Artificial Intelligence to Overcome Challenges in Dynamic Clustering of VANET

kground and Objectives: Vehicular Ad Hoc Networks (VANETs) face significant challenges due to high mobility and rapid topology changes. One of the most critical issues in this context is the clustering process, which directly impacts delay reduction, cluster stability, and overall network efficiency. However, traditional clustering methods such as K-Means and MFO, which mainly rely on simple metrics like distance or signal strength, fail to deliver optimal performance in dynamic environments with variable network density. The primary objective of this study is to design and evaluate an advanced clustering algorithm called AI_MCA (Artificial Intelligence Multi Clustering Algorithm), leveraging artificial intelligence and multi-criteria decision-making. By considering factors such as signal strength, relative speed, node density, and vehicle movement direction, the proposed algorithm forms clusters with higher stability and efficiency in dynamic and high-density environments.Methods: This study uses simulations to evaluate AI_MCA in VANETs, which facilitate vehicle-to-vehicle communication and are characterized by high mobility and rapid position changes.Results: Simulations in NS۳ and SUMO show that AI_MCA reduces latency by ۲۰% (۱۲ms vs. ۱۵ms in MFO) and improves cluster stability by ۳۰% (lifetime of ۴۵s vs. ۳۳s in K-Means) within a ۶۰۰m range. At a ۱۰۰۰m range with ۳۰۰ nodes, delay increases to ۱۴ms and PDR drops to ۸۸%.Conclusion: AI_MCA outperforms traditional methods like K-Means and MFO, offering a scalable solution for VANET clustering.

کلیدواژه ها:

VANET ، Clustering ، Artificial Intelligence ، Scalability ، Dynamics