Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication. Clustering can solve the issues of vehicular ad hoc network (VANET); however, due to the high mobility of the vehicles, clustering in VANET suffers stability issue. Previously proposed clustering algorithms for VANET are optimized for either cluster head or cluster member duration. Moreover, the absence of the intelligent use of mobility parameters, such as direction, movement, position, and velocity, results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a new robust and dynamic mobility-based clustering algorithm junction-based clustering for VANET (JCV) is proposed in this paper. In contrast to previous studies, transmission range, moving direction of the vehicle at the next junction, and vehicle density are considered in the creation of a cluster, whereas relative position, movement at the junction, degree of a node, and time spent on the road are considered to select the cluster head. The performance of JCV is compared with two existing VANET clustering algorithms in terms of the average cluster head duration, the average cluster member duration, the average number of cluster head change, and the percentage of vehicles participating in the clustering process. The simulation result shows JCV outperforms the existing algorithms and achieved better stability.

车载通信是智慧城市的重要组成部分。可扩展性是车辆通信的主要问题。聚类可以解决车载自组网（VANET）的问题；然而，由于车辆的高机动性，VANET 中的集群存在稳定性问题。先前为 VANET 提出的聚类算法针对簇头或簇成员持续时间进行了优化。此外，没有智能使用移动性参数，如方向、移动、位置和速度，会导致集群稳定性问题。考虑有效利用移动性参数的动态聚类算法可以解决VANET中的稳定性问题。为了提高VANET的稳定性，本文提出了一种新的基于鲁棒性和动态移动性的VANET聚类算法JCV（junction-based clustering for VANET）。与之前的研究相比，传输距离、车辆在下一个路口的移动方向和车辆密度在创建集群时被考虑，而相对位置、路口的移动、节点的度数和在集群上花费的时间道路被认为是选择簇头。从平均簇头持续时间、平均簇成员持续时间、平均簇头变化次数、参与聚类过程的车辆百分比等方面，将JCV与现有两种VANET聚类算法的性能进行比较。仿真结果表明，JCV 优于现有算法并取得了更好的稳定性。而相对位置、交界处的移动、节点的度数和在道路上花费的时间被认为是选择簇头。从平均簇头持续时间、平均簇成员持续时间、平均簇头变化次数、参与聚类过程的车辆百分比等方面，将JCV与现有两种VANET聚类算法的性能进行比较。仿真结果表明，JCV 优于现有算法并取得了更好的稳定性。而相对位置、交界处的移动、节点的度数和在道路上花费的时间被认为是选择簇头。从平均簇头持续时间、平均簇成员持续时间、平均簇头变化次数、参与聚类过程的车辆百分比等方面，将JCV与现有两种VANET聚类算法的性能进行比较。仿真结果表明，JCV 优于现有算法并取得了更好的稳定性。簇头变化的平均次数，以及参与聚类过程的车辆百分比。仿真结果表明，JCV 优于现有算法并取得了更好的稳定性。簇头变化的平均次数，以及参与聚类过程的车辆百分比。仿真结果表明，JCV 优于现有算法并取得了更好的稳定性。