Vehicular communication is an essential part of a smart city. Scalability is a major issue for vehicular communication, specially, when the number of vehicles increases at any given point. Vehicles also suffer some other problems such as broadcast problem. 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 straight road or for intersection. Moreover, the absence of the intelligent use of a combination of the mobility parameters, such as direction, movement, position, velocity, degree of vehicle, movement at the intersection etc., results in cluster stability issues. A dynamic clustering algorithm considering the efficient use of all the mobility parameters can solve the stability problem in VANET. To achieve higher stability for VANET, a novel robust and dynamic clustering algorithm stable dynamic predictive clustering (SDPC) for VANET is proposed in this paper. In contrast to previous studies, vehicle relative velocity, vehicle position, vehicle distance, transmission range, and vehicle density are considered in the creation of a cluster, whereas relative distance, movement at the intersection, degree of vehicles are considered to select the cluster head. From the mobility parameters the future road scenario is constructed. The cluster is created, and the cluster head is selected based on the future construction of the road. The performance of SDPC is compared in terms of the average cluster head change rate, the average cluster head duration, the average cluster member duration, and the ratio of clustering overhead in terms of total packet transmission. The simulation result shows SDPC outperforms the existing algorithms and achieved better clustering stability.
翻译:由于车辆流动性高,VANET的集群具有稳定性问题。以前,VANET的拟议组合算法是用于直路或交叉路口的优化。此外,没有明智地使用移动参数的组合,例如方向、移动、位置、速度、车辆速度、程度、交叉路口的移动等,造成集束稳定性问题。考虑到高效使用所有流动参数的动态组合算法可以解决VANET的稳定性问题。为了提高VANET的稳定性,本文提议VANET的新型强和动态组合算法稳定。与先前的研究相比,车辆相对速度、车辆位置、车辆距离、车辆速度、车辆速度、车辆速度、交界点、车辆密度等流动参数的混合,也存在集束稳定性问题。考虑到在创建一个集束上的平均比率、移动率和车辆密度方面,而相对远距离的集束值则显示,在构建一个集束中,现有集束中的平均移动率、相对的移动率是未来集束的深度。在创建的集束中,从一个平均的集束的集束值显示,在创建中,平均的集束路面上的平均移动,而相对的流值显示,平均的集束的集束值显示,平均的集群群体变化是基于已形成的集群体的集群体的集群体的集体的集体变化。