Skip to main content
SpringerLink
Log in

Machine Learning Techniques for Industrial Internet of Things

  • Chapter
  • First Online:
Learning Techniques for the Internet of Things

  • 178 Accesses

Abstract

Industrial Internet of Things (IIoT), which connects millions of smart devices, will allow for industrial use cases like smart cities and supply chain management with minimal human involvement in the future. The IIoT has revolutionized production by making data faster, more accurate, and more accessible to stakeholders at all levels. In the IIoT, machine learning (ML) techniques are frequently utilized to add intelligence to the industrial environment and manufacturing operations. For instance, timely and accurate data analysis is essential, and ML techniques are used to examine and comprehend the enormous amounts of data created by IoT devices. Organizations use ML algorithms to promote innovation, make smart decisions, and create autonomous industrial environments. IoT and ML are employed in manufacturing to enhance quality control, streamline production, and cut waste. For instance, producers can spot areas for improvement and carry out preventative maintenance before equipment faults occur by applying ML algorithms to analyze data from IoT sensors on factory equipment. Learning techniques in IIoT are critical to deliver rapid and accurate data analysis, essential for enhancing production quality, sustainability, and safety. Motivated by the abovementioned learning technology, in this chapter, we discuss the significance of ML and its benefits toward IIoT for processing real-time applications. We shed light on several key ML technologies for IIoT. Finally, we highlight several research challenges and outstanding concerns that need further addressing to realize the IIoT scenario.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abuhasel, Khaled Ali, and Mohammad Ayoub Khan. 2020. A secure Industrial Internet of Things (IIoT) framework for resource management in smart manufacturing. IEEE Access 8: 117354–117364. https://doi.org/10.1109/ACCESS.2020.3004711.

    Article  Google Scholar 

  • Akherfi, Khadija, et al. 2018. Mobile cloud computing for computation offloading: Issues and challenges. Applied Computing and Informatics 14 (1): 1–16.

    Article  Google Scholar 

  • Amjad, Anam, et al. 2021. A systematic review on the data interoperability of application layer protocols in industrial IoT. IEEE Access 9: 96528–96545. https://doi.org/10.1109/ACCESS.2021.3094763.

    Article  Google Scholar 

  • Amruthnath, Nagdev, and Tarun Gupta. 2018. Fault class prediction in unsupervised learning using model-based clustering approach. In 2018 International Conference on Information and Computer Technologies (ICICT), 5–12. https://doi.org/10.1109/INFOCT.2018.8356831.

    Google Scholar 

  • Ananya, A., et al. 2020. SysDroid: A dynamic ML-based android malware analyzer using system call traces. Cluster Computing 23 (4): 2789–2808.

    Article  Google Scholar 

  • Aouedi, Ons, et al. 2023. Federated semisupervised learning for attack detection in Industrial Internet of Things. IEEE Transactions on Industrial Informatics 19 (1): 286–295. https://doi.org/10.1109/TII.2022.3156642.

    Article  Google Scholar 

  • Babbar, Himanshi, et al. 2022. Intelligent edge load migration in SDN-IIoT for smart healthcare. IEEE Transactions on Industrial Informatics 18 (11): 8058–8064. https://doi.org/10.1109/TII.2022.3172489.

    Article  MathSciNet  Google Scholar 

  • Bi, Suzhi, and Ying Jun Zhang. 2018. Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading. IEEE Transactions on Wireless Communications 17 (6): 4177–4190. https://doi.org/10.1109/TWC.2018.2821664.

    Article  Google Scholar 

  • Boyes, Hugh, et al. 2018. The industrial internet of things (IIoT): An analysis framework. Computers in Industry 101: 1–12.

    Article  Google Scholar 

  • Carbonell, Jaime G., et al. 1983. An overview of machine learning. In Machine Learning, 3–23.

    Google Scholar 

  • Chehri, Abdellah, and Gwanggil Jeon. 2019. The industrial internet of things: examining how the IIoT will improve the predictive maintenance. In Innovation in Medicine and Healthcare Systems, and Multimedia: Proceedings of KES-InMed-19 and KES-IIMSS-19 Conferences, 517–527. Berlin: Springer.

