Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 7 ، دوره 14، شماره 1 ، مهر 2026، صفحه 73-82 1.11 M )
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2025.11588.819 نویسندگان
Neda Sedighian ؛ Abbas Karimi* ؛ Javad Mohammadzadeh ؛ Faraneh Zarafshan
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
مراجع
[1] M.R. Ghori, K. Z. Zamli, N. Quosthoni, M. Hisyam, M. Montaser, "Vehicular ad-hoc network (VANET): Review," in Proc. 2018 IEEE International Conference on Innovative Research and Development (ICIRD), 2018 .
[2] S. Ashraf, T. Ahmed, "Machine learning shrewd approach for an imbalanced dataset conversion samples,” J. Eng. Technol., 11(1): 1-22, 2020.
[3] S. A. Ali Shah, X. Fernando, R. Kashef, "A survey on artificial-intelligence-based internet of vehicles utilizing unmanned aerial vehicles," Drones, 8(8): 353, 2024 .
[4] S. Abbasi, A. M. Rahmani, A. Balador, A. Sahafi, “A fault-tolerant adaptive genetic algorithm for service scheduling in internet of vehicles,” Appl. Soft Comput., 143(c), 2023 .
[5] B. Saoud, I. Shayea, A. E. Yahya, Z. A. Shamsan, A. Alhammadi, M. A. Alawad, Y. Alkhrijah, “Artificial intelligence, internet of things and 6G methodologies in the context of vehicular ad-hoc networks (VANETs): Survey,” ICT Express, 10(4): 959-980, 2024 .
[6] M. Mukhtaruzzaman, M. Atiquzzaman, “Stable dynamic predictive clustering (SDPC) protocol for vehicular ad hoc network,” arXiv preprint arXiv:2209.08075, 2022 .
[7] B. Marzak, K. Guemmat, H. Benlahmar, M. Talea, “Clustering in vehicular ad-hoc network using artificial neural network,” Int. Rev. Comput. Software (IRECOS), 11(6): 548, 2022.
[8] S. R. Ramlee et al., "Implementing of MFO algorithm and k-means clustering in VANET cluster optimization," J. Tec Empresarial, 2024.
[9] M.R. Esmaeili, S. H. Zahiri, S. M. Razavi, “A framework for high-level synthesis of VLSI circuits using a modified moth-flame optimization algorithm,” J. Electr. Comput. Eng. Innovations, 7(1): 95-111, 2019.
[10] A. Karimi, I. Rezaei, F. Zar Afshan, “Improving service quality in vehicular ad hoc networkusing cuckoo's multi-objective optimization algorithm,” J. M. Continua Math. Sci., 15(3), 2020.
[11] M. Ren, L. Khoukhi, H. Labiod, J. Zhang, V. Vèque, “A mobility-based scheme for dynamic clustering in vehicular ad-hoc networks (VANETs),” Veh. Commun., 9: 233-241, 2017.
[12] A. Sharma, S. Saini, “Gauss-sigmoid based clustering routing protocol for wireless sensor networks,” Int. J. Inf. Technol., 13(6): 2569–2577, 2021 .
[13] M. Ayyub, A. Oracevic, R. Hussain, A. Anjum Khan, Z. Zhang, “A comprehensive survey on clustering in vehicular networks: Current solutions and future challenges,” J. Ad Hoc Networks, 124, 102729, 2022 .
[14] M. Elhoseny, R. S. Rajan, M. Hammoudeh, K. Shankar, O. Aldabbas, “Swarm intelligence–based energy efficient clustering with multihop routing protocol for sustainable wireless sensor networks,” Int. J. Distrib. Sens. Netw., 16(9): 1–14, 2020.
[15] F. Mirhakimi, A. Karimi, “A preliminary study for improving reliability in hybrid vehicles,” Procedia Comput. Sci., 42: 308-312, 2014.
