## 2 Related Works
### Optimization-Associated Research  
Suggested in [30] is a novel approach to improve the packet delivery ratio of the network called reliability aware multi-objective optimization using the enhanced Gaussian mutation harmony searching method. The core process of this method is Gaussian mutation, objective decomposition, and a harmony memory extraction algorithm. This method improves the packet delivery ratio and reduced the delay. However, the routing overhead is high. Promoted in [31] is a two-hop routing algorithm to improve the throughput, delay, packet delivery ratio, packet loss rate, and communication overhead of the VANETs. This algorithm is based on multi-objective Harris Hawks optimization. Though this method achieves a better overall performance, this type of approach is not suitable for a network with a huge dynamic mobility model. In [32], the author described the federated learning and blockchain to improve the trust and integrity of the network. This paper shows the current research challenges of the VANETs in terms of security and privacy perspectives. Introduced in [33] is a method to improve the convergence speed of the network called the Hybrid Genetic Firefly Algorithm-based Routing Protocol. The essential feature of GA is combined with the Firefly Algorithm and it is applied in both the lightly populated and heavily populated areas. The performance is better in both scenarios, but it fails to achieve higher throughput. Suggested in [34] is a reactive routing protocol called AODV with ant colony optimization (ACO) to improve the data transmission in VANETs during the process of communication. The multicasting approach is used for path establishment. Due to this method, the system achieves high throughput, low packet loss, and low end-to-end delay, but it produces more routing overhead during transmission. Developed in [35] is a method to improve the packet delivery ratio and throughput of unmanned aerial vehicles (UAVs). Here, the particle swarm optimization algorithm is utilized for finding the optimal deployment of UAVs, which is based on vehicle density, heading direction, and previous coverage information. This method produces high routing overhead. In [36], the instability is reduced, which is created by random topology in VANETs after the author introduced a grey wolf optimization-based clustering algorithm. In this concept, the cluster head is chosen according to the social behavior and hunting mechanism of grey wolves. This method produced a high packet delivery ratio with reduced energy consumption, but the network throughput is very low. The efficiency of the network is reduced with the increase of the throughput in the network. Proposed in [37] is a street-centric routing scheme (SCRS) for the process of optimal path selection in the VANET network. Concepts such as multipath routing and ant colony optimization (ACO)-based clustering play a major role in improving the performance of the network. This method greatly reduces the computational cost and end-to-end delay,and produced more packet delivery ratio. However, the routing overhead is high and throughput is moderate. In [38], the author proposed the concept called QMM-VANET, which is the quality of service (QoS)-based mobility management; it is the reliable and stable CH selection method with the trustier vehicle. Additionally, it concentrates on retransmitting the packets using gateways and gateway recovery algorithms. This method achieves a high packet delivery ratio and network stability. However, throughput is moderate. Table 1 shows the summary of previous related works. After analyzing the earlier study, the major limitations which are present in the earlier research are improper mobility management in dynamic communication range, improper inter-vehicular link reliability, ineffective cluster formation, and lack of security. Due to these drawbacks, the effectiveness of VANETs' communication is highly affected, which leads to a reduction in the performance of the network.