    Chapter  Google Scholar 

  • Chen, Baotong, and Jiafu Wan. 2019. Emerging trends of ML-based intelligent services for Industrial Internet of Things (IIoT). In 2019 Computing, Communications and IoT Applications (ComComAp), 135–139. https://doi.org/10.1109/ComComAp46287.2019.9018815.

  • Churcher, Andrew, et al. 2021. An experimental analysis of attack classification using machine learning in IoT networks. Sensors 21 (2): 446.

    Article  Google Scholar 

  • Costa, Felipe S., et al. 2020. Fasten IIoT: An open real-time platform for vertical, horizontal and end-to-end integration. Sensors 20 (19): 5499.

    Article  Google Scholar 

  • Fumera, G., and F. Roli. 2005. A theoretical and experimental analysis of linear combiners for multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (6): 942–956. https://doi.org/10.1109/TPAMI.2005.109.

    Article  Google Scholar 

  • Handelman, Guy S., et al. 2018. Peering into the black box of artificial intelligence: Evaluation metrics of machine learning methods. AJR. American Journal of Roentgenology 212 (1): 38–43.

    Article  Google Scholar 

  • Hassan, Mohammad Mehedi, et al. 2021. An adaptive trust boundary protection for IIoT networks using deep-learning feature-extraction-based semisupervised model. IEEE Transactions on Industrial Informatics 17 (4): 2860–2870. https://doi.org/10.1109/TII.2020.3015026.

    Article  Google Scholar 

  • Hazra, Abhishek, Ahmed Alkhayyat, et al. 2022. Blockchain-aided integrated edge framework of cybersecurity for Internet of Things. IEEE Consumer Electronics Magazine, 1–1. https://doi.org/10.1109/MCE.2022.3141068.

  • Hazra, Abhishek, Mainak Adhikari, and Tarachand Amgoth. 2022. Dynamic service deployment strategy using reinforcement learning in edge networks. In 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), 1–6. https://doi.org/10.1109/IC3SIS54991.2022.9885498.

    Google Scholar 

  • Hazra, Abhishek, Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Sri-rama. 2021. A comprehensive survey on interoperability for IIoT: Taxonomy, standards, and future directions. ACM Computing Surveys 55 (1). ISSN: 0360-0300. https://doi.org/10.1145/3485130.

  • Hazra, Abhishek, Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Sri-rama. 2022a. Fog computing for energy-efficient data offloading of IoT applications in industrial sensor networks. IEEE Sensors Journal 22 (9): 8663–8671. https://doi.org/10.1109/JSEN.2022.3157863.

    Article  Google Scholar 

  • Hazra, Abhishek, Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Sri-rama. 2022b. Intelligent service deployment policy for next-generation industrial edge networks. IEEE Transactions on Network Science and Engineering 9 (5): 3057–3066. https://doi.org/10.1109/TNSE.2021.3122178.

    Article  MathSciNet  Google Scholar 

  • Hazra, Abhishek, Mainak Adhikari, Tarachand Amgoth, and Satish Narayana Sri-rama. 2023. Collaborative AI-enabled intelligent partial service provisioning in green industrial fog networks. IEEE Internet of Things Journal 10 (4): 2913–2921. https://doi.org/10.1109/JIOT.2021.3110910.

    Article  Google Scholar 

  • Hazra, Abhishek, Praveen Kumar Donta, et al. 2023. Cooperative transmission scheduling and computation offloading with collaboration of fog and cloud for industrial IoT applications. IEEE Internet of Things Journal 10 (5): 3944–3953. https://doi.org/10.1109/JIOT.2022.3150070.

    Article  Google Scholar 

  • Hazra, Abhishek, and Tarachand Amgoth. 2022. CeCO: Cost-efficient computation offloading of IoT applications in green industrial fog networks. IEEE Transactions on Industrial Informatics 18 (9): 6255–6263. https://doi.org/10.1109/TII.2021.3130255.

