Published on: April 2026
DIGITAL TWIN OF EMBEDDED SYSTEM USING REAL- TIME SENSOR MIRRORING
Andrix Vinusha G Hemaniverthane C R Karthiga M
Dr.Bharathidasan S
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Abstract
- Digital binary tech is honestly a little wild it connects the real world to digital systems in ways that feel straight out of science fiction. In this paper, we get into how to actually build and design a digital twin for embedded systems. The whole idea? Capture exactly what the sensors pick up, right as it happens. That way, you can track everything, really see what’s going on, and catch issues before they blow up. We collect real-time data from all kinds of sensors temperature, vibration, voltage, current, SpO2, you name it. All that info gets processed and zipped through secure IoT channels, then the digital twin gets to work. There’s a cloud-based dashboard that shows exactly what’s happening in the system, live. So you always know how things are running no second guessing. Think of the digital twin as the system’s smart shadow. It crunches sensor data with algorithms, predicts when something’s about to fail, and keeps checking nonstop. So, you spot problems early, without shutting anything down. The result? Better performance, less downtime, and fewer nasty surprises. This setup is perfect for places like factories and hospitals, where the sensor data never lets up. When we tested it, the real system and its digital twin stayed perfectly in sync, working together with zero fuss. We saw more reliability, faster problem detection, and remote monitoring was way easier. By mixing embedded smarts with predictive analytics, the digital twin makes these systems smarter, safer, and more efficient keeping them ready for whatever today throws their way.
How to Cite this Paper
G, A. V., R, H. C. & M, K. (2026). Digital Twin of Embedded System using Real- Time Sensor Mirroring. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.496
G, Andrix, et al.. "Digital Twin of Embedded System using Real- Time Sensor Mirroring." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.496.
G, Andrix,Hemaniverthane R, and Karthiga M. "Digital Twin of Embedded System using Real- Time Sensor Mirroring." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.496.
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References
- Vallee, “Digital Twin for Healthcare Systems,” Frontiers in Digital Health, vol. 5, no. 1253050, pp. 1–8, 2023. DOI: 10.3389/fdgth.2023.1253050.
- Bharathidasan, S. (2009). Image fusion for intelligent transport device. National Conference on Computing, Control and Communication Systems (NCCS’09), Hindusthan College of Engineering and Technology,
- Subramanian, A. Anandpaul, J. Kim, and M. Maray, “Digital Twin Model: A Real-Time Emotion Recognition System for Personalized Healthcare,” IEEE Access, vol. 10,
- 82809–82821, 2022. DOI: 10.1109/ACCESS.2022.3193941.
- Bharathidasan, S. Classification of leukocytes by microscopic images. National Conference on Modeling Analysis and Simulation of Computers and Telecommunication Systems, Government College of Engineering, Salem.
- Alam and A. Ahmed, “IoT-Enabled Digital Twin Framework for Industrial Equipment Monitoring,” in 2022 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2022, pp. 412–
- DOI: 10.1109/ICOSEC55829.2022.9923248.
- Bharathidasan, S. (2024, April 17–18). Enhancing photovoltaic system resilience: A logistic regression approach to fault 3rd International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication and Computational Intelligence (RaEEUCCI–2024). IEEE.
- Wang and J. Yu, “An Edge Computing Enabled Digital Twin for Real-Time Industrial Monitoring,” in 2020 IEEE International Conference on Industrial Informatics (INDIN), Warwick, UK, 2020.pp.735–740.DOI: 10.1109/INDIN45523.2020.9442098.
- Bharathidasan, S. (2023). Internet of Things. RK Publications. ISBN: 978-81-19140-22-0.
- Zhang, X. Li, and M. Zhang, “Digital Twin Driven Predictive Maintenance for Smart Factories,” IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 4128–4138, 2022. DOI: 10.1109/TII.2021.3118732.
- Bharathidasan, (2022, May 20). Design of a Secure Wi-Fi Based Home Automation System Using IoT (Application No. 20224102705). The Patent Office, India
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- •Published on: Apr 19 2026
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