eISSN: 3087-4289 editor@aimjournals.com
All Journals
Submit your article

International Journal of Modern Computer Science and IT Innovations

Open Access Peer Review International
Open Access

Architectural Synergies: Integrating Blockchain, Fog Computing, And Generative Intelligence for Secure Digital Twin Ecosystems in Cyber-Physical Systems

DOI
PDF
Hiroshi Tanaka 1 ,
4 Department of Systems Engineering, University of Technology Sydney, Australia

Abstract

The rapid convergence of Cyber-Physical Systems (CPS) with the Industrial Internet of Things (IIoT) has necessitated the evolution of sophisticated monitoring frameworks, prominently manifest in the form of Digital Twins (DT). While Digital Twins provide a transformative mechanism for real-time monitoring and predictive maintenance, their implementation in complex environments remains fraught with challenges regarding security, data integrity, and architectural scalability. This article explores the integration of blockchain-based access management, fog computing infrastructures, and generative artificial intelligence to address these critical deficiencies. By synthesizing existing research on multi-fidelity data fusion and secure provenance schemes, this study presents a comprehensive architectural framework designed to support the next generation of industrial applications. The proposed model utilizes fog computing to facilitate low-latency data processing while leveraging blockchain to ensure decentralized, immutable auditability of sensitive sensor data. Furthermore, the inclusion of generative intelligence for sensor fusion allows for the construction of high-fidelity models that are resilient to the noise and uncertainty inherent in real-world deployments. Through a rigorous examination of the literature, including systematic mapping studies and formal testing protocols, this research identifies the essential requirements for standardization-aligned DT ecosystems. The analysis concludes that the unification of these distributed technologies is imperative for achieving fault-tolerant, scalable, and trustworthy CPS environments, providing a roadmap for practitioners and researchers to navigate the complexities of Industry 4.0 and beyond.

