An Integrated NFT, Deep Learning and AR Framework to enable Intelligent Industrial Monitoring in Industry 4.0
DOI:
https://doi.org/10.33736/jaspe.12676.2026Keywords:
Industry 4.0, Non-Fungible Token, Deep Learning, Predictive Maintenance, Industrial IoT, Augmented Reality, BlockchainAbstract
Despite the rapid advancement of Industry 4.0, manufacturing environments continue to struggle with the secure synchronization of physical assets and digital twins often leading to fragmented data, lack of trust in automated diagnostics and inefficient human-machine interaction. Traditional centralized systems are vulnerable to data tampering and fail to provide a transparent audit trail for predictive maintenance and quality assurance. This study proposed an integrated framework that leverages the synergy between Non-Fungible Tokens (NFTs), Deep Learning (DL), the Industrial Internet of Things (IIoT) and Augmented Reality (AR). The study aims to develop a decentralized identity layer for industrial assets, implement high-accuracy predictive maintenance models and create a real-time visualization ecosystem to bridge the gap between digital analytics and physical operations. The study utilizes a three-tiered approach: first, an ERC-721 smart contract was developed and deployed on an Ethereum-based blockchain to tokenize IIoT assets, ensuring immutable record-keeping. Second, Feedforward and Convolutional Neural Networks (CNNs) were trained on the AI4I 2020 Predictive Maintenance Dataset to classify multi-class failure modes. Finally, an AR framework was designed to superimpose these decentralized digital twins and real-time AI analytics onto the physical industrial environment. Experimental validation of the smart contract deployment demonstrated a low-cost execution profile (0.0107 ETH), proving the feasibility of high-frequency asset synchronization. The deep learning models achieved robust performance in identifying critical failure types with the CNN specifically excelling in detecting general machine failure, while the Feedforward network showed superior accuracy in multiclass failure categorization. This research establishes a novel paradigm for “Secure Intelligent Manufacturing.” By using NFTs as digital anchors for physical assets, the framework ensures unparalleled data integrity and traceability. When combined with AR visualization, it empowers engineers with verifiable, real-time insights, significantly reducing operational downtime and enhancing the reliability of autonomous industrial systems.
References
Li, L. (2018). China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”. Technological forecasting and social change, 135, 66-74. https://doi.org/10.1016/j.techfore.2017.05.028
Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE access, 6, 3585-3593. https://doi.org/10.1109/ACCESS.2018.2793265
Malmodin, J., & Lundén, D. (2018). The energy and carbon footprint of the global ICT and E&M sectors 2010–2015. Sustainability, 10(9), 3027. https://doi.org/10.3390/su10093027
Ray, P. P. (2018). A survey on Internet of Things architectures. Journal of King Saud University Computer and Information Sciences, 30(3), 291-319. https://doi.org/10.1016/j.jksuci.2016.10.003
Siyal, F., Guzzo, A., Alkhabbas, F., Sacca, D., & Fortino, G. (2026). Secure Supply Chain Provenance via PUF-Anchored NFTs and 6G Edge Networks. IEEE Wireless Communications. https://doi.org/10.1109/MWC.2025.3634094
Serena, L., Ferretti, S., Marzolla, M., & D’Angelo, G. (2025, July). An NFT-Based Solution to Enhance Trust in Decentralized Marketplaces. In 2025 IEEE Symposium on Computers and Communications (ISCC) (pp. 1-6). IEEE. https://doi.org/10.1109/ISCC65549.2025.11326256
Chen, P., Liu, X., Cheng, W., & Huang, R. (2017). A review of using Augmented Reality in Education from 2011 to 2016. Innovations in smart learning, 13-18. https://doi.org/10.1007/978-981-10-2419-1_2
Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2020). Industrial internet of things: Recent advances, enabling technologies and open challenges. Computers & electrical engineering, 81, 106522. https://doi.org/10.1016/j.compeleceng.2019.106522
Liao, Y., Loures, E. D. F. R., & Deschamps, F. (2018). Industrial Internet of Things: A systematic literature review and insights. IEEE Internet of Things Journal, 5(6), 4515-4525. https://doi.org/10.1109/JIOT.2018.2834151
Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., & Eschert, T. (2016). Industrial internet of things and cyber manufacturing systems. In Industrial internet of things: Cybermanufacturing systems (pp. 3-19). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-42559-7_1
