Ontology-Mediated State Synchronization across Heterogeneous Industrial Digital Twins
Keywords:
Digital Twins, Ontology, State Synchronization, Semantic Interoperability, Cyber-Physical SystemsAbstract
The rapid proliferation of Industry 4.0 paradigms has positioned Digital Twins as a fundamental technological pillar for modern cyber-physical systems. As these virtual replicas become more ubiquitous, industrial environments increasingly deploy multiple, heterogeneous Digital Twins to monitor and control diverse assets, ranging from robotic manipulators to automated guided vehicles. A significant challenge arises when these heterogeneous Digital Twins must interact, as discrepancies in their underlying data models, communication protocols, and temporal state representations lead to severe interoperability bottlenecks. To address this challenge, this paper proposes a comprehensive framework for ontology-mediated state synchronization across heterogeneous industrial Digital Twins. By abstracting asset-specific data structures into a unified semantic representation using top-level and domain-specific ontologies, the proposed framework enables seamless state translation and synchronization in real-time. The methodology incorporates semantic mapping algorithms, conflict resolution mechanisms, and a centralized semantic middleware architecture designed to minimize synchronization drift. Through rigorous mathematical modeling of state transitions and empirical validation on a multi-asset industrial testbed, this research demonstrates that ontology mediation can maintain high-fidelity state alignment across disparate systems with acceptable computational latency. The findings offer a scalable and resilient approach to realizing fully interoperable, system-of-systems Digital Twin architectures in complex manufacturing environments, thereby facilitating advanced cooperative automation and predictive maintenance strategies.
Downloads
References
1. Yao, Y., Lu, Z., Chen, G., Huang, C., Feng, C., & Quek, T. Q. (2026). Interference Management in ISAC-SAGINs Based on Transformer-Enabled Mean-Field Reinforcement Learning Method. IEEE Transactions on Wireless Communications.
2. Yao, Y., Sun, J., Miao, P., Chen, G., & Tafazolli, R. (2026). Energy-Efficient Beamforming for STAR-RIS-Aided ISAC With Hardware Impairments: A Generative AI-Enabled DRL Method. IEEE Transactions on Wireless Communications.
3. Li, R., Wang, Y., Wang, C., Chen, J., Wu, D., Han, G., & Zeng, Y. (2026). Digital Twin-Assisted 3D Channel Knowledge Map Inference and Multi-Type Base Station Deployment Optimization for Urban Low-Altitude Economy. IEEE Transactions on Mobile Computing.
4. Wei, X., Li, R., Chen, Y., Xu, L., Wang, L., & Han, Z. (2026). Coordinated rate-splitting multiple access for emergency UAV-enabled integrated sensing and communication. IEEE Transactions on Cognitive Communications and Networking.
5. Zhang, Z., Gao, S., Su, S., Sun, L., & Chen, R. (2023). MINING: Multi-granularity network alignment based on contrastive learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12785-12798.
6. Gao, S., Zhang, Z., Su, S., Wen, J., & Sun, L. (2020). Fair-aware competitive event influence maximization in social networks. IEEE Transactions on Network Science and Engineering, 7(4), 2528-2540.
7. Wang, F., Sun, L., & Zhang, Z. (2020, December). Hyperbolic user identity linkage across social networks. In GLOBECOM 2020-2020 IEEE Global Communications Conference (pp. 1-6). IEEE.
8. Xiao, W., Tan, Q., Chen, M., Yao, Y., & Shu, F. (2026). Pareto-Optimal Beamforming Design for RSMA-RHS-Aided ISAC System with Imperfect CSI. IEEE Transactions on Vehicular Technology.
9. Sun, L., Zhang, Z., Ji, P., Wen, J., Su, S., & Yu, P. S. (2019, December). DNA: Dynamic social network alignment. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 1224-1231). IEEE.
10. Li, Q., Ye, Q., Zhang, N., Zhang, W., & Hu, F. (2025). Digital-twin-enabled industrial IoT: Vision, framework, and future directions. IEEE Wireless Communications, 32(6), 173-181.
11. Pei, Z., Huang, Q., & Wang, S. (2026). When LLMs develop languages: Symbolic communication for efficient multi-agent reasoning. arXiv preprint arXiv:2606.29354.
12. Wang, Y., Wang, Z., Kim, E. H., & Fu, Z. FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting. In Forty-third International Conference on Machine Learning.
13. Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
14. Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
15. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
16. Feng, Y., Li, Q., Chen, Y., Wu, Q., & Wen, M. (2024). RIS-aided symbiotic radio: A number and phase modulation approach. IEEE Transactions on Cognitive Communications and Networking, 10(5), 1597-1607.
17. Meng, L., Jia, Z., Hu, X., Zhang, B., & Cao, L. (2026). Constructing heritage narrative corridors of the Great Tea Road in Central Shanxi based on a dual-value-multilayer network framework. npj Heritage Science.
18. Kim, E. H., Huang, H., Wang, Z., Duan, H., Fu, Z., & Pedrycz, W. (2026). Fuzzy Neural Module Network: Leveraging Univariate Models and Layer-Specific and Network-Wide Dual Learning. IEEE Transactions on Cybernetics.
