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Fusion: Practice and Applications
Volume 12 , Issue 2, PP: 54-69 , 2023 | Cite this article as | XML | Html |PDF

Title

Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing

  Hayder Sabah Salih 1 * ,   Fatema Akbar Mohamed 2

1  Department of Private Education in the Iraqi Ministry of Higher education and Scientific Research, Baghdad, 10024, Iraq
    (haydersabah2@gmail.com)

2  Teaching Excellence and Technology Center, Gulf University, Sanad 26489, Bahrain
    (fatema.akbar@gulfuniversity.edu.bh)

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Doi   :   https://doi.org/10.54216/FPA.120205

Received: January 16, 2023 Revised: April 22, 2023 Accepted: June 18, 2023

Abstract :

The Internet of Vehicles (IoV) is a distributed system that enables data connectivity between vehicles and vehicular ad hoc networks, ensuring efficient and secure information exchange with infrastructures. Challenges in IoV include security clustering related to packet loss during data exchange, real-time analysis of public communication, and the need for autonomous-vehicle technology development using machine learning (ML). ML-assisted IoV has made significant progress in communication with public networks and interaction with the immediate surroundings. This study presents an experimental foundation for the advancement of the IoV system. While support vector machine (SVM) offers a robust and accurate approach for clustering velocity and solving classification challenges related to security, it is primarily a binary classifier and faces limitations in handling multi-class classification. To address this, an artificial neural network (ANN) is proposed for effective packet loss management in the autonomous system, improving the physical layer's secure network and offering better packet loss experience using the Global Positioning System. The fusion-based diversified model not only enables IoV systems to compete with rivals but also provides key advantages to ensure consistent profitability in cloud-enabled IoV. This paradigm integrates cloud computing (CC) with in-vehicle networks and the Internet of Things, offering safety and infotainment applications for road users. Data collection and experiments are conducted using Network Simulator 2 to automate AI configuration in the IoV fusion system.

Keywords :

Internet of vehicle; Fusion system; SVM; ANN; Cloud computing.

References :

[1] Ning, Z., Zhang, K., Wang, X., Obaidat, M. S., Guo, L., Hu, X., ... & Kwok, R. Y. (2020). Joint computing and caching in 5G-envisioned Internet of vehicles: A deep reinforcement learning-based traffic control system. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5201-5212.

[2] Zhao, J., Sun, X., Li, Q., & Ma, X. (2020). Edge caching and computation management for real-time internet of vehicles: An online and distributed approach. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2183-2197.

[3] Bagga, P., Das, A. K., Wazid, M., Rodrigues, J. J., & Park, Y. (2020). Authentication protocols in internet of vehicles: Taxonomy, analysis, and challenges. IEEE Access, 8, 54314-54344.

[4] Kamal, M., Srivastava, G., & Tariq, M. (2020). Blockchain-based lightweight and secured v2v communication in the internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(7), 3997-4004.

[5] Kurdi, Sarah Zuhair, Mohammed Hasan Ali, Mustafa Musa Jaber, Tanzila Saba, Amjad Rehman, and Robertas Damaševičius. "Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks." Journal of Personalized Medicine 13, no. 2 (2023): 181.

[6] Singh, P. K., Singh, R., Nandi, S. K., Ghafoor, K. Z., Rawat, D. B., & Nandi, S. (2020). Blockchain-based adaptive trust management in internet of vehicles using smart contract. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3616-3630.

[7] Qian, Y., Jiang, Y., Hu, L., Hossain, M. S., Alrashoud, M., & Al-Hammadi, M. (2020). Blockchain-based privacy-aware content caching in cognitive internet of vehicles. IEEE Network, 34(2), 46-51.

[8] Aman, M. N., Javaid, U., & Sikdar, B. (2020). A privacy-preserving and scalable authentication protocol for the internet of vehicles. IEEE Internet of Things Journal, 8(2), 1123-1139.

[9] Vasudev, H., Deshpande, V., Das, D., & Das, S. K. (2020). A lightweight mutual authentication protocol for V2V communication in internet of vehicles. IEEE Transactions on Vehicular Technology, 69(6), 6709-6717.

[10] Jabbar, R., Kharbeche, M., Al-Khalifa, K., Krichen, M., & Barkaoui, K. (2020). Blockchain for the internet of vehicles: A decentralised IoT solution for vehicles communication using ethereum. Sensors, 20(14), 3928.

[11] Liu, C., Liu, K., Guo, S., Xie, R., Lee, V. C., & Son, S. H. (2020). Adaptive offloading for time-critical tasks in heterogeneous internet of vehicles. IEEE Internet of Things Journal, 7(9), 7999-8011.

