5G and Edge AI for Smart Industries: Technologies Enabling Real-Time Industrial Intelligence
DOI:
https://doi.org/10.69996/jsihs.2026010Keywords:
5G, Edge AI, Smart Industries, Internet of Things, CommunicationAbstract
The merging of 5G communication and Edge Artificial Intelligence (Edge AI) is driving smart industrial systems toward a revolutionary change in how they handle real-time data processing, achieve extremely fast communication speeds, and make intelligent decisions directly at the network edge. This analysis examines how 5G and Edge AI work together to power cutting-edge industrial applications like predictive maintenance, autonomous robotics, smart manufacturing, and industrial automation systems. The combination of 5G's ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) features, paired with Edge AI's distributed intelligence and quick response capabilities, creates opportunities for better operational performance, increased flexibility, and stronger system reliability. This paper presents a thorough examination of key enabling technologies such as network slicing, edge computing frameworks, AI models designed for edge environments, and integration systems. The article also addresses major implementation obstacles including security concerns, scalability requirements, interoperability issues, and energy consumption challenges. Additionally, it explores current research developments and future pathways for creating completely intelligent and connected industrial environments.
References
[1] K. K. Vaigandla and N. Venu, “A survey on future generation wireless communications5G: Multiple access techniques, physical layer security, beamforming approach,” J. Inf. Comput. Sci., vol. 11, no. 9, pp. 449–474, 2021.
[2] Z. Li, “5G URLLC: Design challenges and system concepts,” in Proc. 2018 15th Int. Symp. Wireless Commun. Syst. (ISWCS), 2018.
[3] K. K. Vaigandla, “Communication technologies and challenges on 6G networks for the Internet: Internet of Things (IoT) based analysis,” in Proc. 2022 2nd Int. Conf. Innovative Practices Technol. Manage. (ICIPTM), vol. 2, 2022.
[4] B. Liang, M. A. Gregory and S. Li, “Multi-access edge computing fundamentals, services, enablers and challenges: A complete survey,” J. Netw. Comput. Appl., vol. 199, 2022.
[5] T. Nguyen, H. Nguyen and T. N. Gia, “Exploring the integration of edge computing and blockchain IoT: Principles, architectures, security, and applications,” J. Netw. Comput. Appl., vol. 226, 2024.
[6] Y. O. Imam-Fulani, “5G frequency standardization, technologies, channel models, and network deployment: Advances, challenges, and future directions,” Sustainability, vol. 15, no. 6, 2023.
[7] S. Senger and P. K. Malik, “A comprehensive survey of massive-MIMO based on 5G antennas,” Int. J. RF Microw. Comput.-Aided Eng., vol. 32, no. 12, 2022.
[8] K. K. Vaigandla and N. Venu, “Survey on massive MIMO: Technology, challenges, opportunities and benefits,” 2021.
[9] R. Dangi et al., “Study and investigation on 5G technology: A systematic review,” Sensors, vol. 22, no. 1, Art. no. 26, 2022.
[10] S. Mattisson, “An overview of 5G requirements and future wireless networks: Accommodating scaling technology,” IEEE Solid-State Circuits Mag., vol. 10, no. 3, pp. 54–60, 2018.
[11] A. Tikhomirov, E. Omelyanchuk, and A. Semenova, “Recommended 5G frequency bands evaluation,” in Proc. 2018 Syst. Signals Generating Processing Field Board Commun., 2018.
[12] K. K. Vaigandla, “A systematic survey on artificial intelligence in 6G wireless networks: Security, opportunities, applications, advantages, future research directions and challenges,” Babylonian J. Artif. Intell., pp. 99–106, 2025.
[13] J. Lu et al., “5G enhanced service-based core design,” in Proc. 2019 28th Wireless Opt. Commun. Conf. (WOCC), 2019.
[14] M. Zieba, M. Natkaniec and P. Borylo, “Cloud-enabled deployment of 5G core network with analytics features,” Appl. Sci., vol. 14, no. 16, 2024.
[15] M. Attaran, “The impact of 5G on the evolution of intelligent automation and industry digitization,” J. Ambient Intell. Humanized Comput., vol. 14, no. 5, pp. 5977–5993, 2023.
[16] P. Orosz, “QoS guarantees for Industrial IoT applications over LTE-A: A feasibility study,” in Proc. 2019 IEEE Int. Conf. Ind. Cyber Phys. Syst. (ICPS), 2019.
[17] Y. Xu, “Artificial intelligence: A powerful paradigm for scientific research,” The Innovation, vol. 2, no. 4, 2021.
[18] T. Wang, “Deploying AI on edge: Advancement and challenges in edge intelligence,” Mathematics, vol. 13, no. 11, Art. no. 1878, 2025.
[19] R. Singh and S. S. Gill, “Edge AI: A survey,” Internet Things Cyber-Phys. Syst., vol. 3, pp. 71–92, 2023.
[20] K. K. Vaigandla, S. Bolla and R. Karne, “A survey on future generation wireless communications-6G: Requirements, technologies, challenges and applications,” Int. J., vol. 10, no. 5, pp. 52021, 2021.
[21] J. Cheng, “5G in manufacturing: A literature review and future research,” Int. J. Adv. Manuf. Technol., vol. 131, no. 11, pp. 5637–5659, 2024.
[22] S. S. Gill, “Edge AI: A taxonomy, systematic review and future directions,” Cluster Comput., vol. 28, no. 1, 2025.
[23] J. Shao and X. Li, “AI flow at the network edge,” IEEE Netw., 2025.
[24] M. Elahi, “A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment,” Discover Artif. Intell., vol. 3, no. 1, 2023.
[25] M. Ergen et al., “Edge computing in future wireless networks: A comprehensive evaluation and vision for 6G and beyond,” ICT Express, vol. 10, no. 5, pp. 1151–1173, 2024.
[26] M. Aizat, A. Azmin and W. Rahiman, “A survey on navigation approaches for automated guided vehicle robots in dynamic surrounding,” IEEE Access, vol. 11, pp. 33934–33955, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Sensors, IoT & Health Sciences (JSIHS,ISSN: 2584-2560)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Fringe Global Scientific Press publishes all the papers under a Creative Commons Attribution-Non-Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) license. Authors have the liberty to replicate and distribute their work. Authors have the ability to use either the whole or a portion of their piece in compilations or other publications that include their own work. Please see the licensing terms for more information on reusing the work.