    Article  Google Scholar 

  • Hore, Umesh W., and DG Wakde. 2022. An effective approach of IIoT for anomaly detection using unsupervised machine learning approach. Journal of IoT in Social, Mobile, Analytics, and Cloud 4: 184–197.

    Google Scholar 

  • Hou, Jianwei, et al. 2019. A survey on internet of things security from data perspectives. Computer Networks 148: 295–306.

    Article  Google Scholar 

  • Huang, Huakun, et al. 2020. Real-time fault detection for IIoT facilities using GBRBM-Based DNN. IEEE Internet of Things Journal 7 (7): 5713–5722. https://doi.org/10.1109/JIOT.2019.2948396.

    Article  MathSciNet  Google Scholar 

  • Huang, Zijie, et al. 2022. An energy-efficient and trustworthy unsupervised anomaly detection framework (EATU) for IIoT. ACM Transactions on Sensor Networks 18 (4): 1–18.

    Article  MathSciNet  Google Scholar 

  • Hussain, Fatima, et al. 2020. Machine learning in IoT security: Current solutions and future challenges. IEEE Communications Surveys & Tutorials 22 (3): 1686–1721. https://doi.org/10.1109/COMST.2020.2986444.

    Article  Google Scholar 

  • Jaidka, Himanshu, et al. 2020. Evolution of IoT to IIoT: Applications & challenges. In Proceedings of the International Conference on Innovative Computing & Communications (ICICC).

    Google Scholar 

  • Javaid, Mohd, et al. 2022. Significance of machine learning in healthcare: Features, pillars and applications. International Journal of Intelligent Networks 3: 58–73.

    Article  Google Scholar 

  • Khattab, Ahmed, and Nouran Youssry. 2020. Machine learning for IoT systems. In Internet of Things (IoT) Concepts and Applications, 105–127.

    Google Scholar 

  • Kollmannsberger, Stefan, et al. 2021. Fundamental concepts of machine learning. In Deep Learning in Computational Mechanics: An Introductory Course, 5–18.

    Google Scholar 

  • Kozma, Dániel, et al. 2019. Supply chain management and logistics 4.0 - A study on arrowhead framework integration. In 2019 8th International Conference on Industrial Technology and Management (ICITM), 12–16. https://doi.org/10.1109/ICITM.2019.8710670.

    Google Scholar 

  • Kuang, Zhufang, et al. 2019. Partial offloading scheduling and power allocation for mobile edge computing systems. IEEE Internet of Things Journal 6 (4): 6774–6785. https://doi.org/10.1109/JIOT.2019.2911455.

    Article  Google Scholar 

  • Kumar, Karthik, et al. 2013. A survey of computation offloading for mobile systems. Mobile Networks and Applications 18: 129–140.

    Article  Google Scholar 

  • Lin, Jie, et al. 2017. A survey on Internet of Things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal 4 (5): 1125–1142. https://doi.org/10.1109/JIOT.2017.2683200.

    Article  Google Scholar 

  • Lin, Yijing, et al. 2023. A novel architecture combining oracle with decentralized learning for IIoT. IEEE Internet of Things Journal 10 (5): 3774–3785. https://doi.org/10.1109/JIOT.2022.3150789.

    Article  Google Scholar 

  • Liu, Mengting, et al. 2019. Performance optimization for blockchain-enabled Industrial Internet of Things (IIoT) systems: A deep reinforcement learning approach. IEEE Transactions on Industrial Informatics 15 (6): 3559–3570. https://doi.org/10.1109/TII.2019.2897805.

    Article  Google Scholar 

  • Lu, Yinzhi, et al. 2023. An intelligent deterministic scheduling method for ultralow latency communication in edge enabled Industrial Internet of Things. IEEE Transactions on Industrial Informatics 19 (2): 1756–1767. https://doi.org/10.1109/TII.2022.3186891.

    Article  Google Scholar 

  • Mukherjee, Mithun, et al. 2020. Revenue maximization in delay-aware computation offloading among service providers with fog federation. IEEE Communications Letters 24 (8): 1799–1803. https://doi.org/10.1109/LCOMM.2020.2992781.

