Digital Twins for Predictive Network Management and System Simulation.
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I3P112Keywords:
Digital Twin, Predictive Network Management, Network Simulation, 5G Networks, IoT, Machine Learning, Anomaly Detection, Resource OptimizationAbstract
Digital Twin (DT) technology has emerged as a transformative approach for enhancing the intelligence, reliability, and automation of modern communication networks. A digital twin creates a virtual replica of a physical network system, enabling real-time monitoring, predictive analytics, and dynamic optimization of network behavior. With increasing network complexity in 5G, IoT, cloud, and edge ecosystems, the ability to forecast failures, predict traffic patterns, and evaluate system behavior before deployment has become critical (Tao et al., 2019). Recent advancements in machine learning and simulation frameworks have further enabled high-fidelity twins capable of reproducing network states, anticipating anomalies, and supporting closed-loop decision-making (Fuller et al., 2020). In predictive network management, digital twins facilitate proactive fault detection, congestion prediction, and automated resource orchestration, thereby reducing operational risks and improving quality of service (Qi & Tao, 2018). Additionally, the integration of data-driven models with traditional simulation approaches enables scalable and accurate system simulations for evaluating “what-if” scenarios without disrupting live networks (Barricelli et al., 2020). Despite these advantages, challenges remain in real-time data synchronization, model drift, scalability, and standardization across diverse infrastructures (Kritzinger et al., 2018). This research paper explores the architecture, modeling techniques, and practical applications of digital twins in predictive network management and system simulation, highlighting key opportunities for autonomous and resilient next-generation networks
References
1. Barricelli, B. R., Casiraghi, E., & Fogli, D. (2020). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access, 8, 190019–190036. https://doi.org/10.1109/ACCESS.2020.3034699
2. El Saddik, A. (2018). Digital twins: The convergence of multimedia technologies. IEEE MultiMedia, 25(2), 87–92. https://doi.org/10.1109/MMUL.2018.023121167
3. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
4. Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
5. Khan, A., Bilal, K., Madni, T. M., & Hayat, K. (2017). Network simulation tools: A comparative analysis. International Journal of Computer Networks & Communications, 9(1), 1–24. https://doi.org/10.5121/ijcnc.2017.9101
6. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
7. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360-degree comparison. IEEE Access, 6, 3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265
8. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., & Nee, A. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(12), 3935–3953. https://doi.org/10.1080/00207543.2018.1443229
9. Akimoto, Y., Shirakawa, S., Ouchi, M., & Sakuma, J. (2021). Adaptive simulation and modeling for digital twins using machine learning. IEEE Transactions on Industrial Informatics, 17(8), 5482–5491. https://doi.org/10.1109/TII.2020.3039817
10. Ahmed, M., Iqbal, M., Kim, D., & Kim, S. (2021). Digital twin-driven cyber-physical system for predictive maintenance in Industry 4.0. IEEE Access, 9, 146742–146757. https://doi.org/10.1109/ACCESS.2021.3123137
11. Alcaraz, C., & Lopez, J. (2020). Digital twins for cybersecurity: Towards preventing cyber-physical attacks in critical infrastructures. Computer Standards & Interfaces, 71, 103437. https://doi.org/10.1016/j.csi.2020.103437
12. Ansari, R. I., Chrysikos, T., Alhomoud, A., & Imran, M. (2020). Digital twin for 5G and beyond: Smart network modeling, simulation, and optimization. IEEE Communications Standards Magazine, 4(4), 38–45. https://doi.org/10.1109/MCOMSTD.011.2000012
13. Bozic, M., Antonic, A., & Marjanovic, M. (2021). Real-time data processing for network digital twins in smart infrastructure. Sensors, 21(9), 3124. https://doi.org/10.3390/s21093124
14. Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 1–14. https://doi.org/10.2514/6.2012-1818
15. Castellani, A., Schmitt, S., & Squartini, S. (2021). Real‑World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning. IEEE Transactions on Industrial Informatics, 17(7), 4733–4742. https://doi.org/10.1109/TII.2020.3019788
16. Jain, R., & Paul, S. (2020). Network virtualization and software-defined networking for digital twin enablement. IEEE Communications Magazine, 58(5), 20–26. https://doi.org/10.1109/MCOM.001.1900530
17. Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Kim, D. I., & Han, Z. (2020). Applications of deep reinforcement learning in communications and networking: A survey. IEEE Communications Surveys & Tutorials, 21(4), 3133–3174. https://doi.org/10.1109/COMST.2019.2916583
18. Meng, Z., Li, H., & Zhang, Y. (2021). Distributed digital twin architecture for large-scale industrial IoT networks. IEEE Internet of Things Journal, 8(20), 15218–15228. https://doi.org/10.1109/JIOT.2021.3064964
19. Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939–948. https://doi.org/10.1016/j.promfg.2017.07.198
20. Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Concepts, challenges and opportunities. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
21. Schluse, M., Rossmann, J., & Schlette, C. (2018). Simulation-based digital twins for industrial applications. Simulation, 94(12), 1041–1053. https://doi.org/10.1177/0037549718792600
22. Sun, Y., Peng, M., Zhou, Y., Huang, Y., & Mao, S. (2019). Application of machine learning in future wireless networks. IEEE Wireless Communications, 26(5), 86–92. https://doi.org/10.1109/MWC.2019.1800373
23. Tao, F., & Zhang, M. (2021). Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Transactions on Industrial Informatics, 17(6), 4108–4119. https://doi.org/10.1109/TII.2020.2962877
24. Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019). A digital‑twin‑assisted fault diagnosis using deep transfer learning. IEEE Access, 7, 19990–19999. https://doi.org/10.1109/ACCESS.2018.2890566
25. Chalasani, R., Tyagadurgam, M. S. V., Gangineni, V. N., Pabbineedi, S., Penmetsa, M., & Bhumireddy, J. R. (2021). Enhancing IoT (Internet of Things) Security Through Intelligent Intrusion Detection Using ML Models. Available at SSRN 5609630.
26. Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Big Text Data Analysis for Sentiment Classification in Product Reviews Using Advanced Large Language Models. International Journal of AI, BigData, Computational and Management Studies, 2(2), 55-65.
27. Vangala, S. R., Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., & Chundru, S. K. (2021). Smart Healthcare: Machine Learning-Based Classification of Epileptic Seizure Disease Using EEG Signal Analysis. International Journal of Emerging Research in Engineering and Technology, 2(3), 61-70.
28. Polam, R. M., Kamarthapu, B., Kakani, A. B., Nandiraju, S. K. K., Chundru, S. K., & Vangala, S. R. (2021). Data Security in Cloud Computing: Encryption, Zero Trust, and Homomorphic Encryption. International Journal of Emerging Trends in Computer Science and Information Technology, 2(3), 70-80.
29. Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
30. Gupta, A. K., Buddula, D. V. K. R., Patchipulusu, H. H. S., Polu, A. R., Narra, B., & Vattikonda, N. (2021). An Analysis of Crime Prediction and Classification Using Data Mining Techniques.
31. Gupta, K., Varun, G. A. D., Polu, S. D. E., & Sachs, G. Enhancing Marketing Analytics in Online Retailing through Machine Learning Classification Techniques.
32. HK, K. (2020). Design of Efficient FSM Based 3D Network on Chip Architecture. INTERNATIONAL JOURNAL OF ENGINEERING, 68(10), 67-73.
33. Krutthika, H. K. (2019, October). Modeling of Data Delivery Modes of Next Generation SOC-NOC Router. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.
34. Ajay, S., Satya Sai Krishna Mohan G, Rao, S. S., Shaunak, S. B., Krutthika, H. K., Ananda, Y. R., & Jose, J. (2018). Source Hotspot Management in a Mesh Network on Chip. In VDAT (pp. 619-630).
35. Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv preprint arXiv:1001.3781.
36. Gopalakrishnan Nair, T. R., & Krutthika, H. K. (2010). An Architectural Approach for Decoding and Distributing Functions in FPUs in a Functional Processor System. arXiv e-prints, arXiv-1001.
37. Krutthika H. K. & A.R. Aswatha. (2021). Implementation and analysis of congestion prevention and fault tolerance in network on chip. Journal of Tianjin University Science and Technology, 54(11), 213–231. https://doi.org/10.5281/zenodo.5746712
38. Singh, A. A., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Hybrid AI Models Combining Machine-Deep Learning for Botnet Identification. International Journal of Humanities and Information Technology, (Special 1), 30-45.
39. Kothamaram, R. R., Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., & Maniar, V. (2021). A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations. International Journal of Emerging Research in Engineering and Technology, 2(2), 64-72.
40. Rajendran, D., Namburi, V. D., Singh, A. A. S., Tamilmani, V., Maniar, V., & Kothamaram, R. R. (2021). Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environmen. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 83-91.
41. Maniar, V., Tamilmani, V., Kothamaram, R. R., Rajendran, D., Namburi, V. D., & Singh, A. A. S. (2021). Review of Streaming ETL Pipelines for Data Warehousing: Tools, Techniques, and Best Practices. International Journal of AI, BigData, Computational and Management Studies, 2(3), 74-81.
42. Singh, A. A. S., Tamilmani, V., Maniar, V., Kothamaram, R. R., Rajendran, D., & Namburi, V. D. (2021). Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 60-69.
43. Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
44. Yaqoob, I., Salah, K., Jayaraman, R., Mohamed, A., & Al‑Hosani, H. (2020). Blockchain for cybersecurity and privacy: Architectures, challenges, and research opportunities. In Proceedings of the International Conference on Cyber Security and Protection of Digital Services (Cyber Security) (pp. 1–8). IEEE.
45. Attipalli, A., Enokkaren, S. J., Bitkuri, V., Kendyala, R., Kurma, J., & Mamidala, J. V. (2021). A Review of AI and Machine Learning Solutions for Fault Detection and Self-Healing in Cloud Services. International Journal of AI, BigData, Computational and Management Studies, 2(3), 53-63.
46. Enokkaren, S. J., Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., & Attipalli, A. (2021). Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection. International Journal of Emerging Research in Engineering and Technology, 2(2), 43-54.
47. Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., & Enokkaren, S. J. (2021). A Survey on Hybrid and Multi-Cloud Environments: Integration Strategies, Challenges, and Future Directions. International Journal of Computer Technology and Electronics Communication, 4(1), 3219-3229.
48. Kendyala, R., Kurma, J., Mamidala, J. V., Attipalli, A., Enokkaren, S. J., & Bitkuri, V. (2021). A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 35-42.
49. Bitkuri, V., Kendyala, R., Kurma, J., Mamidala, V., Enokkaren, S. J., & Attipalli, A. (2021). Systematic Review of Artificial Intelligence Techniques for Enhancing Financial Reporting and Regulatory Compliance. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 73-80.
