The utilization of 6G in Industry 4.0

• Autorzy: Shukur Hanan M. , Askar Shavan , Zeebaree Subhi R.M.

Identyfikatory

DOI

10.35784/acs-2024-17

• Języki publikacji: EN

Abstrakty

EN

The sixth-generation (6G) communication technology has potential in various applications, for instance, industrial automation, intelligent transportation, healthcare systems, and energy consumption prediction. On the other hand, the concerns of privacy measures and security measures in 6G-enabled networks are considered critical issues and challenges. The integration of 6G with advanced technologies for example computing, Artificial Intelligence (AI), and Internet of Things (IoT) is a common theme in this paper. Additionally, the paper discusses the challenges and advancements required for 6G technology to be utilized with other technologies, involving edge technology, big data analytics, and deep learning. In this review paper, the authors overview the integration of 6G with cutting-edge technologies like IoT, IoMT, AI, and edge computing that address human requirements and issues. In addition, to make values for new technologies like Big data, federated learning machine learning, deep learning, and multiple aspects are merged collectively to offer a network for the machine and human growing era. These integrations can be utilized for monitoring energy consumption in real-time, intelligent transportation solutions, improved security in industrial applications, signal reconstruction, and industrial automation. Additionally, the authors illustrate the critical considerations and challenges that face the integration of 6G for instance, performance requirements, security, and privacy concerns. Overall, this paper suggests that 6G communication technology can revolutionize different sides of our society, and enhance efficiency and accuracy in various future industrial automation and sectors.

Słowa kluczowe

EN

6G; Industry 4.0; AI; IoT; industrial applications

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 2
• Strony: 75--89
• Opis fizyczny: Bibliogr. 53 poz., fig., tab.

Twórcy

autor

Shukur Hanan M.

• Al-Kitab University, Technical Engineering College, Computer Techniques Engineering Dept., Iraq

• https://orcid.org/0000-0002-8420-5331

autor

Askar Shavan

• Technical Engineering College, Information System Eng. Dept., Iraq

• https://orcid.org/0000-0002-9279-8181

autor

Zeebaree Subhi R.M.