    Article  Google Scholar 

  • Muttil, Nitin, and Kwok-Wing Chau. 2007. Machine-learning paradigms for selecting ecologically significant input variables. Engineering Applications of Artificial Intelligence 20 (6): 735–744.

    Article  Google Scholar 

  • Novo, Oscar, et al. n.d. Capillary networks - bridging the cellular and IoT worlds, year\(=\)2015. In 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), 571–578. https://doi.org/10.1109/WF-IoT.2015.7389117.

  • Obaid, O. Ibrahim, et al. 2018. Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. International Journal of Engineering & Technology 7 (4.36): 160–166.

    Google Scholar 

  • Pitis, Silviu. 2019. Rethinking the discount factor in reinforcement learning: A decision theoretic approach. In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. 01, 7949–7956.

    Google Scholar 

  • Schneider, Stan. 2017. The industrial internet of things (IIoT) applications and taxonomy. In Internet of Things and Data Analytics Handbook, 41–81.

    Google Scholar 

  • Sharma, Parjanay, et al. 2021. Role of machine learning and deep learning in securing 5G-driven industrial IoT applications. Ad Hoc Networks 123: 102685.

    Article  Google Scholar 

  • Short, Elaine Schaertl, et al. 2018. Detecting contingency for HRI in open-world environments. In Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 425–433.

    Google Scholar 

  • Srirama, Satish Narayana. n.d. A decade of research in fog computing: Relevance, challenges, and future directions. Software: Practice and Experience n/a.n/a. https://doi.org/10.1002/spe.3243. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.3243. https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.3243.

  • Sun, Wen, et al. 2019. AI-enhanced offloading in edge computing: When machine learning meets industrial IoT. IEEE Network 33 (5): 68–74. https://doi.org/10.1109/MNET.001.1800510.

    Article  Google Scholar 

  • Tran, Duc Hoang, et al. 2022. Self-supervised learning for time-series anomaly detection in Industrial Internet of Things. Electronics 11 (14): 2146.

    Article  Google Scholar 

  • Xue, Ming, and Changjun Zhu. 2009. A study and application on machine learning of artificial intellligence. In 2009 International Joint Conference on Artificial Intelligence, 272–274. https://doi.org/10.1109/JCAI.2009.55.

  • Yang, Bo, et al. 2020. Mobile-edge-computing-based hierarchical machine learning tasks distribution for IIoT. IEEE Internet of Things Journal 7 (3): 2169–2180. https://doi.org/10.1109/JIOT.2019.2959035.

    Article  Google Scholar 

  • Yang, Yuchen, et al. 2017. A survey on security and privacy issues in Internet-of-Things. IEEE Internet of Things Journal 4 (5): 1250–1258. https://doi.org/10.1109/JIOT.2017.2694844.

    Article  Google Scholar 

  • Zhang, Peiying, et al. 2021. Deep reinforcement learning assisted federated learning algorithm for data management of IIoT. IEEE Transactions on Industrial Informatics 17 (12): 8475–8484. https://doi.org/10.1109/TII.2021.3064351.

    Article  Google Scholar 

Download references

Acknowledgements

The author would like to thank NSUT and IIIT Sri City for providing the necessary support to conduct this research work.

Author information

Authors and Affiliations

  1. Netaji Subhas University of Technology, Delhi, India

    Megha Sharma & Abhinav Tomar

  2. Indian Institute of Information Technology, Sri City, India

    Abhishek Hazra

Authors
  1. Megha Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhishek Hazra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abhinav Tomar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Distributed Systems Group, TU Wein, Vienna, Austria

    Praveen Kumar Donta

  2. Indian Institute of Information Technology, Sri City, India

    Abhishek Hazra

  3. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Lauri Lovén

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sharma, M., Hazra, A., Tomar, A. (2024). Machine Learning Techniques for Industrial Internet of Things. In: Donta, P.K., Hazra, A., Lovén, L. (eds) Learning Techniques for the Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-50514-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50514-0_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50513-3

  • Online ISBN: 978-3-031-50514-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation