THE UTILIZATION OF 6G IN INDUSTRY 4.0
Article Sidebar
Open full text
Issue Vol. 20 No. 2 (2024)
-
FEW-SHOT LEARNING WITH PRE-TRAINED LAYERS INTEGRATION APPLIED TO HAND GESTURE RECOGNITION FOR DISABLED PEOPLE
Mohamed ELBAHRI, Nasreddine TALEB, Sid Ahmed El Mehdi ARDJOUN, Chakib Mustapha Anouar ZOUAOUI1-23
-
DIGITAL NEWS CLASSIFICATION AND PUNCTUACTION USING MACHINE LEARNING AND TEXT MINING TECHNIQUES
Fernando Andrés CEVALLOS SALAS24-42
-
MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS
Jerzy JÓZWIK, Magdalena ZAWADA-MICHAŁOWSKA, Monika KULISZ, Paweł TOMIŁO, Marcin BARSZCZ, Paweł PIEŚKO, Michał LELEŃ, Kamil CYBUL43-59
-
EXAMINATION OF SUMMARIZED MEDICAL RECORDS FOR ICD CODE CLASSIFICATION VIA BERT
Dilek AYDOGAN-KILIC, Deniz Kenan KILIC, Izabela Ewa NIELSEN60-74
-
THE UTILIZATION OF 6G IN INDUSTRY 4.0
Hanan M. SHUKUR, Shavan ASKAR, Subhi R.M. ZEEBAREE75-89
-
APPLICATION OF EEMD-DFA ALGORITHMS AND ANN CLASSIFICATION FOR DETECTION OF KNEE OSTEOARTHRITIS USING VIBROARTHROGRAPHY
Anna MACHROWSKA, Robert KARPIŃSKI, Marcin MACIEJEWSKI, Józef JONAK, Przemysław KRAKOWSKI90-108
-
PREDICTING STATES OF EPILEPSY PATIENTS USING DEEP LEARNING MODELS
Boutkhil SIDAOUI109-125
-
IMPROVING E-LEARNING BY FACIAL EXPRESSION ANALYSIS
Amina KINANE DAOUADJI, Fatima BENDELLA126-137
-
EXPLORING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMANROBOT COOPERATION IN THE CONTEXT OF INDUSTRY 4.0
Hawkar ASAAD, Shavan ASKAR, Ahmed KAKAMIN, Nayla FAIQ138-156
-
AN AUTHENTICATION METHOD BASED ON A DIOPHANTINE MODEL OF THE COIN BAG PROBLEM
Krzysztof NIEMIEC, Grzegorz BOCEWICZ157-174
-
PREDICTION OF PATIENT’S WILLINGNESS FOR TREATMENT OF MENTAL ILLNESS USING MACHINE LEARNING APPROACHES
Mohammed Chachan YOUNIS175-193
-
AUTOMATION OF POLYCYSTIC OVARY SYNDROME DIAGNOSTICS THROUGH MACHINE LEARNING ALGORITHMS IN ULTRASOUND IMAGING
Roman GALAGAN, Serhiy ANDREIEV, Nataliia STELMAKH; Yaroslava RAFALSKA; Andrii MOMOT194-204
Archives
-
Vol. 21 No. 3
2025-10-05 12
-
Vol. 21 No. 2
2025-06-27 12
-
Vol. 21 No. 1
2025-03-31 12
-
Vol. 20 No. 4
2025-01-31 12
-
Vol. 20 No. 3
2024-09-30 12
-
Vol. 20 No. 2
2024-08-14 12
-
Vol. 20 No. 1
2024-03-30 12
-
Vol. 19 No. 4
2023-12-31 10
-
Vol. 19 No. 3
2023-09-30 10
-
Vol. 19 No. 2
2023-06-30 10
-
Vol. 19 No. 1
2023-03-31 10
-
Vol. 18 No. 4
2022-12-30 8
-
Vol. 18 No. 3
2022-09-30 8
-
Vol. 18 No. 2
2022-06-30 8
-
Vol. 18 No. 1
2022-03-30 7
-
Vol. 16 No. 4
2020-12-30 8
-
Vol. 16 No. 3
2020-09-30 8
-
Vol. 16 No. 2
2020-06-30 8
-
Vol. 16 No. 1
2020-03-30 8
Main Article Content
DOI
Authors
hanan.m.shukur@uoalkitab.edu.iq
Abstract
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.
