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Artificial Intelligence to Overcome Challenges in Dynamic Clustering of VANET | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 22 تیر 1404 | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2025.11588.819 | ||
| نویسندگان | ||
| N. Sedighian؛ A. Karimi* ؛ J. Mohammadzadeh؛ F. Zarafshan | ||
| Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran & Institute of Artificial Intelligence and Social and Advanced Technology, Ka.C., Islamic Azad University, Karaj, Iran. | ||
| تاریخ دریافت: 24 اسفند 1403، تاریخ بازنگری: 25 اردیبهشت 1404، تاریخ پذیرش: 09 تیر 1404 | ||
| چکیده | ||
| Background 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 NS3 and SUMO show that AI_MCA reduces latency by 20% (12ms vs. 15ms in MFO) and improves cluster stability by 30% (lifetime of 45s vs. 33s in K-Means) within a 600m range. At a 1000m range with 300 nodes, delay increases to 14ms and PDR drops to 88%. Conclusion: AI_MCA outperforms traditional methods like K-Means and MFO, offering a scalable solution for VANET clustering. | ||
| کلیدواژهها | ||
| VANET؛ Clustering؛ Artificial Intelligence؛ Scalability؛ Dynamics | ||
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آمار تعداد مشاهده مقاله: 320 |
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