19. Hu, Y., Yang, Z., Zhao, C., & Ji, W. (2026, May). Adaptive guidance semantically enhanced via multimodal llm for edge-cloud object detection. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12962-12966). IEEE.
20. Tian, Y., & Yang, Z. (2026, May). SAEC: scene-aware enhanced edge-cloud collaborative industrial vision inspection with multimodal LLM. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 19747-19751). IEEE.
21. Yang, Z., Ji, W., Guo, Q., Zhao, J., Zhao, C., Zhang, X., ... & You, Y. (2026). CLAP: Cross-Layer Adaptive Pipelining Inference Scheduling for Resource-Efficient Edge-Cloud Vision Systems. ACM Transactions on Architecture and Code Optimization.
22. Wang, Y. (2025, May). Construction of a Clinical Trial Data Anomaly Detection and Risk Warning System based on Knowledge Graph. In Forum on Research and Innovation Management (Vol. 3, No. 6, pp. 40-42).
23. Tang, J., Wang, Z., Gong, Z., Yu, J., Zhu, X., & Yin, J. (2025, April). Multi-grained query-guided set prediction network for grounded multimodal named entity recognition. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 24, pp. 25246-25254).
24. Leroy, C., Peretz, Y., Wang, Z., Suzuki, H., & Smith, N. (2026, May). A Multi-Source Temporal Graph Object Detection Network for Rare-Event Blockchain Anomaly Localization. In 2026 2nd International Conference on Mechatronics, Robotics, and Artificial Intelligence (MRAI) (pp. 512-516). IEEE.
25. Li, Q., Feng, Y., Wen, M., Wen, J., Alexandropoulos, G. C., Basar, E., & Poor, H. V. (2024). Cooperative backscatter communications with reconfigurable intelligent surfaces: An APSK approach. IEEE Transactions on Wireless Communications, 23(11), 16218-16233.
26. Xu, Yuanchao, Amal Feriani, and Ekram Hossain. "Decentralized multi-agent reinforcement learning for task offloading under uncertainty." arXiv preprint arXiv:2107.08114 (2021).
27. Wang, E., Deng, Z., Pan, C., He, B., & Zhang, J. (2025, October). Estimating Continuum Robot Shape under External Loading using spatiotemporal Neural Networks. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 21645-21650). IEEE.
28. Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003). The Google file system. In Proceedings of SOSP.
29. Dean, J., & Ghemawat, S. (2004). MapReduce: Simplified data processing on large clusters. In Proceedings of OSDI.
30. Burrows, M. (2006). The Chubby lock service for loosely-coupled distributed systems. In Proceedings of OSDI.
31. DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., et al. (2007). Dynamo: Amazon’s highly available key-value store. In Proceedings of SOSP.
32. Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Proceedings of NSDI.
33. Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., et al. (2010). Spark: Cluster computing with working sets. In Proceedings of HotCloud.
34. Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, and
Bin Liang. Offline reinforcement learning with value-based episodic memory. arXiv preprint
arXiv:2110.09796, 2021b.
35. Lin, Y., Yao, Y., Zhu, J., & He, C. (2025, July). Application of generative AI in predictive analysis of urban energy distribution and traffic congestion in smart cities. In 2025 10th International Conference on Cyber Security and Information Engineering (ICCSIE) (pp. 620-623). IEEE.
36. Tao, J., Lyu, R., & Cao, X. (2026). A Scalable Data Governance Architecture for Privacy-Aware Intelligent Learning Systems in Lifelong. Future-Adaptive Intelligence and Lifelong Systems, 1 (1).
37. Zhao, K., Li, G., Li, C., Jiang, T., Wang, W., & Yang, Z. (2026). Identification Markers for Salvia miltiorrhiza and Its Close Relatives Based on Cell Wall Component Characteristics. Engineered Science, 40, 2122.
38. Jia, Y., Yu, J., Liu, T., Huang, J., Mu, W., & Deng, F. (2024). Research on the matching effect of social media advertising appeal and narrative person: evidence from China. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 2980-2996.
39. Luo, T., Li, Q., Jiang, M., & Hou, C. (2025). Nonconvex and Adaptive Multi-Label Learning for Highly Incomplete Labels. Knowledge-Based Systems, 114784.
40. Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gabriel Reyes, Layla Gray
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This article is licensed under the Creative Commons Attribution 4.0 International License. This license permits use, sharing, and adaptation of the article for non-commercial purposes, provided the original authors and source are properly credited.
Alternatively, you may choose other Creative Commons licenses based on your preferences:
-
CC Attribution-NonCommercial-NoDerivatives 4.0: This license allows others to download the works and share them with others as long as they credit the author, but they can’t change them in any way or use them commercially.
-
CC Attribution-NonCommercial 4.0: This license allows others to remix, tweak, and build upon the work non-commercially, as long as they credit the author, but they cannot use the material for commercial purposes.
-
CC Attribution-NonCommercial-ShareAlike 4.0: This allows others to remix, tweak, and build upon the work non-commercially, as long as they credit the author and license their new creations under identical terms.
These terms ensure that your article is freely accessible and usable, while respecting the rights of the original authors.
