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. The possibilities of current Artificial Intelligence of Things devices and distributed execution of ML algorithms using Edge/Fog/Cloud computing environments are being considered. 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.
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Peraković D. Periša M, Cvitić I, Zorić P, Kuljanić TM, Aleksić D, Knapčíková L, Peraković D (2023) 7th EAI international conference on management of manufacturing systems opportunities of using machine learning methods in telecommunications and industry 4.0—a survey. Springer International Publishing Cham, pp 211–225
European Commission: White paper on Artificial Intelligence - A European approach to excellence and trust., Brussels (2020)
Wilson A A Brief Introduction to Supervised Learning, https://towardsdatascience.com/a-brief-introduction-to-supervised-learning-54a3e3932590, last accessed 2022/06/04
Mishra S Unsupervised Learning and Data Clustering, https://towardsdatascience.com/unsupervised-learning-and-data-clustering-eeecb78b422a, last accessed 2022/06/04.
Lawtomated Supervised Learning vs Unsupervised Learning. Which is better?, https://lawtomated.com/supervised-vs-unsupervised-learning-which-is-better/, last accessed 2022/06/04
altexsoft : Unsupervised Learning: Algorithms and Examples, https://www.altexsoft.com/blog/unsupervised-machine-learning/, last accessed 2022/06/04
Batta M (2020) Machine learning algorithms - a review. Int J Sci Res (IJ 9:381–386. https://doi.org/10.21275/ART20203995
Johnson D Reinforcement Learning Tutorial, https://www.guru99.com/reinforcement-learning-tutorial.html, last accessed 2022/06/04
Brownlee J Machine Learning Tools, https://machinelearningmastery.com/machine-learning-tools/, last accessed 2022/06/04
My server name (2021) : 11 Most Popular Machine Learning Software Tools https://hr.myservername.com/11-most-popular-machine-learning-software-tools-2021, last accessed 2022/06/04.
Scikit-learn : Machine Learning in Python, https://scikit-learn.org/stable/, last accessed 2022/06/04
TensorFlow : TensorFlow, https://www.tensorflow.org/, last accessed 2022/06/04.
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/, last accessed 2022/06/04
PyTorch : PyTorch, https://pytorch.org/, last accessed 2022/06/04
TutorialsPoint : What is Weka?, https://www.tutorialspoint.com/weka/what_is_weka.htm, last accessed 2022/07/01
Weka 3 : Weka 3: Machine Learning Software in Java, https://www.cs.waikato.ac.nz/ml/weka/, last accessed 2022/07/01
Brownlee J How to Run Your First Classifier in Weka, https://machinelearningmastery.com/how-to-run-your-first-classifier-in-weka/, last accessed 2022/07/01
RapidMiner https://rapidminer.com/platform/, last accessed 2022/07/01
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, last accessed 2022/07/04
Stouffer C DDoS attacks: A simplified guide + DDoS attack protection tips, https://us.norton.com/internetsecurity-emerging-threats-ddos-attacks.html, last accessed 2022/07/04
Perakovic D, Perisa M, Cvitic I, Husnjak S (2017) Model for detection and classification of DDoS traffic based on artificial neural network. Telfor J 9:26–31. https://doi.org/10.5937/telfor1701026P
Clark J What is the Internet of Things (IoT)?, https://www.ibm.com/blogs/internet-of-things/what-is-the-iot/, last accessed 2022/07/04
Cvitic I, Perakovic D, Gupta BB, Choo K-KR (2022) Boosting-based DDoS detection in internet of Things Systems. IEEE Internet of Things Journal 9:2109–2123. https://doi.org/10.1109/JIOT.2021.3090909
Phishing.org.: What Is Phishing?, https://www.phishing.org/what-is-phishing, last accessed 2022/07/04
Gupta BB, 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 Netw 28:493–503. https://doi.org/10.1007/s11276-021-02619-w
Norton Phishing email examples to help you identify phishing scams, https://us.norton.com/internetsecurity-online-scams-phishing-email-examples.html, last accessed 2022/07/01
Cvitić I, Peraković D, Periša M, Gupta B (2021) Ensemble machine learning approach for classification of IoT devices in smart home. Int J Mach Learn Cybernet 12:3179–3202. https://doi.org/10.1007/s13042-020-01241-0
Jamshidi S (2019) : The Applications of Machine Learning Techniques in Networking.
