Clustering method and symmetric/asymmetric cryptography scheme adapted to securing urban VANET networks

Abstract 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.