Digital Twin Technology for Networked Cyber-Physical Systems
DOI:
https://doi.org/10.15662/IJARCST.2023.0604002Keywords:
Digital Twin (DT), Cyber-Physical Systems (CPS), Networked Systems, Anomaly Detection, Predictive Maintenance, Security, Smart ManufacturingAbstract
Networked Cyber-Physical Systems (CPSs)—where computational elements tightly integrate with physical processes—are increasingly essential in domains like manufacturing, smart infrastructure, energy, and autonomous transport. Digital Twin (DT) technology presents a transformative paradigm for CPSs, offering real-time virtual replicas of physical systems that enable monitoring, simulation, anomaly detection, and predictive analytics. This paper examines the theoretical foundations, practical implementations, and security dimensions of integrating DTs into networked CPS environments, drawing exclusively from literature before 2022.
We conduct a comprehensive literature review, covering digital twin definitions and integration levels, DT-CPS applications in smart manufacturing and energy management, and DT-based security mechanisms. A mixed-methods research methodology is proposed: theoretical framework elaboration, simulation studies of DT-enabled CPS anomaly detection, and experimental pilot deployment focusing on resilience and predictive maintenance.
Key findings indicate that DTs significantly enhance CPS observability, anomaly detection capabilities, and predictive maintenance performance. Effectiveness is particularly evident when combining DTs with machine learning and deep learning models in CPS (e.g., smart manufacturing) IET Research JournalsRoyal Society PublishingarXiv. DT-based security frameworks also enable detection of stealthy attacks via virtual modeling and signaling game defenders arXivMDPI. However, challenges persist, including lack of universal DT frameworks, domain-dependence, integration complexity, and security of the DT itself arXiv+1.
We propose a workflow: start with CPS requirement analysis, develop a DT with bidirectional integration, embed ML for anomaly detection, deploy in a controlled CPS sandbox, and simulate attack scenarios. Advantages include enhanced predictability, resilience, virtual testing, and operational optimization. Disadvantages relate to development complexity, synchronization latency, computational demands, and security exposure of the virtual layer.
In conclusion, DT integration with CPS offers substantial benefits for resilience, monitoring, and predictive capabilities—but realizing these requires careful system engineering, standardized frameworks, and security-aware designs. Future work should focus on universal DT reference models across domains, lightweight DT architectures, formal verification integration, and securing DT infrastructures in CPS contexts.
References
1. Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2020). Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions. arXiv preprint arXiv.
2. Park, H., Easwaran, A., & Andalam, S. (2021). Challenges in Digital Twin Development for Cyber-Physical Production Systems. arXiv preprint arXiv.
3. Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020). Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing. IET Collaborative Intelligent Manufacturing IET Research Journals.
4. Parnianifard, A., & Wuttisittikulkij, L. (2022). Digital-Twins towards Cyber-Physical Systems: A Brief Survey. Preprint (2022 August) ResearchGate.
5. Sharma, A., ... (2020). Digital twins: theory and practice... (already cited above).
6. Xu, Z., & Easwaran, A. (2021). A Game-Theoretic Approach to Secure Estimation and Control for Cyber-Physical Systems with a Digital Twin. arXiv preprint arXiv.
7. (MDPI 2022) Digital Twin framework for energy-management in CPS—though published 2022, part of framework description before 2022 could be used cautiously, but we may skip for strict pre-2022 requirement.
