# Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network
## 2 Literature Study
**Secondly**, we explain the trends in clustering techniques and clustering based on K-Means by discussing different clustering models and K-Means clustering algorithms. The Chain-Branch-Leaf (CBL) clustering algorithm proposed by [16] divides the VANET nodes into sectors and then creates a branch node as a cluster head and a leaf node as a normal node. The CBL algorithm was introduced to enhance the flooding of broadcast traffic and compared with the multi-point relay node (MPR) used in the Optimized Link State Routing protocol (OLSR). The CBL model has high stability in a high-speed VANET with location changes. The K-Means clustering algorithm is used in various applications, including VANETs. The remainder of this section focuses on K-Means clustering models, with particular attention paid to their application in VANETs. Regarding remote sensing applications, the authors of [17] used K-Means to select an optimum number of clusters (k) via a data-driven approach. They chose the optimum value of k from the value range (\(1\leq\text{k}\leq 20\)), within which the true value was presumed to lie, and ran the K-Means clustering algorithm many times individually for each value of k. If the centroid values of all the tests were the same, they moved towards the new value of k, and this process was repeated for different sequence values of k until the centroid values of the clusters were found to be the same. The results were evaluated, and they obtained high cluster similarity. In [18], the authors investigated an efficient clustering-based spectrum resource management scheme for dynamic heterogeneous vehicular networks. They proposed a low-complexity vehicle matching algorithm based on large-scale fading to improve the stability of communication links. In [19], the authors adjusted the well-known K-medoids algorithm to improve the stability of the network, and the lifetimes of all established links were increased. The cluster number and the initial CHs were not selected randomly, as is usual, but were based on a mathematical formula that considered the transmission ranges and the environment size and then assigned the nodes according to speed, proximity, directions, and other metrics. This metric prevented nodes with volatile and suspicious behavior from being selected as a new CH. The effectiveness of this model lies in its reduction of the cluster change rate in a highway scenario. In [20], a noise K-Means clustering algorithm was proposed to effectively solve the problems of computing the cluster numbers and the sensitivity of the clustering center initialization of the K-Means algorithm. The noise K-Means algorithm was applied to capture urban hotspots in big cities around the world. The authors of [21] suggested a modified K-Means algorithm and applied it with a Continuous Hopfield Network and Maximum Stable Set Problem for a VANET. The number of clusters was selected using the Maximum Stable Set Problem and Continuous Hopfield Network. Then, the distribution of vehicles to clusters was constrained by the link reliability model as a metric instead of the distance parameter that was used in the K-Means algorithm. The cluster member lifetime was increased, and the CH changing rate decreased. In [22], a location-based K-Means++ clustering algorithm was first developed to construct initial unmanned aerial vehicle (UAV) clusters. Subsequently, a weighted summation-based cluster head selection algorithm was proposed. The authors of [23] proposed an overlapping K-Means (OK-Means) clustering technique for a VANET based on overlapping clustering, and the number of clusters was calculated according to the node transmission range. The cluster length had to be less than two side-node transmission ranges. In [24], the authors proposed an entropy K-Means clustering algorithm that can be initialized without knowing the number of clusters and also demonstrates feature-reduction abilities.