## 5 Taxonomy of Soft Computing Techniques Aware Clustering-Based Routing Protocols in VANETs
### K-Means Algorithm  
It is a centroid-based algorithm, which means that each cluster has its centroid. The main goal of this algorithm is to reduce the number of distances between data points and the clusters to which they belong. The algorithm takes an unlabeled dataset as input, divides it into k-number of clusters, and repeats the procedure until no better clusters are found. In this algorithm, the value of k should be predetermined. The k-means clustering algorithm primarily accomplishes two goals:  
* Uses an iterative approach to find the best value for K center points or centroids. * Assigns each data point to the K center that is nearest to it. A cluster is formed to data points that are closed to a specific K center. Hence, each cluster has data points with some commonalities, and is away from other clusters. However, the K-means clustering [25] approach has been used significantly to solve a variety of VANET problems. In summary, the PDR and throughput are improved with reduced delay, which is a major finding in the research [25]; however, the nodes are limited from 50-100, with a fixed communication range and less vehicle speed. [PERSON] et al. [26] proposed a clustering-based optimization approach to extend the energy efficiency of V2V communication. This paper introduced the model of the k-medoid to cluster the vehicles, and the energy-efficient nodes are accepted for convincing transmission. The successful node identification of VANETs was introduced in this paper, considering at least the energy utilization factor. A k-means algorithm recognizes effective nodes from each cluster with the probability of achieving energy-efficient transmission. The k-medoid process groups the vehicles in various modifications and selects any nodes in some rounds as CHs. It has the potential to decrease the amount of communicated messages from each node to other, saving the network more resources. At that point, through the evolution of the network factor (EC) using the EDA algorithm, the optimal path for V2V transmission was acquired between the vehicles in VANETs. The proposed clustering-based optimization technique improves the energy efficiency in the networking nodes with less execution time. However, the work can be taken forward to maximize QoS by proposing efficient clustering and optimization techniques. [PERSON] et al. [27] suggested a dynamic grouping that fits well with VANET's dynamic topology characteristics by K-implies. The suggested strategy fits admirably with the number of bunches referred to in advance and even the mysterious number of categories. The user has the opportunity to resolve the number of bunches needed in this method. This measure demonstrates the new focus of the bunch by increasing the unit counter by one in each concentration until the goal work is accomplished. The same can be established and it is possible to overcome the ideal CHs and the connection between CMs and CHs. There is a dynamic weighting potential for dynamic k-means. The detailed analysis shows that the proposed computation produces fine improvements in the main VANET factors. This calculation decreases the number of messages and further increases the proportion of package transport. K-implies allow dynamic grouping of cluster members which improves its connectivity with the cluster head for the dynamic topology of VANET, but choosing the best cluster head in the different scenarios is still a challenging task. [PERSON] et al. [28] proposed a new Triple Cluster Based Routing Protocol (TCRP) for CH selection utilizing the revised K-Means & Floyd-Warshall algorithm. The updated k-means divide the vehicle nodes inside their velocity confidence variety into three clusters. For all VANET pairs of vehicles, the Floyd-Warshall algorithm determines the smallest path. A vehicle with the lowest average distance to the other and the smallest velocity variation will be chosen. The modified k-means algorithm for cluster formation is also proposed, considering the distance and cluster size.