[16] A. Sharif, J. P. Li, M. A. Saleem, G. Manogaran, S. Kadry, A. Basit, M. A. Khan, “A dynamic clustering technique based on deep reinforcement learning for Internet of vehicles,” J. Intell. Manuf., 32(3): 757–768, 2021 .
[17] G. H. Alsuhli, A. Khattab, Y. A. Fahmy, Y. Massoud, “Enhanced urban clustering in VANETs using online machine learning,” in Proc. 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES): 1–6, 2019 .
[18] M. Bersali, A. Rachedi, H. Bouarfa, M. E. A. Badjara, “A novel cooperative clustering approach based on multi-criteria decision-making for IoV,” Int. J. High Perform. Syst. Archit., 11(1): 36–46, 2022 .
[19] H. Zerrouki, S. Moussaoui, A. Derder, Z. Doukha, “Reinforcement learning-based clustering scheme for the Internet of Vehicles,” Ann. Telecommun., 76: 685–698, 2021 .
[20] M. K. Jabbar, H. Trabelsi, “Clustering review in vehicular Ad hoc networks: Algorithms, comparisons, challenges and solutions,” Int. J. Interact. Mob. Technol., 16(10): 25-48, 2022.
[21] M. Aissa, B. Bouhdid, A. B. Mnaouer, A. Belghith, S. AlAhmadi, “SOFCluster: Safety-oriented, fuzzy logic-based clustering scheme for vehicular ad hoc networks,” Trans. Emerg. Telecommun. Technol., 31(12), e4042, 2020 .
[22] M. A. Hamza, H. M. Alshahrani, F. N. Al-Wesabi, M. Al Duhayyim, A. M. Hilal, H. Mahgoub, “Artificial intelligence based clustering with routing protocol for Internet of vehicles,” Comput. Mater. Continua, 70(3): 5835–5853, 2022 .
[23] Y. Fahmy, G. Alsuhli, A. Khattab, “Optimizing environment-aware VANET clustering using machine learning,” Int. J. Intell. Transport. Syst. Res., 21: 394–408, 2023 .
[24] S. Charoenchai, P. Siripongwutikorn, “Genetic algorithm for multi-hop VANET clustering based on coalitional game,” J. Network Syst. Manag., 32(1), 2024.
[25] M. Ren, J. Zhang, L. Khoukhi, V. Végue, "A review of clustering algorithms in VANETs," Ann. Telecommun., 76(5-6): 581-603, 2021.
[26] R. K. Karne, T. K. Sreeja, “A novel approach for dynamic stable clustering in VANET using deep learning (LSTM) model,” Int. J. Electr. Electron. Res., 10(4): 1092–1098, 2022 .
[27] M. Ren, J. Zhang, L. Khoukhi, “A review of clustering algorithms in VANETs,” Ann. Telecommun., 76: 581–603, 2021.
[28] H. N. Abdulrazzak, G. C. Hock, N. A. Mohamed Radzi, N. M. L. Tan, C. F. Kwong, “Modeling and analysis of new hybrid clustering techn ique for vehicular ad hoc network,” Math.s , 10(24): 4720, 2022 .
[29] Y. Fahmy, G. Alsuhli, A. Khattab, “Optimizing environment-aware vanet clustering using machine learning,” Int. J. Intell. Transport. Syst. Res., 21: 394-408, 2023.
[30] M. H. Badole, A. D. Thakare, “An evolutionary optimization based on clustering algorithm to enhance VANET communication services,” in Proc. IoT Based Control Networks and Intelligent Systems: 291-311, 2023.
[31] X. Zhao, Z. Tang, F. Cao, C. Zhu, J. Périaux, “An efficient hybrid evolutionary optimization method coupling cultural algorithm with genetic algorithms and its application to aerodynamic shape design,” Appl. Sci., 12(7): 3482, 2022 .
آمار
تعداد مشاهده مقاله: 567
تعداد دریافت فایل اصل مقاله: 114