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of manufacturing systems, 48, 144-156. https://doi.org/10.1016/j.jmsy.2018.01.003
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020). Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials, 13(24), 5755. https://doi.org/10.3390/ma13245755
Ceschin, F., & Gaziulusoy, I. (2016). Evolution of design for sustainability: From product design to design for system innovations and transitions. Design studies, 47, 118-163. https://doi.org/10.1016/j.destud.2016.09.002
Bloch, P. H. (2011). Product design and marketing: Reflections after fifteen years. Journal of Product Innovation Management, 28(3), 378-380. https://doi.org/10.1111/j.1540-5885.2011.00805.x
Van Nes, N., & Cramer, J. (2005). Influencing product lifetime through product design. Business Strategy and the Environment, 14(5), 286-299. https://doi.org/10.1002/bse.491
Karana, E., Hekkert, P., & Kandachar, P. (2008). Material considerations in product design: A survey on crucial material aspects used by product designers. Materials & Design, 29(6), 1081-1089. https://doi.org/10.1016/j.matdes.2007.06.002
Banker, R. D., Bardhan, I., & Asdemir, O. (2006). Understanding the impact of collaboration software on product design and development. Information Systems Research, 17(4), 352-373. https://doi.org/10.1287/isre.1060.0104
Bottani, E., & Vignali, G. (2019). Augmented reality technology in the manufacturing industry: A review of the last decade. Iise Transactions, 51(3), 284-310. https://doi.org/10.1080/24725854.2018.1493244
Egger, J., & Masood, T. (2020). Augmented reality in support of intelligent manufacturing–a systematic literature review. Computers & Industrial Engineering, 140, 106195. https://doi.org/10.1016/j.cie.2019.106195
Musamih, A., Yaqoob, I., Salah, K., Jayaraman, R., Omar, M., & Ellahham, S. (2022). Using NFTs for product management, digital certification, trading, and delivery in the healthcare supply chain. IEEE Transactions on Engineering Management, 71, 4480-4501. https://doi.org/10.1109/TEM.2022.3215793
Hafeez, T., Xu, L., & Mcardle, G. (2021). Edge intelligence for data handling and predictive maintenance in IIOT. IEEE access, 9, 49355-49371. https://doi.org/10.1109/ACCESS.2021.3069137
Rosales, J., Deshpande, S., & Anand, S. (2021). IIoT based augmented reality for factory data collection and visualization. Procedia Manufacturing, 53, 618-627. https://doi.org/10.1016/j.promfg.2021.06.062
Khalil, M., Bergs, C., Papadopoulos, T., Wüchner, R., Bletzinger, K. U., & Heizmann, M. (2019, July). IIoT-based fatigue life indication using augmented reality. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (Vol. 1, pp. 746-751). IEEE. https://doi.org/10.1109/INDIN41052.2019.8972114
Pereira, A. C., & Romero, F. (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia manufacturing, 13, 1206-1214. https://doi.org/10.1016/j.promfg.2017.09.032
Leng, J., Ye, S., Zhou, M., Zhao, J. L., Liu, Q., Guo, W., Cao, W., & Fu, L. (2020). Blockchain-secured smart manufacturing in industry 4.0: A survey. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 237-252. https://doi.org/10.1109/TSMC.2020.3040789
Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X., & Liu, C. (2020). Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey. Renewable and sustainable energy reviews, 132, 110112. https://doi.org/10.1016/j.rser.2020.110112
Behl, A., Pereira, V., Nigam, A., Wamba, S., & Sindhwani, R. (2024). Knowledge development in non-fungible tokens (NFT): a scoping review. Journal of Knowledge Management, 28(1), 232-267. https://doi.org/10.1108/JKM-12-2022-0937
Singh, A. K., Kumar, V. P., Dehdasht, G., Mohandes, S. R., Manu, P., & Rahimian, F. P. (2023). Investigating the barriers to the adoption of blockchain technology in sustainable construction projects. Journal of cleaner production, 403, 136840. https://doi.org/10.1016/j.jclepro.2023.136840
Sarker, I. H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 420. https://doi.org/10.1007/s42979-021-00815-1
Abdel-Basset, M., Chang, V., Hawash, H., Chakrabortty, R. K., & Ryan, M. (2024). Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey. Annals of Operations Research, 339(1), 3-51. https://doi.org/10.1007/s10479-021-04164-3
Al-Qatf, M., Lasheng, Y., Al-Habib, M., & Al-Sabahi, K. (2018). Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. Ieee Access, 6, 52843-52856. https://doi.org/10.1109/ACCESS.2018.2869577.
