COMPARATIVE ANALYSIS OF IOT MONITORING TECHNOLOGIES IN THE MINING INDUSTRY

This paper presents the results of a comparative analysis of the architecture of IoT monitoring systems in the mining industry. It examines the implementation of Internet of Things technologies to improve the efficiency of monitoring and control in an industrial environment. The focus is on ensuring safe working conditions, increasing productivity and environmental sustainability, as well as compliance with regulatory requirements. Furthermore, this article examines the current state of information technology in the mining industry, with a focus on the risks and consequences caused by outdated equipment, unreliable software, and the degree of digitalisation. This study discusses the integration of IoT technologies into existing information systems in the mining industry, enhancing their levels of security, efficiency and sustainability. The scientific novelty of the research lies in the creation of a phenomenological model of an IoT system architecture suitable for a wide range of operations in the mining industry. The study provides a detailed analysis of process optimisation and the operation of distributed sensor systems that collect, process and analyse critical data in real time. Based on the information obtained, well-founded decisions are made regarding the implementation of intelligent monitoring and control systems designed to ensure sustainable operations.

1. On the impact of Industrial Internet of Things (IIoT): mining sector perspectives / T. Zvarivadza et al // International Journal of Mining, Reclamation and Environment. – 2024. – Vol. 38, № 10. – P. 771-809. https://doi.org/10.1080/17480930.2024.2347131.

2. Enhancing safety in surface mine blasting operations with IoT-based ground vibration monitoring and prediction system integrated with machine learning / A. Singh et al // Scientific Reports. – 2025. https://doi.org/10.1038/s41598-025-86827-w.

3. New approach for monitoring the underground coal mines atmosphere using IoT technology / A.K. Tripathi et al // Instrumentation Mesure Métrologie. – 2023. – Vol. 22, № 1. – P. 29-34. https://doi.org/10.18280/i2m.220104.

4. Yu R. Deep learning and IoT enabled digital twin framework for monitoring open-pit coal mines / R. Yu, X. Yang, K. Cheng // Frontiers in Energy Research. – 2023. – Vol. 11. – Art. № 1265111. – https://doi.org/10.3389/fenrg.2023.1265111

5. Monitoring, warning, and control of rockburst in deep metal mines / H. Zhou et al // Engineering. – 2017. – Vol. 3, № 4. – P. 538-545. https://doi.org/10.1016/J.ENG.2017.04.013.

6. Rockburst in underground excavations: A review of mechanism, classification, and prediction methods / X. Feng еt al // Underground Space. – 2021. https://doi.org/10.1016/j.undsp.2021.11.008.

7. Rockburst prediction and prevention in underground space excavation / Z. Li, et al // Underground Space. – 2023. https://doi.org/10.1016/j.undsp.2023.05.009.

8. Rockburst risk control and mitigation in deep mining / Q. Zhang et al // Deep Underground Science and Engineering. – 2024. https://doi.org/10.1016/j.deepre.2024.100019.

9. Advanced risk assessment in coal mines: Integrating fuzzy logic and linguistic variables for enhanced hazard management / A. Khosravi et al // International Journal of Engineering. – 2025. – Vol. 38, № 12. https://doi.org/10.5829/ije.2025.38.12c.04.

10. Rock mass condition control by the method of predicting of water-conducting fracture zone parameters for the deposits of polymetallic ores / M. Rahimi et al // International Journal of Engineering. – 2026. – Vol. 39, № 6. https://doi.org/10.5829/ije.2026.39.06c.02.