Nowadays, vehicle ad hoc networks (VANET) are becoming one of the trends that motivate many service providers in urban areas. In this work, we take into account the random and continuous evolution of traffic in the VANET environment. We adopt a system to model the mode of evolution based on commutation. The proposed system is defined as a finite collection of linear submodels. Thus, for each subsystem, it is necessary to identify the discrete state and to establish a specific sub-model to model the overall system. However, according to recent studies, the adoption of an efficient VANET clustering algorithm can promote road safety, provide a means of entertainment for passengers, and promote message routing. In this article, a clustering algorithm based on a Self-Adaptative Multi-Kernel clustering for urban VANET (SAMNET) is also provided. SAMNET is based on a set of measurement data, representing the unpredictable density of vehicle nodes, acceleration or deceleration, and the limited radio range of the communication scheme used. The proposed algorithm takes advantage of the concept of identifying these data generated by linear sub-models which communicate through an unpredictable dynamic switching. It is a self-adapting clustering algorithm that consists of modeling each sub-model based on a linear regression function. SAMNET is broken down into three stages: (i) initialization of clusters, (ii) adaptation of clusters, (iii) fusion of clusters. To assess the comparative effectiveness of SAMNET, many experiments are carried out. The results obtained show that the proposed methodology provides almost optimal results and works well with regard to the average lifetime of the clusters and the data delivery rate.

如今，车辆自组织网络（VANET）成为激励城市地区许多服务提供商的趋势之一。在这项工作中，我们考虑了VANET环境中流量的随机和连续演变。我们采用一个系统对基于换向的演化模式进行建模。提出的系统定义为线性子模型的有限集合。因此，对于每个子系统，有必要识别离散状态并建立一个特定的子模型来对整个系统进行建模。但是，根据最近的研究，采用有效的VANET聚类算法可以提高道路安全性，为乘客提供娱乐方式并促进消息路由。本文还提供了一种基于自适应多核聚类的城市VANET（SAMNET）聚类算法。SAMNET基于一组测量数据，这些数据表示车辆节点的不可预测的密度，加速度或减速度以及所用通信方案的有限无线电范围。所提出的算法利用了识别由线性子模型生成的数据的概念，这些线性子模型通过不可预测的动态切换进行通信。它是一种自适应聚类算法，包括基于线性回归函数对每个子模型进行建模。SAMNET分为三个阶段：（i）集群初始化，（ii）集群适应，（iii）集群融合。为了评估SAMNET的相对有效性，进行了许多实验。