Kumar, A., Satheesha, T. Y., Salvador, B. B. L., Mithileysh, S., & Ahmed, S. T. (2023). Augmented Intelligence enabled Deep Neural Networking (AuDNN) framework for skin cancer classification and prediction using multi-dimensional datasets on industrial IoT standards. Microprocessors and Microsystems, 97, 104755. https://doi.org/10.1016/j.micpro.2023.104755
Vasilevski, N., & Birt, J. (2021). Human-centered design science research evaluation for gamified augmented reality. Frontiers in Virtual Reality, 2, 713718. https://doi.org/10.3389/frvir.2021.713718
Salehin, I., Islam, M. S., Amin, N., Baten, M. A., Noman, S. M., Saifuzzaman, M., & Yazmyradov, S. (2023). Real‐time medical image classification with ML framework and dedicated CNN–LSTM architecture. Journal of Sensors, 2023(1), 3717035. https://doi.org/10.1155/2023/3717035
Muff, F., & Fill, H. G. (2023, October). A domain-specific visual modeling language for augmented reality applications using WebXR. In International Conference on Conceptual Modeling (pp. 334-353). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47262-6_18
Lv, Z., Halawani, A., Feng, S., Li, H., & Réhman, S. U. (2014). Multimodal hand and foot gesture interaction for handheld devices. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 11(1s), 1-19. https://doi.org/10.1145/2645860
Crowther, K. G., Upadrashta, R., & Ramachandra, G. (2022, November). Securing over-the-air firmware updates (FOTA) for industrial internet of things (IIOT) devices. In 2022 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-8). IEEE. https://doi.org/10.1109/HST56032.2022.10025441
Michailidis, E. T., Potirakis, S. M., & Kanatas, A. G. (2020). AI-inspired non-terrestrial networks for IIoT: Review on enabling technologies and applications. IoT, 1(1), 3. https://doi.org/10.3390/iot1010003
Pal, S., & Jadidi, Z. (2021). Analysis of security issues and countermeasures for the industrial internet of things. Applied Sciences, 11(20), 9393. https://doi.org/10.3390/app11209393
Corallo, A., Lazoi, M., Lezzi, M., & Luperto, A. (2022). Cybersecurity awareness in the context of the Industrial Internet of Things: A systematic literature review. Computers in Industry, 137, 103614. https://doi.org/10.1016/j.compind.2022.103614
Chalapathi, G. S. S., Chamola, V., Vaish, A., & Buyya, R. (2021). Industrial internet of things (IIoT) applications of edge and fog computing: A review and future directions. Fog/Edge Computing For Security, Privacy, and Applications, 293-325. https://doi.org/10.1007/978-3-030-57328-7_12
Psarommatis, F., May, G., & Azamfirei, V. (2023). Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework. Journal of Manufacturing Systems, 68, 376-399. https://doi.org/10.1016/j.jmsy.2023.04.009
Xu, Y., Xu, H., Chen, X., Zhang, H., Chen, B., & Han, Z. (2025). Blockchain-based AR offloading in UAV-enabled MEC networks: A trade-off between energy consumption and rendering latency. IEEE Transactions on Vehicular Technology, 74(11), 18065-18080. https://doi.org/10.1109/TVT.2025.3581015.
Fernández-Iglesias, M. J., Delgado von Eitzen, C., & Anido-Rifón, L. (2024). Efficient traceability systems with smart contracts: Balancing on-chain and off-chain data storage for enhanced scalability and privacy. Applied Sciences, 14(23), 11078. https://doi.org/10.3390/app142311078
Mayer, R., & Jacobsen, H. A. (2020). Scalable deep learning on distributed infrastructures: Challenges, techniques, and tools. ACM Computing Surveys (CSUR), 53(1), 1-37. https://doi.org/10.1145/3363554
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