Synergizing 5G and Artificial Intelligence: Catalyzing the Evolution of Industry 4.0

Humadi, Mustafa; Faraj, Lydia Naseer; Pardaz Zhumabaevna, Kozhobekova; Merzah, Hanaa Hameed; Kalefa Hasan, Talib

doi:10.22034/jipm.2025.728125

Synergizing 5G and Artificial Intelligence: Catalyzing the Evolution of Industry 4.0

Document Type : Original Article

Authors

Mustafa Humadi ¹

Lydia Naseer Faraj ²

Kozhobekova Pardaz Zhumabaevna ³

Hanaa Hameed Merzah ⁴

Talib Kalefa Hasan ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad 10064, Iraq

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728125

Abstract

ABSTRACT
Background: The marriage of 5G and Artificial Intelligence (AI) has been brought forward as a key enabler of Industry 4.0 and smart city applications. These technologies solve the problem of latency, scalability, and energy use, providing technology support for real-time decision-making and efficient organization of work. Nevertheless, studies regarding their individual and collective effects in a plethora of industrial and urban contexts are still limited.
Objective: The objective of this research is to assess the performance, energy saving, and expansibility of 5G and AI synergies in manufacturing, logistics, healthcare, and smart city applications and highlight their challenges and potential for further exploration.
Methods: An experimental data collection, mathematical modeling and comparative analysis approach was employed. Performance indicators including latency, possible and actual throughput, power usage, and predicting achievement were measured in real pilot tests implemented in dense networks and IoT contexts. Available data were compared with other similar studies to gain an understanding of the results.
Results: The conjoin with 5G and AI suggested potential optimization of process; the latency has been decreased to more than 90%, its predictive maintenance was sharpened, and its power consumption was decrease to 75%. The feasibility of extending scalability and system reliability of the protocol was confirmed in dense IoT environments, with further potential for emission reduction.
Conclusion: The study identifies the use of 5G in Industry 4.0 with AI in addressing dynamic issues but potential drawback includes scalability and security. More studies should be conducted on the novel hybrid architectures and 6G integration concerning more extensive areas.

Keywords

KEYWORDS: 5G

Artificial Intelligence (AI)

Industry 4.0

IoT

machine learning

robotics

automation

smart manufacturing

real-time data

predictive analytics

References

Abbas, T. N. A., Hameed, R., Kadhim, A. A., and Qasim, N. H. (2024). Artificial intelligence and criminal liability: exploring the legal implications of ai-enabled crimes. Encuentros. Revista de Ciencias Humanas, Teoría Social y Pensamiento Crítico., (22 ), 140-159. https://doi.org/:10.5281/zenodo.13386675

Abidi, M. H., Mohammed, M. K., and Alkhalefah, H. (2022). Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing. Sustainability, 14 (6). https://doi.org/:10.3390/su14063387.

Adjogble, F. K., Warschat, J., and Hemmje, M. (2023). Advanced Intelligent Manufacturing in Process Industry Using Industrial Artificial Intelligence. Portland International Conference on Management of Engineering and Technology (PICMET), 23-27 July. https://doi.org/:10.23919/PICMET59654.2023.10216797.

Almiani, M., AbuGhazleh, A., Jararweh, Y., and Razaque, A. (2021). DDoS detection in 5G-enabled IoT networks using deep Kalman backpropagation neural network. International Journal of Machine Learning and Cybernetics, 12 (11), 3337-3349. https://doi.org/:10.1007/s13042-021-01323-7

Attaran, M. (2023). The impact of 5G on the evolution of intelligent automation and industry digitization. Journal of Ambient Intelligence and Humanized Computing, 14 (5), 5977-5993. https://doi.org/:10.1007/s12652-020-02521-x

Cantero, M., Inca, S., Ramos, A., Fuentes, M., Martín-Sacristán, D., and Monserrat, J. F. (2023). System-Level Performance Evaluation of 5G Use Cases for Industrial Scenarios. IEEE Access, 11, 37778-37789. https://doi.org/:10.1109/ACCESS.2023.3266981

Chi, H. R., Wu, C. K., Huang, N. F., Tsang, K. F., and Radwan, A. (2023). A Survey of Network Automation for Industrial Internet-of-Things Toward Industry 5.0. IEEE Transactions on Industrial Informatics, 19 (2), 2065-2077. https://doi.org/:10.1109/TII.2022.3215231

Fang, J., and Ma, A. (2021). IoT Application Modules Placement and Dynamic Task Processing in Edge-Cloud Computing. IEEE Internet of Things Journal, 8 (16), 12771-12781. https://doi.org/:10.1109/JIOT.2020.3007751

Feng, D., Lai, L., Luo, J., Zhong, Y., Zheng, C., and Ying, K. (2021). Ultra-reliable and low-latency communications: applications, opportunities and challenges. Science China Information Sciences, 64 (2), 120301. https://doi.org/:10.1007/s11432-020-2852-1

Jagatheesaperumal, S. K., Rahouti, M., Ahmad, K., Al-Fuqaha, A., and Guizani, M. (2022). The Duo of Artificial Intelligence and Big Data for Industry 4.0: Applications, Techniques, Challenges, and Future Research Directions. IEEE Internet of Things Journal, 9 (15), 12861-12885. https://doi.org/:10.1109/JIOT.2021.3139827

Kartsakli, E., Perez-Romero, J., Sallent, O., Bartzoudis, N., Frascolla, V., Mohalik, S. K., Metsch, T., et al. (2023). AI-Powered Edge Computing Evolution for Beyond 5G Communication Networks. Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 6-9 June. https://doi.org/:10.1109/EuCNC/6GSummit58263.2023.10188371.