• Technical Engineering College, Information System Eng. Dept., Iraq

• https://orcid.org/0000-0002-3895-2619

Bibliografia

• [1] Abdulazeez, D. H, & Askar, S. K. (2023). Offloading mechanisms based on reinforcement learning and deep learning algorithms in the fog computing environment. IEEE Access, 11, 12555-12586. https://doi.org/10.1109/ACCESS.2023.3241881
• [2] Abdulazeez, D. H, & Askar, S. K. (2024). A novel offloading mechanism leveraging fuzzy logic and Deep Reinforcement Learning to improve IoT application performance in a three-layer architecture within the Fog-Cloud environment. IEEE Access, 12, 39936-39952. https://doi.org/10.1109/ACCESS.2024.3376670
• [3] Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On predictive maintenance in Industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 8081. https://doi.org/10.3390/app12168081
• [4] Ahammed, T. B., & Patgiri, R. (2020). 6G and AI: The emergence of future forefront technology. 2020 Advanced Communication Technologies and Signal Processing (ACTS) (pp. 1-6). IEEE. https://doi.org/10.1109/ACTS49415.2020.9350396
• [5] Akhtar, M. W., Hassan, S. A., Ghaffar, R., Jung, H., Garg, S., & Hossain, M. S. (2020). The shift to 6G communications: vision and requirements. Human-centric Computing and Information Sciences, 10, 53. https://doi.org/10.1186/s13673-020-00258-2
• [6] Al-Jaroodi, J., Abukhousa, E., & Mohamed, N. (2020). Health 4.0: On the way to realizing the healthcare of the future. IEEE Access, 8, 211189-211210. https://doi.org/10.1109/access.2020.3038858
• [7] Alshahrani, H., Maray, M., Aljebreen, M., Alymani, M., Ahmed Elfaki, M., Al Duhayyim, M., Balaji, P., & Gupta, D. (2023). Energy aware routing with optimal deep learning based anomaly detection in 6G-IoT networks. Sustainable Energy Technologies and Assessments, 60, 103494. https://doi.org/10.1016/j.seta.2023.103494
• [8] Assad, F., Konstantinov, S., Nureldin, H., Waseem, M., Rushforth, E., Ahmad, B., & Harrison, R. (2021). Maintenance and digital health control in smart manufacturing based on condition monitoring. Procedia CIRP, 97, 142-147. https://doi.org/https://doi.org/10.1016/j.procir.2020.05.216
• [9] Bécue, A., Praça, I., & Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities. Artificial Intelligence Review, 54(5), 3849-3886. https://doi.org/10.1007/s10462-020-09942-2
• [10] Dang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020). What should 6G be? Nature Electronics, 3, 20-29. https://doi.org/10.1038/s41928-019-0355-6
• [11] Darman, I., Mahmood, M. K., Chaudhry, S. A., Khan, S. A., & Lim, H. (2022). Designing an enhanced user authenticated key management scheme for 6G-based industrial applications. IEEE Access, 10, 92774-92787. https://doi.org/10.1109/ACCESS.2022.3198642
• [12] Deng, J., Zeng, J., Mai, S., Jin, B., Yuan, B., You, Y., Lu, S., & Yang, M. (2021). Analysis and prediction of ship energy efficiency using 6G big data internet of things and artificial intelligence technology. International Journal of System Assurance Engineering and Management, 12, 824–834. https://doi.org/10.1007/s13198-021-01116-9
• [13] Dohler, M., Mahmoodi, T., Lema, M. A., Condoluci, M., Sardis, F., Antonakoglou, K., & Aghvami, H. (2017). Internet of skills, where robotics meets AI, 5G and the Tactile Internet. 2017 European Conference on Networks and Communications (EuCNC) (pp. 1-5). IEEE. https://doi.org/10.1109/EuCNC.2017.7980645
• [14] Elaziz, M. A., Dahou, A., Mabrouk, A., Ibrahim, R. A., & Aseeri, A. O. (2023). Medical image classifications for 6G IoT-Enabled smart health systems. Diagnostics, 13(5), 834. https://doi.org/10.3390/diagnostics13050834
• [15] Faouzi, D., Pallathadka, H., Abdullaev, S., Asaad, R. R., Aska, S., & Haroon, N. H. (2023). Probing the impact of process variables in laser-welded aluminum alloys: A Machine Learning study. Materials Today Communications, 38, 107660. https://doi.org/10.1016/j.mtcomm.2023.107660
• [16] Ghildiyal, Y., Singh, R., Alkhayyat, A., Gehlot, A., Malik, P., Sharma, R., Akram, S. V., & Alkwai, L. M. (2023). An imperative role of 6G communication with perspective of industry 4.0: Challenges and research directions. Sustainable Energy Technologies and Assessments, 56, 103047. https://doi.org/10.1016/j.seta.2023.103047
• [17] Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869
