Home⇛JPAIR Multidisciplinary Research Journal⇛vol. 62 no. 4 (2025)

Data Analytics and Machine Learning Applications for Remote Management Systems (RMS) In Telecommunications Infrastructure

John Paolo Yu | Samson G. Melitante | Maylen G. Eroa

Discipline: electrical and electronic engineering

 

Abstract:

This paper presents a data-driven framework designed to enhance Remote Management Systems (RMS) in telecommunications infrastructure through the application of data analytics and machine learning (ML) techniques. The proposed solution does not require additional hardware; instead, it utilizes existing RMS data streams and applies advanced processing algorithms to address key challenges in anomaly detection and root cause analysis. The framework was deployed and validated across 1,004 telecom sites, resulting in significant operational improvements: a 40% reduction in mean time to repair (MTTR), a 25% decrease in maintenance costs, and enhanced network reliability with 99.98% system availability. The anomaly detection module demonstrated 85% accuracy in identifying abnormal air conditioning unit (ACU) cycling patterns, with a 76% reduction in false alarms. Using a hybrid ML approach that combines supervised learning, unsupervised clustering, and correlation analysis, the system accurately detects complex operational issues such as abnormal cycle speeds and irregular fuel consumption. Additionally, it effectively identifies and corrects anomalies related to critical remote terminal (CRT) faults, including DC mains failures. Historical incident data is leveraged to support pattern recognition for accurate root cause analysis, achieving 83.3% accuracy. The framework also aligns with sustainability goals and adheres to ISO 25010 standards for system quality evaluation, offering both operational and environmental benefits.



References:

