Reviews article
Global Digital Deployment and Innovation Technology: A Review of Engineering Pathways, Scenario Adaptation, and Industrial Transformation
Abstract
Digital technologies have evolved from auxiliary tools into core drivers of global economic and industrial restructuring. However, a persistent gap remains between the precision of algorithmic innovation in controlled environments and the readiness of these technologies for deployment in complex, heterogeneous industrial settings. This review examines the full chain from digital theory through engineering deployment to industrial innovation, adopting a deployment-centric lens that distinguishes it from algorithm-focused surveys. We introduce the DEPLOY framework, a four-dimensional evaluation space comprising Deployment Maturity, Engineering Feasibility, Ecosystem Readiness, and Scenario Adaptability, and apply it across five thematic clusters: digital infrastructure architecture, intelligent algorithm deployment, digital twin engineering, network security and trusted deployment, and industrial transformation scenarios. The review draws on a systematic literature analysis spanning 2018–2026, covering cloud-edge-end collaborative architectures, model compression and edge intelligence, digital twin fidelity and interoperability trade-offs, zero-trust and federated learning security paradigms, and cross-sector deployment patterns in manufacturing, cities, energy, and education. Our synthesis reveals that the most critical deployment bottlenecks are not at the technology layer but at the systemic and ecosystem layers, where interoperability failures, standards fragmentation, and incentive misalignment create barriers that purely technical solutions cannot address. We propose the Scenario-Driven Innovation (SDI) conceptual framework, which formalizes a closed-loop paradigm in which deployment feedback, rather than algorithmic novelty, drives theoretical advancement. Five concrete research directions are outlined to bridge the identified deployment gaps.
Keywords
References
[1]Aloqaily, M., Paik, H., Lunardi, W. T., Tunc, C., & He, F. (2024). Guest Editorial: Zero Trust Security Methods for Wireless Networks. IEEE Wireless Communications. https://doi.org/10.1109/mwc.2024.10495912
[2]Arora, J., & Tushir, M. (2021). Digital Twin. CRC Press. https://doi.org/10.1201/9781003132868-2
[3]Borza, D.-L., Darabant, A. S., Ileni, T. A., & Marinescu, A.-I. (2022). Effective Online Knowledge Distillation via Attention-Based Model Ensembling. Mathematics. https://doi.org/10.3390/math10224285
[4]Chen, C.-C., Chou, Y.-M., & Chou, J. (2023). PHY: A performance-driven hybrid communication compression method for distributed training. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2023.104719
[5]Chen, T.-A., Chen, S.-C., Tang, W., & Chen, B.-T. (2022). Internet of Things: Development Intelligent Programmable IoT Controller for Emerging Industry Applications. Sensors. https://doi.org/10.3390/s22145138
[6]Danuso, A., Giones, F., & da Silva, E. R. (2022). The digital transformation of industrial players. Business Horizons. https://doi.org/10.1016/j.bushor.2021.04.001
[7]de los Angeles Renteria del Toro, F., Hao, C., Tokuhiro, A., Gomez-Fernandez, M., & Gomez-Torres, A. (2024). Digitalization as an aggregate performance in the energy transition for nuclear industry. Nuclear Engineering and Technology. https://doi.org/10.1016/j.net.2023.11.030
[8]Deng, S., Zhang, J., Tao, L., Jiang, X., & Wang, F. (2023). LSBlocFL: A secure federated learning model combining blockchain and lightweight cryptographic solutions. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2023.108986
[9]Domínguez-Monferrer, C., Ramajo-Ballester, A., Armingol, J. M., & Cantero, J. L. (2024). Spot-checking machine learning algorithms for tool wear monitoring in automatic drilling operations in CFRP/Ti6Al4V/Al stacks in the aircraft industry. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2024.08.023
[10]Fawole, O. A., & Rawat, D. B. (2024). Recent Advances in 3D Object Detection for Self-Driving Vehicles: A Survey. AI. https://doi.org/10.3390/ai5030061
[11]Firouzi, R., & Rahmani, R. (2024). Delay-sensitive resource allocation for IoT systems in 5G O-RAN networks. Internet of Things. https://doi.org/10.1016/j.iot.2024.101131
[12]Gou, J., Sun, L., Yu, B., Wan, S., & Tao, D. (2023). Hierarchical Multi-Attention Transfer for Knowledge Distillation. ACM Transactions on Multimedia Computing Communications and Applications. https://doi.org/10.1145/3568679
