Cluster Optimization in VANET using MFO Algorithm and K-Means Clustering

Abstract

Wireless Technology is developing very fast. Researchers are actively researching in wireless communication as the technology for wireless communication has been growing quickly. Vehicular Ad Hoc Networks (VANETs), a cutting-edge technology in this area, have the potential to make a significant contribution to smart transportation systems in the future. VANETs offer a framework for communication that enhances traffic services and aids in lowering accident rates. Maintaining stability in Vehicular Ad-Hoc Network (VANET) clustering is difficult tasks due to high node mobility. First issue in VANET clustering is the Cluster Head (CH) selection since the CH has critical role in data routing and responsible for coordinating both inter and intra cluster communication. Second issue is the high mobility of nodes that cause difficulty to retain clustering optimization and will lead to inefficiency in network communication. Introduce MFO algorithm for simulate the movement behavior of moths and update the position based upon movements. Proven to be an effective and efficient method for solving optimization problem. To design K-Means algorithm that portion nodes based on their proximities by optimize the distance between nodes within same cluster by assigning them to the closet cluster center. Improving clustering efficiency by sending frequent updates to the CH in term of improving scalability, coverage, and clustering result, while reducing communication and energy consumption. Overall, the MFO Algorithm and K-Means algorithm can be used in combination to optimize the clustering in VANET, leading to better network performance, more reliable communication, and improved efficiency.