Top

Published in:

2023 | OriginalPaper | Chapter

Opportunities of Using Machine Learning Methods in Telecommunications and Industry 4.0 – A Survey

Authors : Dragan Peraković, Marko Periša, Ivan Cvitić, Petra Zorić, Tibor Mijo Kuljanić, David Aleksić

Published in: 7th EAI International Conference on Management of Manufacturing Systems

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Artificial intelligence can be considered a leading component of industrial transformation, but its application is also present in other areas, such as the telecommunications sector. Methodologies based on artificial intelligence, the most significant of which is machine learning, support these areas when predicting maintenance needs and reducing downtime. Machine learning has many algorithms and methods of its own. This research presents machine learning methods and the possibilities of their use in the areas of telecommunications and Industry 4.0. With the improvement of new technologies such as higher internet speeds and 5G mobile networks in the field of telecommunications or Industry 4.0, comes the need for new and improved management and support for systems that use these new technologies. Some types of machine learning can be used for collecting data to improve user’s quality of service. Also, some types of them can be used to collect data on network traffic or, in general, for any system that needs to collect data, cluster data points, and analyze data.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Comparison of the Impacts of the Financial Crisis and the Covid-19 Pandemic Crisis on Selected Financial Indicator of Construction Companies

next chapter Guidelines for Mitigating Cybersickness During Training in VR Environment Using Head-Mounted Displays

European Commission. (2020). White paper on artificial intelligence – A European approach to excellence and trust. Brussels.

Wilson, A. A brief introduction to supervised learning. https://towardsdatascience.com/a-brief-introduction-to-supervised-learning-54a3e3932590. Accessed 4 June 2022.

Mishra, S. Unsupervised learning and data clustering. https://towardsdatascience.com/unsupervised-learning-and-data-clustering-eeecb78b422a. Accessed 4 June 2022.

Lawtomated. Supervised learning vs unsupervised learning. Which is better? https://lawtomated.com/supervised-vs-unsupervised-learning-which-is-better/. Accessed 4 June 2022.

Altexsoft. Unsupervised learning: Algorithms and examples. https://www.altexsoft.com/blog/unsupervised-machine-learning/. Accessed 4 June 2022.

Batta, M. (2020). Machine learning algorithms – A review. International Journal of Science and Research, 9, 381–386. https://doi.org/10.21275/ART20203995

Johnson, D. Reinforcement learning tutorial. https://www.guru99.com/reinforcement-learning-tutorial.html. Accessed 4 June 2022.

Brownlee, J. Machine learning tools. https://machinelearningmastery.com/machine-learning-tools/. Accessed 4 June 2022.

My server name. 11 most popular machine learning software tools 2021. https://hr.myservername.com/11-most-popular-machine-learning-software-tools-2021. Accessed 4 June 2022.

10.

Scikit-learn. Machine learning in Python. https://scikit-learn.org/stable/. Accessed 4 June 2022.

11.

TensorFlow. TensorFlow. https://www.tensorflow.org/. Accessed 4 June 2022.

12.

Kovalev, S., Sintsov, S., Khizhniak, A., & Turol, S. Implementing k-means clustering with TensorFlow. https://www.altoros.com/blog/using-k-means-clustering-in-tensorflow/. Accessed 4 June 2022.

13.

PyTorch. PyTorch. https://pytorch.org/. Accessed 4 June 2022.

14.

TutorialsPoint. What is Weka? https://www.tutorialspoint.com/weka/what_is_weka.htm. Accessed 1 July 2022.

15.

Weka 3. Weka 3: Machine learning software in Java. https://www.cs.waikato.ac.nz/ml/weka/. Accessed 1 July 2022.

16.

Brownlee, J. How to run your first classifier in Weka. https://machinelearningmastery.com/how-to-run-your-first-classifier-in-weka/. Accessed 1 July 2022.

17.

RapidMiner. https://rapidminer.com/platform/. Accessed 1 July 2022.

18.

Burke, J. What is the role of machine learning in networking? https://searchnetworking.techtarget.com/answer/What-is-the-role-of-machine-learning-in-networking. Accessed 4 July 2022.