Keywords:
References
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 DOI: https://doi.org/10.1109/ACCESS.2023.3241881
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 DOI: https://doi.org/10.1109/ACCESS.2024.3376670
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 DOI: https://doi.org/10.3390/app12168081
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 DOI: https://doi.org/10.1109/ACTS49415.2020.9350396
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 DOI: https://doi.org/10.1186/s13673-020-00258-2
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 DOI: https://doi.org/10.1109/ACCESS.2020.3038858
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 DOI: https://doi.org/10.1016/j.seta.2023.103494
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 DOI: https://doi.org/10.1016/j.procir.2020.05.216
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 DOI: https://doi.org/10.1007/s10462-020-09942-2
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 DOI: https://doi.org/10.1038/s41928-019-0355-6
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 DOI: https://doi.org/10.1109/ACCESS.2022.3198642
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 DOI: https://doi.org/10.1007/s13198-021-01116-9
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 DOI: https://doi.org/10.1109/EuCNC.2017.7980645
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 DOI: https://doi.org/10.3390/diagnostics13050834
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 DOI: https://doi.org/10.1016/j.mtcomm.2023.107660
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 DOI: https://doi.org/10.1016/j.seta.2023.103047
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 DOI: https://doi.org/10.1016/j.jclepro.2019.119869
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 DOI: https://doi.org/10.1109/MWC.001.1900516
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 DOI: https://doi.org/10.1109/MNET.101.2100285
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 DOI: https://doi.org/10.1109/OJVT.2023.3325382
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 DOI: https://doi.org/10.1109/MCOM.001.2000484
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 DOI: https://doi.org/10.1016/j.ijmedinf.2024.105338
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 DOI: https://doi.org/10.1109/TITS.2023.3235151
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 DOI: https://doi.org/10.1109/ACCESS.2023.3343613
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 DOI: https://doi.org/10.1109/ACCESS.2023.3337034
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 DOI: https://doi.org/10.1109/OJCOMS.2021.3057679
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 DOI: https://doi.org/10.1109/ACCESS.2022.3215493
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 DOI: https://doi.org/10.1109/JIOT.2021.3053088
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 DOI: https://doi.org/10.23919/JCC.2020.09.008
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 DOI: https://doi.org/10.1109/JIOT.2020.3038794
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 DOI: https://doi.org/10.1109/ACCESS.2021.3130030
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 DOI: https://doi.org/10.1109/TII.2022.3182988
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 DOI: https://doi.org/10.1109/MNET.011.2100097
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 DOI: https://doi.org/10.1016/j.comcom.2022.02.010
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 DOI: https://doi.org/10.1109/ACCESS.2021.3094045
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 DOI: https://doi.org/10.4108/eai.11-11-2020.166959
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 DOI: https://doi.org/10.4108/eai.17-8-2020.166293
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 DOI: https://doi.org/10.1109/OJCOMS.2021.3078081
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 DOI: https://doi.org/10.3390/asi4010011
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. DOI: https://doi.org/10.30880/ijie.2023.15.04.027
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 DOI: https://doi.org/10.3390/s21041470
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 DOI: https://doi.org/10.1109/LCOMM.2020.2982151
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 DOI: https://doi.org/10.13052/jicts2245-800X.935
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 DOI: https://doi.org/10.1007/s42979-021-00592-x
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
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 DOI: https://doi.org/10.12694/scpe.v24i3.2136
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 DOI: https://doi.org/10.1016/j.compind.2020.103335
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 DOI: https://doi.org/10.1109/TVT.2021.3060459
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 DOI: https://doi.org/10.1109/MWC.001.1900488
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
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 DOI: https://doi.org/10.1109/JIOT.2020.3023436
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 DOI: https://doi.org/10.1109/TVT.2021.3063783
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 DOI: https://doi.org/10.1016/j.comnet.2020.107556
Article Details
Abstract views: 952
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
