventing packet collisions. Finally, the simulation results show that by enhancing delay, packet delivery ratio, and throughput, their strategy minimizes collision and broadcast storm concerns. The roads were divided into clusters to predict the traffic density and optimize routing; this algorithm requires a large amount of time to detect the congestion, which leads to high latency. In this study, a cloud-based vehicle ad hoc network is used to distribute emergency messages securely. The type 2 fuzzy logic-based secure clustering (T2 FLSC) approach is used to dynamically cluster the cars, and it chooses the cluster head based on several factors, including travel speed, connection quality, trust factor, inter-vehicle distance, and neighboring node count. Additionally, the trust factor's inclusion aids in choosing the right CH for a secure data transfer method. A thorough outcomes analysis was carried out. Finally, the suggested model provides the highest throughput, packet delivery ratio, and key calculation time. Here, emergency message dissemination is limited to abnormal vehicles. However, other emergency messages (i.e., accident, ambulance alert) were not considered, leading to inefficient emergency message dissemination [29]. In [30], the author developed several rateless coding methods for message distribution in VANET. In the suggested study, a new protocol for data dissemination was created to reduce the negative impacts of problems and boost their effectiveness, providing a dependable method to provide mutual operating capabilities for V2V and V2I communications between nodes. To further minimize irregular connections, as well as address uncertainties in receiving information and collision issues, changing the messages sent by roadside devices over time and their influence on the network is recommended. The final set of findings demonstrates that the suggested system decreases the delay in disseminating messages between the vehicles, increases the number of delivery packets, enhances the range of data dissemination, and decreases the overhead of handshakes on average.