Improving Security and Efficiency for Vehicular Ad-Hoc Networks using MVQQ

Vehicular Ad Hoc Networks (VANETs) represent a rapidly expanding domain within wireless communication, crucial for establishing dependable and secure transportation systems. In VANETs, each vehicle functions as a node, facilitating communication with other nodes and base stations along its route. Operating akin to intelligent mobile nodes, vehicles in VANETs interact with neighboring vehicles and nodes to ensure seamless communication. Moreover, users of VANETs benefit from access to the Intelligent Transportation System (ITS), which enhances operational efficiency. This study focuses on integrating the K-means clustering and Multivariate Quadratic Quasigroups (MvQQ) algorithm within the VANET framework, serving as an asymmetric encryption method. The selection procedures for Personal Best (pbest) and Global Best (gbest) are guided by these groupings, reflecting a comprehensive review of existing methodologies in VANET research.