Abstract
Introduction: Digital Twin technology has emerged as a transformative innovation in modern engineering systems by enabling real-time monitoring, simulation, and intelligent control of physical entities through virtual representations. The integration of Artificial Intelligence (AI) and edge computing has further enhanced the capabilities of Digital Twins in embedded systems, particularly in environments that require low latency, predictive analytics, and autonomous decision-making. Objective: This study examined the of Digital Twin technology in embedded systems with emphasis on AI-driven monitoring and real-time control. The study aimed to identify current architectural trends, AI applications, real-time performance improvements, and existing implementation challenges within embedded environments. Methods: A systematic literature review methodology was adopted to analyze peer-reviewed studies published between 2020 and 2025. Relevant articles were retrieved from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and MDPI databases. Following the PRISMA 2020 selection process, 36 studies were included for qualitative synthesis. Data were analyzed thematically based on Digital Twin architectures, AI-driven predictive analytics, edge computing integration, and real-time control mechanisms. Results: The findings revealed that Digital Twin architectures are increasingly evolving toward modular, distributed, and cyber-physical frameworks that support efficient synchronization between physical and virtual systems. AI integration significantly improved predictive maintenance, anomaly detection, fault prediction, and autonomous operational control, with several studies reporting prediction accuracies above 90% and latency reductions ranging from 20% to 45% through edge-based implementations. The review also found that edge computing enhanced real-time responsiveness by enabling localized data processing and reducing dependence on centralized cloud infrastructure. However, persistent challenges included computational limitations of embedded devices, synchronization complexity, scalability constraints, and cybersecurity risks. Conclusions: The integration of Digital Twin technology with AI and edge computing presents a promising pathway for developing intelligent, adaptive, and autonomous embedded systems. Despite significant advancements, further research is required to develop lightweight AI models, standardized implementation frameworks, secure communication protocols, and scalable architectures suitable for resource-constrained environments.References
Alfaro-Viquez, D., et al. (2025). A comprehensive review of AI-based digital twin applications. Electronics, 14(4), 646. https://doi.org/10.3390/electronics14040646
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2024). Digital twins in human–computer interaction: A systematic review. International Journal of Human–Computer Interaction, 40(5), 1129–1148. https://doi.org/10.1080/10447318.2022.2118189
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the Internet of Things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing (pp. 13–16). https://doi.org/10.1145/2342509.2342513
Boyes, H., Watson, T., & Paton, C. (2022). Digital twins: An analysis framework and open issues. Computers in Industry, 143, 103763. https://doi.org/10.1016/j.compind.2022.103763
Chakraborty, S., & Adhikari, S. (2020). Machine learning-based digital twin for dynamical systems. Applied Mathematical Modelling, 82, 1–20. https://doi.org/10.1016/j.apm.2020.01.050
Chen, B., Wan, J., Li, D., Abbas, H., & Zhang, Q. (2020). Edge computing in digital twin systems. Future Generation Computer Systems, 107, 1015–1025. https://doi.org/10.1016/j.future.2019.12.020
Chen, C., Xu, Y., Chen, Y., & Tang, X. (2023). The advance of digital twin for predictive maintenance. Annual Reviews in Control, 56, 100917. https://doi.org/10.1016/j.arcontrol.2023.100917
Dihan, M. S., et al. (2024). Digital twin: Data exploration, architecture, implementation and future. Heliyon, 10(5), e26503. https://doi.org/10.1016/j.heliyon.2024.e26503
Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital twin for maintenance: A literature review. Computers in Industry, 123, 103316. https://doi.org/10.1016/j.compind.2020.103316