[13]Jiang, B., Li, J., Wang, H., & Song, H. (2021). Privacy-Preserving Federated Learning for Industrial Edge Computing via Hybrid Differential Privacy and Adaptive Compression. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2021.3131175
[14]Khan, F., Kumar, R. L., Abidi, M. H., Kadry, S., Alkhalefah, H., & Aboudaif, M. K. (2022). Federated Split Learning Model for Industry 5.0: A Data Poisoning Defense for Edge Computing. Electronics. https://doi.org/10.3390/electronics11152393
[15]Ladj, A., Wang, Z., Meski, O., Belkadi, F., Ritou, M., & Cunha, C. D. (2021). A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2020.07.018
[16]Lee, C.-H., Liu, C.-L., Trappey, A. J. C., Mo, J. P. T., & Desouza, K. C. (2021). Understanding digital transformation in advanced manufacturing and engineering: A bibliometric analysis, topic modeling and research trend discovery. Advanced Engineering Informatics. https://doi.org/10.1016/j.aei.2021.101428
[17]Lee, E., Kwon, Y., Kim, C., Choi, W., & Sohn, H.-G. (2024). Multi-source point cloud registration for urban areas using a coarse-to-fine approach. GIScience & Remote Sensing. https://doi.org/10.1080/15481603.2024.2341557
[18]Lempinen, S., Kiesi, I., Nivanaho, N., & Seppänen, P. (2024). Digital Transformation and Discourses of Change Commodify Finnish Public Education. New Zealand Journal of Educational Studies. https://doi.org/10.1007/s40841-024-00357-z
[19]Li, D., Lai, J., Wang, R., Li, X., Vijayakumar, P., Gupta, B. B., & Alhalabi, W. (2023). Ubiquitous intelligent federated learning privacy-preserving scheme under edge computing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2023.03.010
[20]Li, F., Trappey, A. J. C., Lee, C.-H., & Li, L. (2022). Immersive technology-enabled digital transformation in transportation fields: A literature overview. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2022.117459
[21]Li, J., Yu, Z., Yu, L., Cheng, P., Chen, J., & Chi, C. (2023). A Comprehensive Survey on SAR ATR in Deep-Learning Era. Remote Sensing. https://doi.org/10.3390/rs15051454
[22]Liu, D., Kong, H., Luo, X., Liu, W., & Subramaniam, R. (2022). Bringing AI to edge: From deep learning’s perspective. Neurocomputing. https://doi.org/10.1016/j.neucom.2021.04.141
[23]Liu, G., Wang, C., Ma, X., & Yang, Y. (2021). Keep Your Data Locally: Federated-Learning-Based Data Privacy Preservation in Edge Computing. IEEE Network. https://doi.org/10.1109/mnet.011.2000215
[24]Lv, F., Wang, H., Pan, Z., Sun, R., Si, S., Zhang, W., Lv, S., & Sun, L. (2025). Asynchronous federated learning based zero trust architecture for the next generation industrial control systems. Computer Networks. https://doi.org/10.1016/j.comnet.2025.111459
[25]Ma, S., Ding, W., Liu, Y., Ren, S., & Yang, H. (2022). Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries. Applied Energy. https://doi.org/10.1016/j.apenergy.2022.119986
[26]Mhlanga, D., Denhere, V., & Moloi, T. (2022). COVID-19 and the Key Digital Transformation Lessons for Higher Education Institutions in South Africa. Education Sciences. https://doi.org/10.3390/educsci12070464
[27]Moreno-Cardenas, E., & Guijarro, L. (2023). Market and Sharing Alternatives for the Provision of Massive Machine-Type and Ultra-Reliable Low-Latency Communications Services over a 5G Network. Electronics. https://doi.org/10.3390/electronics12244994
[28]Mukul, E., & Büyüközkan, G. (2023). Digital transformation in education: A systematic review of education 4.0. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2023.122664
[29]Nain, G., Pattanaik, K. K., & Sharma, G. K. (2022). Towards edge computing in intelligent manufacturing: Past, present and future. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2022.01.010
[30]Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2021). Secure Computation Offloading in Blockchain Based IoT Networks With Deep Reinforcement Learning. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/tnse.2021.3106956
[31]Niu, S., Shao, H., Su, Y., & Wang, C. (2023). Efficient heterogeneous signcryption scheme based on Edge Computing for Industrial Internet of Things. Journal of Systems Architecture. https://doi.org/10.1016/j.sysarc.2023.102836
[32]Peng, Y., Hao, J., Chen, Y., & Gan, J. (2024). Fine-grained resource adjustment of edge server in cloud-edge collaborative environment. Cluster Computing. https://doi.org/10.1007/s10586-024-04380-z
[33]Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2020.3009103
[34]Ren, W.-Q., Qu, Y.-B., Dong, C., Jing, Y.-Q., Sun, H., Wu, Q.-H., & Guo, S. (2023). A Survey on Collaborative DNN Inference for Edge Intelligence. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1391-7