19.

Stouffer, C. DDoS attacks: A simplified guide + DDoS attack protection tips. https://us.norton.com/internetsecurity-emerging-threats-ddos-attacks.html. Accessed 4 July 2022.

20.

Perakovic, D., Perisa, M., Cvitic, I., & Husnjak, S. (2017). Model for detection and classification of DDoS traffic based on artificial neural network. Telfor Journal, 9, 26–31. https://doi.org/10.5937/telfor1701026PCrossRef

21.

Clark, J. What is the Internet of Things (IoT)? https://www.ibm.com/blogs/internet-of-things/what-is-the-iot/. Accessed 4 July 2022.

22.

Cvitic, I., Perakovic, D., Gupta, B. B., & Choo, K.-K. R. (2022). Boosting-based DDoS detection in internet of things systems. IEEE Internet of Things Journal, 9, 2109–2123. https://doi.org/10.1109/JIOT.2021.3090909CrossRef

23.

Phishing.org. What is phishing? https://www.phishing.org/what-is-phishing. Accessed 4 July 2022.

24.

Gupta, B. B., Tewari, A., Cvitić, I., Peraković, D., & Chang, X. (2022). Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0. Wireless Networks, 28, 493–503. https://doi.org/10.1007/s11276-021-02619-wCrossRef

25.

Norton. Phishing email examples to help you identify phishing scams. https://us.norton.com/internetsecurity-online-scams-phishing-email-examples.html. Accessed 1 July 2022.

26.

Cvitić, I., Peraković, D., Periša, M., & Gupta, B. (2021). Ensemble machine learning approach for classification of IoT devices in smart home. International Journal of Machine Learning and Cybernetics, 12, 3179–3202. https://doi.org/10.1007/s13042-020-01241-0CrossRef

27.

Jamshidi, S. (2019). The applications of machine learning techniques in networking.

28.

Akhtar, M., & Moridpour, S. (2021). A review of traffic congestion prediction using. Journal of Advanced Transportation, 2021, 18. https://doi.org/10.1155/2021/8878011CrossRef

29.

Joshi, M., & Hadi, T. (2015). A review of network traffic analysis and prediction techniques. ArXiv. abs/1507.0. https://doi.org/10.48550/arXiv.1507.05722

30.

Zorić, P., Musa, M., & Kuljanić, T. M. (2021). Smart factory environment: Review of security threats and risks. In Lecture notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (pp. 203–214). https://doi.org/10.1007/978-3-030-78459-1_15

31.

Peraković, D., Periša, M., Cvitić, I., & Zorić, P. (2021). Artificial intelligence application in different scenarios of the networked society 5.0 environment. In Zbornik Radova Trideset Devetog Simpozijuma O Novim Tehnologijama U Poštanskom I Telekomunikacionom Saobraćaju – Postel 2021. University of Belgrade, Faculty of Transport and Traffic Engineering. https://doi.org/10.37528/FTTE/9788673954455/POSTEL.2021.021

32.

Gonfalonieri, A. How to implement machine learning for predictive maintenance. https://towardsdatascience.com/how-to-implement-machine-learning-for-predictive-maintenance-4633cdbe4860. Accessed 4 July 2022.

33.

Wang, M., Cui, Y., Wang, X., Xiao, S., & Jiang, J. (2017). Machine learning for networking: Workflow, advances and opportunities. IEEE Network, 32, 92–99. https://doi.org/10.1109/MNET.2017.1700200CrossRef

Title: Opportunities of Using Machine Learning Methods in Telecommunications and Industry 4.0 – A Survey
Authors: Dragan Peraković
Marko Periša
Ivan Cvitić
Petra Zorić
Tibor Mijo Kuljanić
David Aleksić
Publisher: Springer International Publishing
Book: 7th EAI International Conference on Management of Manufacturing Systems
Print ISBN: 978-3-031-22718-9

Electronic ISBN: 978-3-031-22719-6

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-22719-6_16

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partners