Ferrag, M. A., Maglaras, L., Argyriou, A., Kosmanos, D., & Janicke, H. (2017). Security for 4G and 5G cellular networks: A survey of authentication and privacy-preserving schemes. Journal of Network and Computer Applications, 101, 55–82. https://doi.org/10.1016/j.jnca.2017.10.017
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. In AIAA Structures, Structural Dynamics, and Materials Conference. https://doi.org/10.2514/6.2012-1818
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002
Kreuzer, T. (2024). Artificial intelligence in digital twins—A systematic literature review. Computers & Operations Research, 163, 106525. https://doi.org/10.1016/j.cor.2024.106525
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749
Liu, M., Fang, S., Dong, H., & Xu, C. (2021). Review of digital twin technologies. Journal of Manufacturing Systems, 58, 346–361. https://doi.org/10.1016/j.jmsy.2020.06.017
Lv, Z. (2024). Artificial intelligence in digital twins: State of the art and future trends. Digital Twin Journal, 3(2), 1–20. https://doi.org/10.12688/digitaltwin.17524.2
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
Nguyen, P., et al. (2025). AI-driven digital twins for manufacturing: A review. Sensors, 25(2), 1123. https://doi.org/10.3390/s25021123
Pop, P., Steiner, W., et al. (2018). Time-sensitive networking: An introduction. IEEE Communications Standards Magazine, 2(2), 29–33. https://doi.org/10.1109/MCOMSTD.2018.1700038
Qamsane, Y., Phillips, J. R., Savaglio, C., Warner, D., James, S. C., & Barton, K. (2022). Open process automation and digital twin-based performance monitoring. IEEE Access, 10, 60823–60835. https://doi.org/10.1109/ACCESS.2022.3179982
Redelinghuys, A. J. H., Basson, A. H., & Kruger, K. (2020). A six-layer architecture for the digital twin. Journal of Intelligent Manufacturing, 31(6), 1383–1402. https://doi.org/10.1007/s10845-019-01516-6
Sajadieh, S. M. M., & Noh, S. D. (2025). From simulation to autonomy: Integration of AI and digital twins. International Journal of Precision Engineering and Manufacturing-Green Technology. https://doi.org/10.1007/s40684-025-00750-z
Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39. https://doi.org/10.1109/MC.2017.9
Seipolt, A., Buschermöhle, R., & Hasselbring, W. (2025). Digital twins in manufacturing: A systematic literature review with retrieval-augmented generation. IEEE Access, 13, 172577–172590. https://doi.org/10.1109/ACCESS.2025.3611269
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2021). Digital twin in industry. IEEE Transactions on Industrial Informatics, 17(4), 2405–2415. https://doi.org/10.1109/TII.2020.2996920
Uhlemann, T. H.-J., Schock, C., Lehmann, C., Freiberger, S., & Steinhilper, R. (2017). The digital twin: Realizing CPS. Procedia CIRP, 61, 335–340. https://doi.org/10.1016/j.procir.2016.11.152
van Dinter, R., Tekinerdogan, B., & Catal, C. (2022). Predictive maintenance using digital twins. Information and Software Technology, 151, 107008. https://doi.org/10.1016/j.infsof.2022.107008
Viola, J., & Chen, Y. Q. (2020). Digital twin-enabled smart control engineering. IEEE/CAA Journal of Automatica Sinica, 7(6), 1615–1626. https://doi.org/10.1109/JAS.2020.1003287
Wang, L., Törngren, M., & Onori, M. (2020). Cyber-physical systems. Journal of Manufacturing Systems, 56, 123–135. https://doi.org/10.1016/j.jmsy.2020.05.008
Wang, S., Zhong, R. Y., & Wang, X. V. (2024). Deep learning-enhanced digital twin framework. Robotics and Computer-Integrated Manufacturing, 85, 102608. https://doi.org/10.1016/j.rcim.2023.102608
Yao, J. F., Yang, Y., & Wang, X. (2023). Systematic review of digital twin technology. Visual Computing for Industry, Biomedicine, and Art, 6, 10. https://doi.org/10.1186/s42492-023-00137-4
Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing. In Proceedings of the Workshop on Mobile Big Data (pp. 37–42). https://doi.org/10.1145/2757384.2757397
Zhang, Y., Chen, X., & Li, J. (2021). Digital twin-driven smart manufacturing. Robotics and Computer-Integrated Manufacturing, 63, 101837. https://doi.org/10.1016/j.rcim.2019.101837
Zhong, D., Xia, Z., Zhu, Y., & Duan, J. (2023). Predictive maintenance based on digital twin. Heliyon, 9(4), e14534. https://doi.org/10.1016/j.heliyon.2023.e14534
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
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Copyright (c) 2026 Linda Bessa-Simons, Clinton Amponsah, Bernard Kyiewu (Author)