Due to the number of constraints and the dynamic nature of vehicular ad hoc networks (VANET), effective information exchange always remains a difficult task. Management of various load control parameters between the different nodes of the urban VANET (UVANET) network makes the optimization is difficult. In this work, we use a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input. This algorithm aims to provide an improved class lifetime, an improved information delivery rate, a reduced inter-class overload, and an optimization of a global criterion. A scalable algorithm is used to optimize the parameters of the GCMAV. Then, to address the performance and security challenges in the UVANET environment, we introduce an Efficient Key Management Scheme (KMSUNET) based on symmetric and asymmetric encryption. Our KMSUNET diagram takes into account the position of the vehicle nodes, the speed, the direction, the number of neighboring vehicle nodes, and the reputation of each node. The simulations were carried out using the NetSim simulator and Multi-Objective Evolutionary Algorithms (MOEA) framework to optimize parameters. Experiments were carried out with realistic maps of Open Street Maps and its results were compared with other algorithms. The survey suggests that the proposed methodology works well concerning the average lifetime of the inter-classes and the information's delivery rate.

由于约束条件的数量以及车辆自组织网络（VANET）的动态性质，有效的信息交换始终是一项艰巨的任务。在城市VANET（UVANET）网络的不同节点之间管理各种负载控制参数使优化变得困难。在这项工作中，我们使用一个多目标问题，该问题采用基于带有属性向量的图分类方法（GCMAV）作为输入的算法参数。该算法旨在提供改进的类寿命，改进的信息传递速率，减少的类间过载和全局标准的优化。可伸缩算法用于优化GCMAV的参数。然后，为应对UVANET环境中的性能和安全性挑战，我们介绍了一种基于对称和非对称加密的有效密钥管理方案（KMSUNET）。我们的KMSUNET图考虑了车辆节点的位置，速度，方向，相邻车辆节点的数量以及每个节点的信誉。使用NetSim模拟器和多目标进化算法（MOEA）框架进行了仿真，以优化参数。实验是在开放街道地图的真实地图上进行的，并将其结果与其他算法进行了比较。调查表明，所提出的方法论在类间的平均寿命和信息的传递率方面效果很好。相邻车辆节点的数量以及每个节点的信誉。使用NetSim模拟器和多目标进化算法（MOEA）框架进行了仿真，以优化参数。实验是在开放街道地图的真实地图上进行的，并将其结果与其他算法进行了比较。调查表明，所提出的方法论在类间的平均寿命和信息的传递率方面效果很好。相邻车辆节点的数量以及每个节点的信誉。使用NetSim模拟器和多目标进化算法（MOEA）框架进行了仿真，以优化参数。实验是在开放街道地图的真实地图上进行的，并将其结果与其他算法进行了比较。调查表明，所提出的方法论在类间的平均寿命和信息的传递率方面效果很好。