API-Integrated Bayesian Risk Management Model for Cellular Network Base Stations

The article presents a new approach to risk management of emergency situations at cellular network base stations. The diversity of emergencies arising on heterogeneous equipment under the influence of various external factors makes risk management a critically important task. The novelty lies in the creation of a mathematical model that accounts for this diversity, providing more accurate and comprehensive prediction of emergency situations. The model is based on a Bayesian network and generates real-time solutions in the form of probabilities of emergency occurrences, identifying critical points and potential threats to the base station's overall functionality. This helps generate recommendations for reducing risks, identifying priority areas for implementing improvements, and modernizing equipment. To ensure effective interaction with the model, an API is being developed and studied using FastAPI and Python. The API interacts with the Bayesian model created in BayesFusion GeNIe. The model implements a new method for integrating the developed Bayesian network with existing applications based on REST API principles, thus introducing a new approach to risk management. The article describes the processes of API creation, performance testing, and deployment. As a result of using the API, real-time risk management becomes possible, helping operators prevent emergency situations.

The integrated model was developed for research purposes to monitor the risk landscape of cellular network base stations. The application of this model significantly increases the level of automation in the risk management process during the operation of cellular network base stations.

1. Buntsev, I. A., Kanev, V. S. Sistemnoe upravlenie riskami v telekomunikatsiyakh [Systematic risk management in telecommunications (state of the problem, methods, models, implementations). Vestnik SibGUTI. 2009, pp. 26-52.

2. Diedrich, A., Deutschmann, P. and Junker, C. ServiceNavigator - a bayesian assistance system for diagnosing industrial production systems. Proc. of 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 1-6).

3. Bogner, J., Fritzsch, J., Wagner, S., Zimmermann, A. Microservices in Industry: Insights into Technologies, Characteristics, and Software Quality. 2019. doi: 10.1109/ICSA-C.2019.00041.

4. Beaulieu, N., Dascalu, S. & Hand, E. API-First Design: A Survey of the State of Academia and Industry. 2022. doi: 10.1007/978-3-030-97652-1_10.

5. Rezedinova, E. Yu., Kyrkunov, P. N., Sergeev, A. V. Vybor servis-orientirovannoy arkhitektury dlya sozdaniya servisa po blago ustroystvu goroda [Choice of service-oriented architecture for creating a city improvement service]. SAEC, 2023, no. 3.

6. Galiguzova, E. V., Illarionova, Yu. E. Yazyk zaprosov GraphQL kak zamena REST API: sravnenie GraphQL i REST API [GraphQL query language as a replacement for REST API: comparison of GraphQL and REST API]. Simvol nauki, 2023, no. 1-2.

7. Zhang, H., Wang, Z. R., Wang, X. W., Lin, F. C.. Practice and Research on FMEA of Telecommunication Satellite System. In: Sun, J., Wang, Y., Huo, M., Xu, L. (eds) Signal and Information Processing, Networking and Computers. Lecture Notes in Electrical Engineering, 2023, vol. 917. Springer, Singapore. https://doi.org/10.1007/978-981-19-3387-5_110.

8. Alijanzadeh, M. R., Shayannia, S. A. & Movahedi, M. M.. Optimization of maintenance in supply chain process and risk-based critical failure situations (case study: Iranian oil pipeline and telecommunication company, north district). Journal of applied research on industrial engineering, 2024, vol. 11(1), pp. 125-142.

9. Carretero-Ayuso, M.J.; Sánchez-Barroso, G.; González-Domínguez, J.; García-Sanz-Calcedo, J. Failure Modes in Electricity and Telecommunication Facilities in Dwellings in Spain. Applied Sciences, 2021, vol. 11:5274. https://doi.org/10.3390/app11115274

10. Tarcsay B. L., Bárkányi Á, Németh S, Chován T, Lovas L, Egedy A. Risk-Based Fault Detection Using Bayesian Networks Based on Failure Mode and Effect Analysis. Sensors, 2024; vol. 24:3511.

11. Nalluri, V., Chen, L. Risk assessment for sustainability on telecom supply chain: A hybrid fuzzy approach. Uncertain Supply Chain Management, 2022, no. 10(2), pp. 559-576.

12. Tsyganov, V. V., Gurlev, I. V. Kognitivnoe prognozirovanie informatsionno-telekommunikatsionnoy infrastruktury krupnomasshtabnogo regiona [Cognitive forecasting of information and telecommunication infrastructure in a large-scale region]. ITNOU: Informatsionnye tekhnologii v nauke, obrazovanii i upravlenii, 2020, no. 1 (15). pp. 3-7.

13. Mishra, A., Kumar, D., Shuaib, M., Tyagi, M., Singh, R. Measurement of Critical Factors: A Case of Telecommunication Industry. 2021. doi: 10.1007/978-981-15-6017-0.

14. Bayas, B., Zambrano, C. Redes bayesianas aplicadas a la predicción de errores en las redes definidas por software [Bayesian networks applied to error prediction in software-defined networks]. Revista Universidad y Sociedad, 2021, Vol. 13, pp. 419-429.

15. Dumbrais, K. O., Glushchenko, O. M. Modelirovanie i analiz riskov IT-proyektov [Modeling and analysis of risks in IT projects]. Nauka i obrazovanie segodnya, 2021, No. 2 (61), pp. 26–33.

16. Demchuk, D. A., Demchuk, K. A., Shevtsova, Y. V., Poletaykin, A. N. Bayesovskiy podkhod pri chislennom raschete riska meditsinskoy evakuatsii avtotransportom [Bayesian approach to numerical risk assessment in medical evacuation by road transport]. Vestnik SibGUTI, 2023, vol. 17(1), pp. 18-32. https://doi.org/10.55648/1998-6920-2023-17-1-18-32.

17. Adib, D., Safarzadeh, H., Mohammadi, M. Designing and explaining the IoT commercialization model in Iranian organizations (Telecommunication Company of Iran): An interpretive structural modeling (ISM) approach, International Journal f Nonlinear Analysis and Applications, 2023, vol. 14(1), pp. 723-738. doi: 10.22075/ijnaa. 2022.26942.3456.

18. Chen, W.-K., Nalluri, V., Ma, S., Lin, M.-M., Lin, C.-T. An Exploration of the Critical Risk Factors in Sustainable Telecom Services: An Analysis of Indian Telecom Industries. Sustainability, 2021, no. 13, pp. 445. https://doi.org/10.3390/su13020445.

19. GeNIe Modeler. User's Manual. BayesFusion, LLC, 2024. Available at: https://support.bayesfusion.com/docs/GeNIe.pdf (Accessed: 12 August 2024).

20. Gaag, L. C., Kjaerulff, U. Making Sensitivity Analysis Computationally Efficient, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, 2000, pp. 317–325.