Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey

Yıl 2024, Cilt: 8 Sayı: 2, 282 – 299, 30.04.2024

Hussam Alkashto , Abdullah Elewi

https://doi.org/10.31127/tuje.1366248

Öz

The integration of blockchain and machine learning technologies has the potential to enable the development of more secure, reliable, and efficient autonomous car systems. Blockchain can be used to store, manage, and share the large amounts of data generated by autonomous vehicle various sensors and cameras, ensuring the integrity and security of these data. Machine learning algorithms can be used to analyze and fuse these data in real time, allowing the vehicle to make informed decisions about how to navigate its environment and respond to changing conditions. Thus, the combination of these technologies has the potential to improve the safety, performance, and scalability of autonomous car systems, making them a more applicable and attractive option for consumers and industry stakeholders. In this paper, all relevant technologies, such as machine learning, blockchain and autonomous cars, were explored. Various techniques of machine learning were investigated, including reinforcement learning strategies, the evolution of artificial neural networks and main deep learning algorithms. The main features of the blockchain technology, as well as its different types and consensus mechanisms, were discussed briefly. Autonomous cars, their different types of sensors, potential vulnerabilities, sensor data fusion techniques, and decision-making models were addressed, and main problem domains and trends were underlined. Furthermore, relevant research discussing blockchain for intelligent transportation systems and internet of vehicles was examined. Subsequently, papers related to the integration of blockchain with machine learning for autonomous cars and vehicles were compared and summarized. Finally, the main applications, challenges and future trends of this integration were highlighted.

Anahtar Kelimeler

Blockchain, Machine learning, Autonomous vehicles, Reinforcement learning, Internet of vehicles

Kaynakça

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Yıl 2024, Cilt: 8 Sayı: 2, 282 – 299, 30.04.2024

Hussam Alkashto , Abdullah Elewi

https://doi.org/10.31127/tuje.1366248

Öz

Kaynakça

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  • Ahamed, N. N., & Karthikeyan, P. (2020). A reinforcement learning integrated in heuristic search method for self-driving vehicle using blockchain in supply chain management. International Journal of Intelligent Networks, 1, 92-101. https://doi.org/10.1016/j.ijin.2020.09.001
  • Liu, C. H., Lin, Q., & Wen, S. (2018). Blockchain-enabled data collection and sharing for industrial IoT with deep reinforcement learning. IEEE Transactions on Industrial Informatics, 15(6), 3516-3526. https://doi.org/10.1109/TII.2018.2890203
  • Liu, M., Yu, F. R., Teng, Y., Leung, V. C., & Song, M. (2019). Performance optimization for blockchain-enabled industrial Internet of Things (IIoT) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 15(6), 3559-3570. https://doi.org/10.1109/TII.2019.2897805
  • He, Y., Huang, K., Zhang, G., Yu, F. R., Chen, J., & Li, J. (2021). Bift: A blockchain-based federated learning system for connected and autonomous vehicles. IEEE Internet of Things Journal, 9(14), 12311-12322. https://doi.org/10.1109/JIOT.2021.3135342
  • Jain, S., Ahuja, N. J., Srikanth, P., Bhadane, K. V., Nagaiah, B., Kumar, A., & Konstantinou, C. (2021). Blockchain and autonomous vehicles: Recent advances and future directions. IEEE Access, 9, 130264-130328. https://doi.org/10.1109/ACCESS.2021.3113649
  • Singh, P., Elmi, Z., Lau, Y. Y., Borowska-Stefańska, M., Wiśniewski, S., & Dulebenets, M. A. (2022). Blockchain and AI technology convergence: Applications in transportation systems. Vehicular Communications, 38, 100521. https://doi.org/10.1016/j.vehcom.2022.100521

Toplam 88 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ağ Mühendisliği, İletişim Mühendisliği (Diğer)
BölümArticles
Yazarlar

Hussam Alkashto MERSIN UNIVERSITY 0009-0009-6770-0160 Türkiye

Abdullah Elewi MERSIN UNIVERSITY 0000-0001-9774-5292 Türkiye

Erken Görünüm Tarihi9 Nisan 2024
Yayımlanma Tarihi30 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 2

Kaynak Göster

APAAlkashto, H., & Elewi, A. (2024). Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. Turkish Journal of Engineering, 8(2), 282-299. https://doi.org/10.31127/tuje.1366248
AMAAlkashto H, Elewi A. Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. TUJE. Nisan 2024;8(2):282-299. doi:10.31127/tuje.1366248
ChicagoAlkashto, Hussam, ve Abdullah Elewi. “Integration of Blockchain and Machine Learning for Safe and Efficient Autonomous Car Systems: A Survey”. Turkish Journal of Engineering 8, sy. 2 (Nisan 2024): 282-99. https://doi.org/10.31127/tuje.1366248.
EndNoteAlkashto H, Elewi A (01 Nisan 2024) Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. Turkish Journal of Engineering 8 2 282–299.
IEEEH. Alkashto ve A. Elewi, “Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey”, TUJE, c. 8, sy. 2, ss. 282–299, 2024, doi: 10.31127/tuje.1366248.
ISNADAlkashto, Hussam – Elewi, Abdullah. “Integration of Blockchain and Machine Learning for Safe and Efficient Autonomous Car Systems: A Survey”. Turkish Journal of Engineering 8/2 (Nisan 2024), 282-299. https://doi.org/10.31127/tuje.1366248.
JAMAAlkashto H, Elewi A. Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. TUJE. 2024;8:282–299.
MLAAlkashto, Hussam ve Abdullah Elewi. “Integration of Blockchain and Machine Learning for Safe and Efficient Autonomous Car Systems: A Survey”. Turkish Journal of Engineering, c. 8, sy. 2, 2024, ss. 282-99, doi:10.31127/tuje.1366248.
VancouverAlkashto H, Elewi A. Integration of blockchain and machine learning for safe and efficient autonomous car systems: A survey. TUJE. 2024;8(2):282-99.

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