Kaur, B., Sood, K., and Grima, S. (2022). A systematic review on forensic accounting and its contribution towards fraud detection and prevention. Journal of Financial Regulation and Compliance.

Kaur, M., Khan, M. Z., Gupta, S., and Alsaeedi, A. (2022). Adoption of Blockchain With 5G Networks for Industrial IoT: Recent Advances, Challenges, and Potential Solutions. IEEE Access, 10, 981-997. https://doi.org/:10.1109/ACCESS.2021.3138754

Lee, J., Kim, H., Park, C., Kim, Y., and Park, J. G. (2022). AI-based Network Security Enhancement for 5G Industrial Internet of Things Environments. 13th International Conference on Information and Communication Technology Convergence (ICTC), 19-21 Oct. 2022. https://doi.org/:10.1109/ICTC55196.2022.9952490.

Mahmood, A., Beltramelli, L., Abedin, S. F., Zeb, S., Mowla, N. I., Hassan, S. A., Sisinni, E., et al. (2022). Industrial IoT in 5G-and-Beyond Networks: Vision, Architecture, and Design Trends. IEEE Transactions on Industrial Informatics, 18 (6), 4122-4137. https://doi.org/:10.1109/TII.2021.3115697

Massaro, A. (2022). Advanced Control Systems in Industry 5.0 Enabling Process Mining. Sensors, 22 (22). https://doi.org/:10.3390/s22228677.

Nameer, Q., Aqeel, J., and Muthana, M. (2023). The Usages of Cybersecurity in Marine Communications. Transport Development, 3 (18). https://doi.org/:10.33082/td.2023.3-18.05

Qasim, N., and Fatah, O. (2022). The role of cyber security in military wars. V International Scientific and Practical Conference: Problems of cyber security of information and telecommunication systems (PCSITS). October 27 - 28. Kyiv, Ukraine.

Qasim, N., and Pyliavskyi, V. (2020). Color temperature line: forward and inverse transformation. Semiconductor physics, quantum electronics and optoelectronics, 23, 75-80. https://doi.org/:10.15407/spqeo23.01.075

Qasim, N., Shevchenko, Y. P., and Pyliavskyi, V. (2019). Analysis of methods to improve energy efficiency of digital broadcasting. Telecommunications and Radio Engineering, 78 (16). https://doi.org/:10.1615/TelecomRadEng.v78.i16.40

Qasim, N. H., Vyshniakov, V., Khlaponin, Y., and Poltorak, V. (2021). Concept in information security technologies development in e-voting systems. International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), 3 (9), 40-54.

Ridhawi, I. A., Aloqaily, M., Boukerche, A., and Jararweh, Y. (2021). Enabling Intelligent IoCV Services at the Edge for 5G Networks and Beyond. IEEE Transactions on Intelligent Transportation Systems, 22 (8), 5190-5200. https://doi.org/:10.1109/TITS.2021.3053095

Rodriguez, I., Mogensen, R. S., Fink, A., Raunholt, T., Markussen, S., Christensen, P. H., Berardinelli, G., et al. (2021). An Experimental Framework for 5G Wireless System Integration into Industry 4.0 Applications. Energies, 14 (15). https://doi.org/:10.3390/en14154444.

Tinh, B. T., Nguyen, L. D., Kha, H. H., and Duong, T. Q. (2022). Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges. IEEE Access, 10, 13311-13328. https://doi.org/:10.1109/ACCESS.2022.3146335

Wen, M., Li, Q., Kim, K. J., López-Pérez, D., Dobre, O. A., Poor, H. V., Popovski, P., et al. (2022). Private 5G Networks: Concepts, Architectures, and Research Landscape. IEEE Journal of Selected Topics in Signal Processing, 16 (1), 7-25. https://doi.org/:10.1109/JSTSP.2021.3137669

Yang, D., Mahmood, A., Hassan, S. A., and Gidlund, M. (2022). Guest Editorial: Industrial IoT and Sensor Networks in 5G-and-Beyond Wireless Communication. IEEE Transactions on Industrial Informatics, 18 (6), 4118-4121. https://doi.org/:10.1109/TII.2022.3142149

Yousif, O., Dawood, M., Jassem, F. T., and Qasim, N. H. (2024). Curbing crypto deception: evaluating risks, mitigating practices and regulatory measures for preventing fraudulent transactions in the middle east. Encuentros: Revista de Ciencias Humanas, Teoría Social y Pensamiento Crítico, (22), 311-334. https://doi.org/:10.5281/zenodo.13732337

Zou, X., Li, K., Zhou, J. T., Wei, W., and Chen, C. (2023). Robust Edge AI for Real-Time Industry 4.0 Applications in 5G Environment. IEEE Communications Standards Magazine, 7 (2), 64-70. https://doi.org/:10.1109/MCOMSTD.0008.2100019