Design and Implementation of Heterogeneous Route Selection Algorithm for Delay Minimization in VANET

Abstract

Data dissemination is a promising use in Vehicular Ad hoc NETworks (VANETs), where messages are jointly carried and delivered by vehicles toward their destinations at defined points. Vehicles find it challenging to select the target uplink node among the roadside units (RSU) put in VANETs due to the RSU coverage, traffic intensity, and other sophisticated and dynamic affecting factors. In this study, a new adaptive vehicle clustering method is proposed that attempts to reduce the power consumption of automobiles. It dynamically distributes the computational assets of every virtual machine within the automobile. The mimosa pudica clustering optimization algorithm (MPCOA) is used to identify the ideal clustering number to reduce the overall energy consumption of the cars, and the clustering head is chosen based on the direction the vehicles are going, their weighted mobility, and their entropy. A cluster head (CH) oversees all intercluster and intracluster communication. Some factors to gauge the effectiveness of a network include the load on every CH, the lifespan of the cluster, and the overall number of clusters in the network. The new cluster is well suited for this type of huge data, in which they are separated into similarity, variations, and neighborhoods as different types of VANET features along with their combinational groups. Using a test bed and numerous simulations, the suggested system efficacy is assessed. Conducting a performance study and comparing the test bed findings to the simulation results provides a thorough understanding of the performance and viability of the proposed MPCOA-based system.