IMPLEMENTING OF MFO ALGORITHM AND K-MEANS CLUSTERING IN VANET CLUSTER OPTIMIZATION

Abstract

Field of wireless communication has experienced rapid growth, leading to an increased focus on research and development by the academic and industry communities. The evolution of wireless communication technology has been driven by the demand for faster, more reliable and efficient data transmission, as well as the emergence of new applications and services. One innovative technology in this field that could play a big role in the future of smart transportation systems is Vehicular Ad Hoc Networks, or VANETs. VANETs provide a communication architecture that improves traffic services and helps reduce accident rates. The dynamic topology of VANET networks presents issues because of the constant mobility of vehicles, variations in communication patterns, and fluctuations in network density. The application of VANET Clustering Optimization is one suggested remedy for these issues. In VANET clustering, choosing the right cluster heads (CHs) is essential since CHs help to coordinate intra- and inter-cluster communication and enable effective data routing. Introducing MFO (Moth Flame Optimization), which updates the position based on movement and simulates the behaviour of moths in motion. To do this, a simulation that iteratively modifies node placements depending on optimization criteria using the MFO methods must be created. Utilize the MFO algorithm's output, which includes the nodes' positions and attributes, as input for the K-Means clustering optimization to determine the ideal cluster node counts and optimal positions for the cluster heads. The study aims to simulate VANET Cluster Optimization in SUMO to visualized and analyse the behaviour of cluster moths (representing vehicles) in the VANET environment. The goal of the study is to determine, in a highway environment, the ideal number of cluster heads based on node count, speed, and dimension. By utilizing SUMO, a traffic simulation software, the research intends to model the movement and interactions of vehicles in a VANET setting to optimized the clustering of vehicles into manageable groups. The simulation in SUMO will allow for the visualization of how vehicles (represented by cluster moths) move, communicate and form clusters based on their speed, location and the size of the areas. By analysing the behaviour of these virtual vehicles, the study aims to determine the most effective number of cluster heads to ensure efficient communication, data sharing and network performance in the VANET environment.