A Survey on AI Models for Vehicular and UAV Networks Challenges in High Mobility and Dynamic Topology

Penulis

  • Entesar Alasaad

DOI:

https://doi.org/10.33387/protk.v13i1.11344

Kata Kunci:

V2X Communication, UAV Networks, Artificial Intelligence, Deep Reinforcement Learning, Graph Neural Networks, Federated Learning.

Abstrak

Artificial Intelligence (AI) has become a key enabler of intelligent vehicular (V2X) and Unmanned Aerial Vehicle (UAV) networks. These networks face extreme mobility and rapidly changing topologies, making stable communication and decision-making very difficult. Traditional AI models such as CNN and RNN fail to adapt quickly to these dynamic conditions. This survey reviews recent AI-based approaches designed to improve reliability, latency, and energy efficiency in V2X and UAV networks. The study compares Deep Reinforcement Learning (DRL), Graph Neural Networks (GNN), and Federated Learning (FL) methods, highlighting their benefits and limitations. Future research should focus on adaptive AI architectures that can operate under continuous topology changes and mobility uncertainty.

Referensi

Wang, D., Qiu, A., Zhou, Q., & Schotten, H. D. (2025). A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications. arXiv preprint arXiv:2506.09512.‏

Wang, W., Wu, Q., Fan, P., Cheng, N., Chen, W., Wang, J., & Letaief, K. B. (2024). Optimizing age of information in vehicular edge computing with federated graph neural network multi-agent reinforcement learning. arXiv preprint arXiv:2407.02342.‏

Ali Shah, S. A., Fernando, X., & Kashef, R. (2024). A survey on artificial-intelligence-based internet of vehicles utilizing unmanned aerial vehicles. Drones, 8(8), 353.‏

Tan, K., Bremner, D., Le Kernec, J., Zhang, L., & Imran, M. (2022). Machine learning in vehicular networking: An overview. Digital Communications and Networks, 8(1), 18-24.‏

Zhou, L., Yin, H., Zhao, H., Wei, J., Hu, D., & Leung, V. C. (2024). A Comprehensive Survey of Artificial Intelligence Applications in UAV-Enabled Wireless Networks. Digital Communications and Networks.‏

Pal, O. K., Shovon, M. S. H., Mridha, M. F., & Shin, J. (2024). In-depth review of AI-enabled unmanned aerial vehicles: trends, vision, and challenges. Discover Artificial Intelligence, 4(1), 97.‏

Tlili, F., Ayed, S., & Fourati, L. C. (2024). Advancing UAV security with artificial intelligence: A comprehensive survey of techniques and future directions. Internet of Things, 27, 101281.‏

Chellapandi, V. P., Yuan, L., Brinton, C. G., Żak, S. H., & Wang, Z. (2023). Federated learning for connected and automated vehicles: A survey of existing approaches and challenges. IEEE Transactions on Intelligent Vehicles, 9(1), 119-137.‏

Unduhan

File Tambahan

Diterbitkan

2026-01-31

Cara Mengutip

Alasaad, E. (2026). A Survey on AI Models for Vehicular and UAV Networks Challenges in High Mobility and Dynamic Topology. PROtek : Jurnal Ilmiah Teknik Elektro, 13(1), 59–62. https://doi.org/10.33387/protk.v13i1.11344

Terbitan

Vol 13 No 1 (2026): Protek : Jurnal Ilmiah Teknik Elektro

Bagian

Telecommunication System and Technology

Lisensi

 

    1. Penulis menyimpan hak cipta dan memberikan jurnal hak penerbitan pertama naskah secara simultan dengan lisensi di bawah Creative Commons Attribution License yang mengizinkan orang lain untuk berbagi pekerjaan dengan sebuah pernyataan kepenulisan pekerjaan dan penerbitan awal di jurnal ini.

    1. Penulis bisa memasukkan ke dalam penyusunan kontraktual tambahan terpisah untuk distribusi non ekslusif versi kaya terbitan jurnal (contoh: mempostingnya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan penerbitan awalnya di jurnal ini.

    1. Penulis diizinkan dan didorong untuk mem-posting karya mereka online (contoh: di repositori institusional atau di website mereka) sebelum dan selama proses penyerahan, karena dapat mengarahkan ke pertukaran produktif, seperti halnya sitiran yang lebih awal dan lebih hebat dari karya yang diterbitkan. (Lihat Efek Akses Terbuka).