## 2 Related Works  
### Clustering Associated Research  
Clustering is utilized in a variety of communication networks [18] and VANET cluster analysis [19]. The clustering of VANETs is the emphasis of this research study. Some additional method uses density as the major parameter for several VANET clustering techniques. A strategy for clustering depending on density measure is presented in [20].  
In [21], the idea is proposed of presenting two CHs in a single cluster, which are the main CH and secondary CH. The maximum weight is given to the main CH (PCH), which is calculated using parameters such as the average rate of speed between vehicles. To enhance cluster stability, the secondary CH will act as the backup to the PCH and assumes responsibilities when the PCH leaves the cluster. Presented in [22] is the double-head clustering (DHC) approach, which used new measures for channel access to improve cluster stability and efficiency. It analyzes the signal strength and the connection expiry time, in addition to the vehicle's velocity, orientation, and location (LET).  
Presented in [23] is a clustering algorithm to reduce power consumption. To enhance the lifetime of the network cluster, head and load balancing are concentrated. Effective CH election with CH rotation is done. This method improves the throughput and lifetime of the network, but fails to achieve a high packet delivery ratio; hence, this is not suitable for a network with dynamically varying topology.  
In [24], the author recommended a destination-aware context-based routing scheme to increase the packet delivery ratio and reduce network delay. This structure includes cluster formation, motor location, and speed of the vehicles. The cluster creation and CH selection are done with the help of the motor mobility and connection details, but this method fails to achieve high throughput.  
In [25], the author suggests a novel clustering method to improve the throughput and packet delivery ratio of the VANETs. The proposed method combines the modified \(k\)-means algorithm and the maximum stable set problem. Effective cluster formation and CH selection are done through this method. However, this method produces more overhead and delays during the process of transmission.  
Presented in [26] is a two-fold model for a stable CH election process, which consists of one or many kinds of communication with the hyper-graph spectral clustering method and cluster maintenance. This method greatly helps to increase the lifetime of the network and achieves moderate improvement in the throughput and delivery ratio. Furthermore, this produces high routing overhead and delay.  
Suggested in [27] is a fuzzy logic-based cluster selection to improve the stability of the network. This method greatly improves the lifetime and efficiency of the network, but fails to achieve high throughput and packet delivery ratio.  
Presented in [28] is a cluster-based routing protocol to improve the path length and data delivery ratio, as well as to reduce the average end-to-end delay. Here, a named data network technique is used in a hybrid communication model for dedicated short-range communication. However, this method produces more overhead and fails to achieve high throughput.  
Introduced in [29] is a multi-channel clustering-based congestion control algorithm to significantly improve the network performance by minimizing the energy consumption in VANETs. It is one of the proactive infrastructure-based clustering approaches. Though this method's energy efficiency has increased, this type of approach is not suitable for a network with a huge dynamic mobility model.