• [18] Gui, G., Liu, M., Tang, F., Kato, N., & Adachi, F. (2020). 6G: Opening new horizons for integration of comfort, security, and intelligence. IEEE Wireless Communications, 27(5), 126-132. https://doi.org/10.1109/MWC.001.1900516
• [19] Feng, H., Cui, Z., Han, C., Ning, J., & Yang, T. (2021). Bidirectional green promotion of 6G and AI: Architecture, solutions, and platform, IEEE Network, 35(6), 57-63. https://doi.org/10.1109/MNET.101.2100285
• [20] Han, B., Habibi, M. A., Richerzhagen, B., Schindhelm, K., Zeiger, F., Lamberti, F., Pratticò, F. G., Upadhya, K., Korovesis, C., Belikaidis, I.-P., Demestichas, P., Yuan, S., & Schotten, H. D. (2023). Digital twins for Industry 4.0 in the 6G era. IEEE Open Journal of Vehicular Technology, 4, 820-835. https://doi.org/10.1109/OJVT.2023.3325382
• [21] Han, S., Xie, T., & Li, C.-L. (2021). Greener physical layer technologies for 6G mobile communications, IEEE Communications Magazine, 59(4), 68-74. https://doi.org/10.1109/MCOM.001.2000484
• [22] Harahap, T. H., Mansouri, S., Abduallah, O. S., Uinarni, H., Askar, S., Jabbar, T. L., Alawadi, A. H., & Hassan, A. Y. (2024). An artificial intelligence approach to predict infants’ health status at birth. International Journal of Medical Informatics, 183, 105338. https://doi.org/10.1016/j.ijmedinf.2024.105338
• [23] Hijji, M., Iqbal, R., Pandey, A. K., Doctor, F., Karyotis, C., Rajeh, W., Alshehri, A., & Aradah, F. (2023). 6G connected vehicle framework to support intelligent road maintenance using Deep Learning data fusion. IEEE Transactions on Intelligent Transportation Systems, 24(7), 7726-7735. https://doi.org/10.1109/TITS.2023.3235151
• [24] Hussein, D. H., & Askar, S. (2023). Federated learning enabled SDN for routing emergency safety messages (ESMs) in IoV under 5G environment. IEEE Access, 11, 141723-141739. https://doi.org/10.1109/ACCESS.2023.3343613
• [25] Ibrahim, M. A., & Askar, S. (2023). An intelligent scheduling strategy in fog computing system based on multi-objective deep reinforcement learning algorithm. IEEE Access, 11, 133607-133622. https://doi.org/10.1109/ACCESS.2023.3337034
• [26] Jiang, W., Han, B., Habibi, M. A., & Schotten, H. (2021). The road towards 6G: A comprehensive survey. IEEE Open Journal of the Communications Society, 2, 334-366. https://doi.org/10.1109/OJCOMS.2021.3057679
• [27] Kuruvatti, N. P., Habibi, M. A., Partani, S., Han, B., Fellan, A., & Schotten, H. D. (2022). Empowering 6G communication systems with digital twin technology: A comprehensive survey. IEEE Access, 10, 112158-112186. https://doi.org/10.1109/ACCESS.2022.3215493
• [28] Liang, J., Li, L., & Zhao, C. (2021). A transfer learning approach for compressed sensing in 6G-IoT. IEEE Internet of Things Journal, 8(20), 15276-15283. https://doi.org/10.1109/JIOT.2021.3053088
• [29] Liu, G., Huang, Y., Li, N., Dong, J., Jin, J., Wang, Q., & Li, N. (2020). Vision, requirements and network architecture of 6G mobile network beyond 2030. China Communications,, 17(9), 92-104,. https://doi.org/10.23919/JCC.2020.09.008
• [30] Liu, S., & Zhang, J. (2021). Local alignment deep network for infrared-visible cross-modal person reidentification in 6G-enabled Internet of Things. IEEE Internet of Things Journal, 8(20), 15170-15179. https://doi.org/10.1109/JIOT.2020.3038794
• [31] Uusitalo, M. A., Rugeland, P., Boldi, M. R., Strinati, E. C., Demestichas, P., Ericson, M., Fettweis, G. P., Filippou, M. C., Gati, A., Hamon, M.-H., Hoffmann, M., Latva-aho, M., Pärssinen, A., Richerzhagen, B., Schotten, H., Svensson, T., Wikström, G., Wymeersch, H., Ziegler, V., & Zou, Y. (2021). 6G vision, value, use cases and technologies from European 6G agship project Hexa-X. IEEE Access, 9, 160004-160020. https:/doi.org/10.1109/ACCESS.2021.3130030
• [32] Mahmood, N. H., Berardinelli, G., Khatib, E. J., Hashemi, R., Lima, C. D., & Latva-aho, M. (2023). A functional architecture for 6G special-purpose industrial IoT networks. IEEE Transactions on Industrial Informatics, 19(3), 2530-2540. https://doi.org/10.1109/TII.2022.3182988
• [33] Mao, B., Tang, F., Kawamoto, Y., & Kato, N. (2021). Optimizing computation offloading in satellite-UAV-served 6G IoT: A Deep Learning approach. IEEE Network, 35(4), 102-108. https://doi.org/10.1109/MNET.011.2100097
• [34] Mezair, T., Djenouri, Y., Belhadi, A., Srivastava, G., & Lin, J. C.-W. (2022). A sustainable deep learning framework for fault detection in 6G Industry 4.0 heterogeneous data environments. Computer Communications, 187, 164-171. https://doi.org/10.1016/j.comcom.2022.02.010