Keywords

Digital Twin, Blockchain, Fog Computing, Generative AI

References

Novo O., Blockchain meets IoT: An architecture for scalable access management in IoT, IEEE Internet Things J., 5 (2) (2018), pp. 1184-1195
Zafar F. et al., Trustworthy data: A survey, taxonomy and future trends of secure provenance schemes, J. Netw. Comput. Appl., 94 (2017), pp. 50-68
Suhail S., Hussain R., Jurdak R., Hong C.S., Trustworthy digital twins in the industrial internet of things with blockchain, IEEE Internet Comput., 26 (3) (2022), pp. 58-67
DurĂŁo L., Haag S., Anderl R., SchĂŒtzer K., Zancul E., Digital twin requirements in the context of industry 4.0, 15th IFIP International Conference on Product Lifecycle Management, AICT-540 of Product Lifecycle Management To Support Industry 4.0, Springer International Publishing (2018), pp. 204-214
Liu L., Song X., Zhang C., Tao D., Gan-mdf: An enabling method for multi-fidelity data fusion, IEEE Internet Things J. (2022), p. 1
Rasheed A., San O., Kvamsdal T., Digital twin: Values, challenges and enablers from a modeling perspective, IEEE Access, 8 (2020), pp. 21980-22012
Wang Y., Su Z., Guo S., Dai M., Luan T.H., Liu Y., A survey on digital twins: Architecture, enabling technologies, security and privacy, and future prospects, IEEE Internet Things J. (2023), p. 1
Arrieta A., Multi-fidelity digital twins: a means for better cyber–physical systems testing? (2021)
Bouachir O., Aloqaily M., Tseng L., Boukerche A., Blockchain and fog computing for cyberphysical systems: The case of smart industry, Computer, 53 (9) (2020), pp. 36-45
Tsang Y.P., Lee C.K.M., Zhang K., Wu C.H., Ip W.H., On-chain and off-chain data management for blockchain-internet of things: A multi-agent deep reinforcement learning approach, J. Grid Comput., 22 (1) (2024), p. 16
Iqbal M., Matulevičius R., Exploring sybil and double-spending risks in blockchain systems, IEEE Access, 9 (2021), pp. 76153-76177
Ala-Laurinaho R., Sensor data transmission from a physical twin to a digital twin, Master’s thesis, School Eng., Master Sci. Technol. Mech. Eng., Aalto Univ., Otaniemi, Espoo (2019)
De Benedictis A., Mazzocca N., Somma A., Strigaro C., Digital twins in healthcare: An architectural proposal and its application in a social distancing case study, IEEE J. Biomed. Health Informat., vol. 27, no. 10, pp. 5143–5154 (2022)
Noeikham P., Buakum D., Sirivongpaisal N., Architecture designing of digital twin in a healthcare unit, Health Informat. J., vol. 30, no. 4 (2024)
Harode A., Thabet W., Dongre P., A tool-based system architecture for a digital twin: A case study in a healthcare facility, J. Inf. Technol. Construct., vol. 28, pp. 107–137 (2023)
Jþrgensen C. S., Shukla A., Katt B., Digital twins in healthcare: Security, privacy, trust and safety challenges, Proc. Eur. Symp. Res. Comput. Secur., Springer (2023), pp. 140–153
Simonetti D., Hendriks M., Koopman B., Keijsers N., Sartori M., A wearable gait lab powered by sensor-driven digital twins for quantitative biomechanical analysis post-stroke, Wearable Technol., vol. 5 (2024)
Ferko E., Towards a standards-based architecture for digital twins facilitating interoperability, M.S. thesis, School Innov., Des. Eng., Malardalen Univ., VÀsterÄs, Sweden (2023)
Khan M.E., Khan F., A comparative study of white box, black box and grey box testing techniques, Int. J. Adv. Comput. Sci. Appl., 3 (6) (2012)
Jones D., Snider C., Nassehi A., Yon J., Hicks B., Characterising the Digital Twin: A systematic literature review, CIRP J. Manuf. Sci. Technol. (2020)
Semeraro C., Lezoche M., Panetto H., Dassisti M., Digital twin paradigm: A systematic literature review, Comput. Ind., 130 (2021)
Kitchenham B.A., Charters S., Guidelines for Performing Systematic Literature Reviews in Software Engineering, Tech. Rep. EBSE 2007-001, Keele University and Durham University Joint Report (2007)
Petersen K., Vakkalanka S., Kuzniarz L., Guidelines for conducting systematic mapping studies in software engineering: An update, Vol. 64, Elsevier (2015), pp. 1-18
Garousi V., Felderer M., MÀntylÀ M.V., Guidelines for including grey literature and conducting multivocal literature reviews in software engineering, Inf. Softw. Technol., 106 (2019), pp. 101-121
Tsafnat G., Glasziou P., Choong M.K., Dunn A., Galgani F., Coiera E., Systematic review automation technologies, Syst. Rev., 3 (2014)
Martín-Lopo M.M., Boal J., Sånchez-Miralles Á., Transitioning from a meta-simulator to electrical applications: An architecture, Simul. Model. Pract. Theory, 94 (2019), pp. 177-198
Standards B., Automation Systems and Integration. Digital Twin Framework for Manufacturing, BS ISO 23247:2021, Standard (2021)
International M.S.C., Gics - global industry classification standard (1999)
ElMaraghy W., ElMaraghy H., Tomiyama T., Monostori L., Complexity in engineering design and manufacturing, CIRP Ann., 61 (2) (2012), pp. 793-814
Ansari S., Chandel A., Tariq M., A comprehensive review on power converters control and control strategies of AC/DC microgrid, IEEE Access, 9 (2021), pp. 17998-18015
Worden K., Barthorpe R., Cross E., Dervilis N., Holmes G., Manson G., Rogers T., On evolutionary system identification with applications to nonlinear benchmarks, Mech. Syst. Signal Process., 112 (2018), pp. 194-232
Koutsoubelias M., Grigoropoulos N., Lalis S., A modular simulation environment for multiple UAVs with virtual WiFi and sensing capability, 2018 IEEE Sensors Applications Symposium (SAS) (2018), pp. 1–6
Wu J., Zhao Y., Yin X., From active to passive: Progress in testing of internet routing protocols, Kim M., Chin B., Kang S., Lee D. (Eds.), Formal Techniques for Networked and Distributed Systems, Springer US (2001), pp. 101-116
M. A. Hussain, V. B. Meruga, A. K. Rajamandrapu, S. R. Varanasi, S. S. S. Valiveti and A. G. Mohapatra, "Generative AI Sensor Fusion for Secure Digital Twin Ecosystems: A Standardization-Aligned Framework for Cyber-Physical Systems," in IEEE Communications Standards Magazine, doi: 10.1109/MCOMSTD.2026.3660106.

Most read articles by the same author(s)

<< < 5 6 7 8 9 10 11 12 > >> 

Similar Articles

1-10 of 78

You may also start an advanced similarity search for this article.