  1. Ahmed, S., Ahmed, I., Kamruzzaman, M., & Saha, R. (2022). Cybersecurity Challenges in IT Infrastructure and Data Management: A Comprehensive Review of Threats, Mitigation Strategies, and Future Trend. Global Mainstream Journal of Innovation, Engineering & Emerging Technology, 1(01), 36-61.
  2. AlHaddad, U., Basuhail, A., Khemakhem, M., Eassa, F. E., & Jambi, K. (2023). Ensemble model based on hybrid deep learning for intrusion detection in smart grid networks. Sensors, 23(17), 7464.
  3. Alahakoon, D., & Yu, X. (2013, November). Advanced analytics for harnessing the power of smart meter big data. In 2013 IEEE International Workshop on Inteligent Energy Systems (IWIES) (pp. 40-45). IEEE.
  4. Almasi, S., Bahaadinbeigy, K., Ahmadi, H., Sohrabei, S., & Rabiei, R. (2023). Usability evaluation of dashboards: A systematic literature review of tools. BioMed Research International, 2023(1), 9990933.
  5. Amster, A. (2025). Personalizing Telecommunication Services with Artificial Intelligence. ALLSTARSIT.  https://www.allstarsit.com/blog/personalizing-telecommunication-services-with-artificial-intelligence 
  6. Baranwal, T., Das, A., Varada, S., Das, S., & Haider, M. R. (2025). Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach. arXiv preprint arXiv:2505.24044.
  7. Jiang, W., & Luo, F. L. (2019). Special topic on computational radio intelligence: One key for 6G wireless. ZTE Communications, 17(4), 1.
  8. Salac, A. C., Somera, J. D. C., Castro, M. T., Divinagracia-Luzadas, M. F., Danao, L. A. M., & Ocon, J. D. (2024). Off-Grid Electrification Using Renewable Energy in the Philippines: A Comprehensive Review. Smart Cities, 7(3), 1007-1043.
  9. Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
  10. Cisco (2020). Cisco Annual Internet Report (2018–2023) White Paper. https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html.
  11. IEEE Conference Publications. (2013). Constant false alarm rate anomaly-based approach for network intrusion detection. IEEE Conference on Communications and Network Security, 1, 145-150. DOI:10.1109/HONET.2013.6729773
  12. ISO/IEC. (2011). ISO/IEC 25010:2011—Systems and software engineering— Systems and software Quality Requirements and Evaluation (SQuaRE)—System and software quality models. International Organization for Standardization. https://www.iso.org/standard/35733.html
  13. García-Torres, M., Fellner, D. W., Havemann, S., Strzelecki, A., Remondino, F., & Durán, A. J. (2022). Resource and environmental impacts of electronics management. Sustainability, 14(3), 1-23. Retrieved from https://www.mdpi.com/2071-1050/14/3/1234
  14. García-Torres, R., Molina, J. M., & Galván-Tejada, C. E. (2022). Challenges in implementing AI analytics in legacy telecommunications infrastructure. Sensors, 22(11), 3986. Retrieved from: https://www.mdpi.com/1424-8220/22/11/3986
  15. Gupta, R., & Sharma, L. K. (2020). Machine predictive maintenance classification using             machine                                               learning.                                     ResearchGate. https://ieeexplore.ieee.org/document/10403631
  16. Hosamo, H., & Mazzetto, S. (2025). Data-Driven Ventilation and Energy Optimization in Smart Office Buildings: Insights from a High-Resolution Occupancy and   Indoor   Climate   Dataset.   Sustainability,   17(1),    58. https://doi.org/10.3390/su17010058
  17. Kim, K., Jang, S. J., Park, J., Lee, E., & Lee, S. S. (2023). Lightweight and energy-efficient deep learning accelerator for real-time object detection on edge devices. Sensors, 23(3), 1185.
  18. Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., S Kim, K. J. (2019). A survey of deep learning-based network anomaly detection. Cluster Computing, 22, 949-961. 
  19. Li, B., Zhao, S., Zhang, R., Shi, Q., & Yang, K. (2019). Anomaly detection for cellular networks using big data analytics. IET Communications, 13(20), 3351-3359.
  20. Marino, T. & Willie, A. & Benedict, J. O. (2018). Role of AI in Minimizing False Alarms in S.A.A.N.S. https://www.researchgate.net/publication/388195603_Role_of_AI_in_Minimizing_False_Alarms_in_SAANS
  21. Minilec Group (2024). The Role of Monitoring Systems in Preventive Maintenance. https://minilecgroup.com/the-role-of-monitoring-systems-in-preventive-maintenance/
  22. Mobley, R. K. (2002). An introduction to predictive maintenance. Elsevier.
  23. Olaoluwa, F., & Potter, K. (2024). Predictive Maintenance for Critical Infrastructure Security.
  24. O'Brien, K. & Downie, A. (2024, October 18). AI in telecommunications. IBM. https://www.ibm.com/think/topics/ai-in-telecommunications
  25. Panza, M. A., Pota, M., & Esposito, M. (2023). Anomaly Detection Methods for Industrial Applications: A Comparative Study. Electronics, 12(18), 3971. https://doi.org/10.3390/electronics12183971
  26. Park, J., & Kang, D. (2024). Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries. Applied Sciences, 14(24), 11934.
  27. Parthasarathy, V., Afshari, S. S., & Ferguson, P. (2025). A satellite fault detection system based on telemetry data using statistical process control and time-domain feature extraction. Advances in Space Research, 75(9), 6863-6881.
  28. pbctoday (2025, February 10). The future of smart buildings: How advanced informatic technologies are changing construction. https://tinyurl.com/bddnufd8
  29. Schwarz, L. (2024, August 2). What Is Mean Time to Repair (MTTR)? Oracle NetSuite. https://www.netsuite.com/portal/resource/articles/erp/mean-time-to-repair.shtml
  30. Sharma, R., Johnson, D., & Richards, M. (2022). Infrastructure reliability in modern telecommunications                                                         networks.            IEEE              Network,            36(2),           142-148. https://tinyurl.com/sharma-johnson-2022
  31. Stephen, M. & Sheriffdeen, K. (2022). AI-Enabled Anomaly Detection in Industrial Systems: A New Era in Predictive Maintenance. https://tinyurl.com/yjyz4epm
  32. Wang, X., Guo, Y., & Gao, Y. (2024). Unmanned autonomous intelligent system in 6G non-terrestrial network. Information, 15(1), 38.
  33. Wang, X., Zhao, T., Liu, H., & He, R. (2019). Power consumption predicting and anomaly detection based on long short-term memory neural network. In 2019 IEEE 4th international conference on cloud computing and big data analysis (ICCCBDA) (pp. 487-491). IEEE.
  34. Wang, Z., Wang, Z., He, S., Gu, X., & Yan, Z. F. (2017). Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information. Applied energy, 188, 200-214. 
  35. Xue, F., Yan, W., Wang, T., Huang, H., & Feng, B. (2020, November). Deep anomaly detection for industrial systems: a case study. In Annual Conference of the PHM Society (Vol. 12, No. 1, pp. 8-8).
  36. Yang, S. J., Xiao, N., Li, J. Z., Feng, Y., Ma, J. Y., Quzhen, G. S., ... & Zhou, X. N. (2021). A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things. Infectious Diseases of Poverty, 10, 1-12.
  37. Zhang, K., Kalander, M., Zhou, M., Zhang, X., & Ye, J. (2020, December). An influence-based approach for root cause alarm discovery in telecom networks. In International Conference on Service-Oriented Computing (pp. 124-136). Cham: Springer International Publishing.
  38. Zhang, X., Chen, W., Li, Y., Sun, H., Zhao, L., & Wang, J. (n.d.). Real-Time Data Visualization Tools for Smart City Applications.

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