[12] Benalia, E., Bitam, S., & Mellouk, A. (2020). Data dissemination for Internet of vehicle based on 5G communications: A survey. Transactions on Emerging Telecommunications Technologies, 31(5), e3881.

[13] Kumar, N., Chaudhry, R., Kaiwartya, O., Kumar, N., & Ahmed, S. H. (2020). Green computing in software defined social internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3644-3653.

[14] da Silva Barbosa, F. E., de Mendonça Júnior, F. F., & Dias, K. L. (2020). A platform for cloudification of network and applications in the Internet of Vehicles. Transactions on Emerging Telecommunications Technologies, 31(5), e3961.

[15] Sun, G., Song, L., Yu, H., Du, X., & Guizani, M. (2020). A two-tier collection and processing scheme for fog-based mobile crowdsensing in the internet of vehicles. IEEE Internet of Things Journal, 8(3), 1971-1984.

[16] Li, T., Li, C., Luo, J., & Song, L. (2020). Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities. Intelligent and Converged Networks, 1(1), 1-17.

[17] Ji, H., Alfarraj, O., & Tolba, A. (2020). Artificial intelligence-empowered edge of vehicles: architecture, enabling technologies, and applications. IEEE Access, 8, 61020-61034.

[18] Alladi, T., Kohli, V., Chamola, V., Yu, F. R., & Guizani, M. (2021). Artificial intelligence (AI)-empowered intrusion detection architecture for the internet of vehicles. IEEE Wireless Communications, 28(3), 144-149.

[19] Queiroz, A., Oliveira, E., Barbosa, M., & Dias, K. (2020, December). A Survey on Blockchain and Edge Computing applied to the Internet of Vehicles. In 2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (pp. 1-6). IEEE.

[20] MENG, Z., GUAN, Z., WU, Z., LI, A., & CHEN, Z. (2020). Security enhanced Internet of vehicles with cloud-fog-dew computing. ZTE Communications, 15(S2), 47-51.

[21] Xu, X., Shen, B., Ding, S., Srivastava, G., Bilal, M., Khosravi, M. R., ... & Wang, M. (2020). Service offloading with deep Q-network for digital twinning-empowered internet of vehicles in edge computing. IEEE Transactions on Industrial Informatics, 18(2), 1414-1423.

[22] Ali, M. H., Al-Azzawi, W. K., Jaber, M., Abd, S. K., Alkhayyat, A., & Rasool, Z. I. (2022). Improving coal mine safety with internet of things (IoT) based Dynamic Sensor Information Control System. Physics and Chemistry of the Earth, Parts A/B/C, 128, 103225.

[23] Javed, A. R., Usman, M., Rehman, S. U., Khan, M. U., & Haghighi, M. S. (2020). Anomaly detection in automated vehicles using multistage attention-based convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4291-4300.

[24] Musa Jaber, M., Yussof, S., Ali, M. H., Khalil Abd, S., Jassim, M. M., & Alkhayyat, A. Internet of Things security framework based on near field communication with Otway Rees protocol. Transactions on Emerging Telecommunications Technologies, e4684.

[25] Song, H. M., Woo, J., & Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21, 100198.

Cite this Article as :
Style #
MLA Hayder Sabah Salih, Fatema Akbar Mohamed. "Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing." Fusion: Practice and Applications, Vol. 12, No. 2, 2023 ,PP. 54-69 (Doi   :  https://doi.org/10.54216/FPA.120205)
APA Hayder Sabah Salih, Fatema Akbar Mohamed. (2023). Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing. Journal of Fusion: Practice and Applications, 12 ( 2 ), 54-69 (Doi   :  https://doi.org/10.54216/FPA.120205)
Chicago Hayder Sabah Salih, Fatema Akbar Mohamed. "Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing." Journal of Fusion: Practice and Applications, 12 no. 2 (2023): 54-69 (Doi   :  https://doi.org/10.54216/FPA.120205)
Harvard Hayder Sabah Salih, Fatema Akbar Mohamed. (2023). Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing. Journal of Fusion: Practice and Applications, 12 ( 2 ), 54-69 (Doi   :  https://doi.org/10.54216/FPA.120205)
Vancouver Hayder Sabah Salih, Fatema Akbar Mohamed. Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing. Journal of Fusion: Practice and Applications, (2023); 12 ( 2 ): 54-69 (Doi   :  https://doi.org/10.54216/FPA.120205)
IEEE Hayder Sabah Salih, Fatema Akbar Mohamed, Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing, Journal of Fusion: Practice and Applications, Vol. 12 , No. 2 , (2023) : 54-69 (Doi   :  https://doi.org/10.54216/FPA.120205)
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