• [35] Nashwan, S., & Nashwan, I. I. H. (2021). Reducing the overhead messages cost of the SAK-AKA authentication scheme for 4G/5G mobile networks. IEEE Access, 9, 97539-97545. https://doi.org/10.1109/ACCESS.2021.3094045
• [36] Nayak, S., & Patgiri, R. (2020a). 6G communication: Envisioning the key issues and challenges. EAI Endorsed Transactions on Internet of Things, 6(24), e1. https://doi.org/10.4108/eai.11-11-2020.166959
• [37] Nayak, S., & Patgiri, R. (2020b). A vision on intelligent medical service for emergency on 5G and 6G communication era. EAI Endorsed Transactions on Internet of Things, 6(22), e2. https://doi.org/10.4108/eai.17-8-2020.166293
• [38] Porambage, P., Gür, G., Osorio, D. P. M., Liyanage, M., Gurtov, A., & Ylianttila, M. (2021). The roadmap to 6G security and privacy. IEEE Open Journal of the Communications Society, 2, 1094-1122. https://doi.org/10.1109/OJCOMS.2021.3078081
• [39] Padhi, P. K., & Charrua-Santos, F. (2021). 6G enabled industrial internet of everything: Towards a theoretical framework. Applied System Innovation, 4(1), 11. https://doi.org/10.3390/asi4010011
• [40] Pallathadka, H., Naser, S. J., Askar, S., Al. Husseini, E. Q., Abdullaeva, B. S., & Haroon, N. H. (2023). Scheduling of multiple energy consumption in the smart buildings with peak demand management. International Journal of Integrated Engineering, 15(4), 311-321.
• [41] Pech, M., Vrchota, J., & Bednář, J. (2021). Predictive maintenance and intelligent sensors in smart factory: review. Sensors, 21(4), 1470. https://doi.org/10.3390/s21041470
• [42] Qi, Q., Chen, X., Zhong, C., & Zhang, Z. (2020). Integration of energy, computation and communication in 6G cellular Internet of Things. IEEE Communications Letters, 24(6), 1333-1337. https://doi.org/10.1109/LCOMM.2020.2982151
• [43] Rao, S. K. (2021). Data-driven business model innovation for 6G. Journal of ICT Standardization, 9(03), 405-426. https://doi.org/10.13052/jicts2245-800X.935
• [44] Sarker, I. H. (2021). Machine Learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x
• [45] Shahraki, A., Abbasi, M., Piran, M. J., & Taherkordi, A. (2021). A comprehensive survey on 6G networks: Applications, core services, enabling technologies, and future challenges. arXiv, abs/2101.12475. https://doi.org/10.48550/arXiv.2101.12475
• [46] Sharma, I., Gupta, K. S., Mishra, A., & Askar, S. (2023). Synchronous federated learning based multi unmanned aerial vehicles for secure applications. Scalable Computing Practice and Experience, 24(3), 191-201. https://doi.org/10.12694/scpe.v24i3.2136
• [47] Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., & Cesarotti, V. (2020). Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Computers in Industry, 123, 103335. https://doi.org/https://doi.org/10.1016/j.compind.2020.103335
• [48] Sliwa, B., Adam, R., & Wietfeld, C. (2021). Client-based intelligence for resource efficient vehicular big data transfer in future 6G networks. IEEE Transactions on Vehicular Technology, 70(6), 5332-5346. https://doi.org/10.1109/TVT.2021.3060459
• [49] Tariq, F., Khandaker, M. R. A., Wong, K.-K., Imran, M. A., Bennis, M., & Debbah, M. (2020). A speculative study on 6G, IEEE Wireless Communications Magazine,, 27(4), 118-125. https://doi.org/ 10.1109/MWC.001.1900488
• [50] Wang, S., Qureshi, M., Miralles-Pechuan, L., Huynh-The, T., Gadekallu, T., & Liyanage, M. (2021). Applications of explainable AI for 6G: Technical aspects, use cases, and research challenges. ArXiv abs/2112.04698. https://doi.org/10.48550/arXiv.2112.04698
• [51] Wang, W., Liu, F., Zhi, X., Zhang, T., & Huang, C. (2021). An integrated Deep Learning algorithm for detecting lung nodules with low-dose CT and its application in 6G-enabled internet of medical things. IEEE Internet of Things Journal, 8(7), 5274-5284. https://doi.org/10.1109/JIOT.2020.3023436
• [52] Wang, Y., Tian, Y., Hei, X., Zhu, L., & Ji, W. (2021). A novel IoV block-streaming service awareness and trusted verification scheme in 6G. IEEE Transactions on Vehicular Technology, 70(6), 5197-5210. https://doi.org/10.1109/TVT.2021.3063783
• [53] Zhang, S., & Zhu, D. (2020). Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities. Computer Networks, 183, 107556. https://doi.org/https://doi.org/10.1016/j.comnet.2020.107556