Akhtar M, Moridpour S (2021) : A Review of Traffic Congestion Prediction Using. Journal of Advanced Transportation. 18 (2021). https://doi.org/10.1155/2021/8878011
Joshi M, Hadi T (2015) A review of Network Traffic Analysis and Prediction techniques. ArXiv abs. https://doi.org/10.48550/arXiv.1507.05722. 1507.0
Zorić P, Musa M, Kuljanić TM (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
Peraković D, Periša M, Cvitić I, Zorić P, ZBORNIK RADOVA TRIDESET DEVETOG SIMPOZIJUMA O NOVIM TEHNOLOGIJAMA U POŠTANSKOM I TELEKOMUNIKACIONOM SAOBRAĆAJU – POSTEL 2021 (2021) : Artificial Intelligence Application in Different Scenarios of the Networked Society 5.0 Environment. In:. University of Belgrade, Faculty of Transport and Traffic Engineering https://doi.org/10.37528/FTTE/9788673954455/POSTEL.2021.021
Gonfalonieri A How to Implement Machine Learning For Predictive Maintenance, https://towardsdatascience.com/how-to-implement-machine-learning-for-predictive-maintenance-4633cdbe4860, last accessed 2022/07/04.
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.2017.1700200
Jevremovic, A., Veinovic, M., Cabarkapa, M., Krstic, M., Chorbev, I., Dimitrovski,I., … Stojmenovic, M. (2021). Keeping Children Safe online with Limited Resources:Analyzing what is seen and heard. IEEE Access, 9, 132723–132732
Bragança S, Costa E, Castellucci I, Arezes PM (2019) A brief overview of the use of collaborative robots in industry 4.0: human role and safety. Occupational and environmental safety and health, pp 641–650
Kapp S, Choi JK, Hong T (2023) Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters. Renew Sustain Energy Rev 172:113045
Feizabadi J (2022) Machine learning demand forecasting and supply chain performance. Int J Logistics Res Appl 25(2):119–142
Brito T, Queiroz J, Piardi L, Fernandes LA, Lima J, Leitão P (2020) A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems. Procedia Manuf 51:11–18
Carvalho TP, Soares FA, Vita R, Francisco RDP, Basto JP, Alcalá SG (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 137:106024
Haqiq N, Zaim M, Bouganssa I, Salbi A, Sbihi M (2022) AIoT with I4. 0: the effect of Internet of Things and Artificial Intelligence technologies on the industry 4.0. In ITM Web of Conferences (Vol. 46, p. 03002). EDP Sciences
Firouzi F et al (2023) Fusion of IoT, AI, edge–Fog–Cloud, and Blockchain: Challenges, Solutions, and a case study in Healthcare and Medicine. IEEE Internet of Things Journal 10(5):3686–3705. https://doi.org/10.1109/JIOT.2022.3191881
Dogo EM, Salami AF, Aigbavboa CO, Nkonyana T (2019) Taking cloud computing to the extreme edge: A review of mist computing for smart cities and industry 4.0 in Africa. Edge computing: from hype to reality, 107–132
https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/
Aleksandar Jevremovic Z, Kostic D, Perakovic (2023) Energy-efficient Edge Intelligence: a comparative analysis of AIoT Technologies. Mob Networks Appl 1383–469X. https://doi.org/10.1007/s11036-023-02122-w
Liang Y, Cui T, Cao Y, Tang H (2022) An effective resource scheduling model for edge cloud oriented AIoT. Concurrency and Computation: Practice and Experience, 34(5), e6720
Gawali SK, Deshmukh MK (2019) Energy autonomy in IoT technologies. Energy Procedia 156:222–226
Hou, X., Liu, J., Tang, X., Li, C., Chen, J., Liang, L., … Guo, M. (2023, June). Architecting Efficient Multi-modal AIoT Systems. In Proceedings of the 50th Annual International Symposium on Computer Architecture (pp. 1–13)
Almanifi ORA, Chow CO, Tham ML, Chuah JH, Kanesan J (2023) Communication and computation efficiency in Federated Learning: a survey. Internet of Things, 100742
Ahmad R, Alsmadi I (2021) Machine learning approaches to IoT security: a systematic literature review. Internet of Things 14:100365
O’Donovan P, Gallagher C, Leahy K, O’Sullivan DT (2019) A comparison of fog and cloud computing cyber-physical interfaces for industry 4.0 real-time embedded machine learning engineering applications. Comput Ind 110:12–35
Banabilah S, Aloqaily M, Alsayed E, Malik N, Jararweh Y (2022) Federated learning review: Fundamentals, enabling technologies, and future applications. Inf Process Manag 59(6):103061
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Conceptualization: IC; Methodology: IC, AJ; Formal analysis: IC, AJ, PL; Investigation: IC, AJ; Supervision: IC; Visualization: IC, AJ, PL; Writing – original draft: IC; Writing – review & editing: AJ, PL;
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Cvitić, I., Jevremovic, A. & Lameski, P. Approaches and Opportunities of Using Machine Learning Methods in Telecommunications and Industry 4.0. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02241-4
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DOI: https://doi.org/10.1007/s11036-023-02241-4