[35]Rocha, A., Monteiro, M., Mattos, C., Dias, M., Soares, J., Magalhães, R., & Macedo, J. (2024). Edge AI for Internet of Medical Things: A literature review. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2024.109202
[36]Rosendo, D., Costan, A., Valduriez, P., & Antoniu, G. (2022). Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2022.04.004
[37]Schmetz, A., & Kampker, A. (2024). Inside Production Data Science: Exploring the Main Tasks of Data Scientists in Production Environments. AI. https://doi.org/10.3390/ai5020043
[38]Shi, A., Wu, Q., Qin, X., Mao, Z., & Wu, M. (2024). Lightweight detector based on knowledge distillation for magnetic particle inspection of forgings. NDT & E International. https://doi.org/10.1016/j.ndteint.2024.103052
[39]Silvano, C., Ielmini, D., Ferrandi, F., Fiorin, L., Curzel, S., Benini, L., Conti, F., Garofalo, A., Zambelli, C., & Calore, E. (2025). A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms. ACM Computing Surveys. https://doi.org/10.1145/3729215
[40]Singh, S. K., Yang, L. T., & Park, J. H. (2023). FusionFedBlock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0. Information Fusion. https://doi.org/10.1016/j.inffus.2022.09.027
[41]Tekin, N., Aris, A., Acar, A., Uluagac, S., & Gungor, V. C. (2024). A review of on-device machine learning for IoT: An energy perspective. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2023.103348
[42]Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2019.09.022
[43]Visconti, P., Rausa, G., Del-Valle-Soto, C., Velázquez, R., Cafagna, D., & Fazio, R. D. (2024). Machine Learning and IoT-Based Solutions in Industrial Applications for Smart Manufacturing: A Critical Review. Future Internet. https://doi.org/10.3390/fi16110394
[44]Wang, C., Yang, K., Zhang, S., Huang, G., & Song, S. (2022). TC 3 KD: Knowledge distillation via teacher-student cooperative curriculum customization. Neurocomputing. https://doi.org/10.1016/j.neucom.2022.07.055
[45]Wang, C.-H., Huang, K.-Y., Yao, Y., Chen, J.-C., Shuai, H.-H., & Cheng, W.-H. (2022). Lightweight Deep Learning: An Overview. IEEE Consumer Electronics Magazine. https://doi.org/10.1109/mce.2022.3181759
[46]Wang, H., Cao, W., & Wang, F. (2022). Digital Transformation and Manufacturing Firm Performance: Evidence from China. Sustainability. https://doi.org/10.3390/su141610212
[47]Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning. https://doi.org/10.1016/j.lrp.2018.12.001
[48]White, G., Zink, A., Codecá, L., & Clarke, S. (2021). A digital twin smart city for citizen feedback. Cities. https://doi.org/10.1016/j.cities.2020.103064
[49]Wu, B., Xu, K., Li, Q., Ren, S., Liu, Z., & Zhang, Z. (2020). Toward Blockchain-Powered Trusted Collaborative Services for Edge-Centric Networks. IEEE Network. https://doi.org/10.1109/mnet.001.1900153
[50]Wu, H., Zhang, H., Cheng, J., Guo, J., & Chen, W. (2023). Perspectives on point cloud-based 3D scene modeling and XR presentation within the cloud-edge-client architecture. Visual Informatics. https://doi.org/10.1016/j.visinf.2023.06.0077
[51]Xie, H., Wang, Y., Ding, Y., Yang, C., Liang, H., & Qin, B. (2024). Industrial Wireless Internet Zero Trust Model: Zero Trust Meets Dynamic Federated Learning with Blockchain. IEEE Wireless Communications. https://doi.org/10.1109/mwc.001.2300368
[52]Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2021). Edge Intelligence: Empowering Intelligence to the Edge of Network. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2021.3119950
[53]Yan, D., & Li, G. (2023). A Heterogeneity Study on the Effect of Digital Education Technology on the Sustainability of Cognitive Ability for Middle School Students. Sustainability. https://doi.org/10.3390/su15032784
[54]Yang, D., Mahmood, A., Hassan, S. A., & Gidlund, M. (2022). Guest Editorial: Industrial IoT and Sensor Networks in 5G-and-Beyond Wireless Communication. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2022.3142149
[55]Zhang, H., Zhang, Z., & Chen, L. (2025). Toward zero trust in 5G Industrial Internet collaboration systems. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2024.03.011
[56]Zhang, J., Chen, X., Wei, H., & Zhang, K. (2024). A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation. Applied Energy. https://doi.org/10.1016/j.apenergy.2023.122184
[57]Zhu, H., Xue, X., Xu, M., & Kim, B.-G. (2024). Zero Trust Consumer IoT With Robust Federated Learning Over Main-Side Blockchain. IEEE Transactions on Consumer Electronics. https://doi.org/10.1109/tce.2024.3471921
[58]Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023). Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions. ACM Computing Surveys. https://doi.org/10.1145/3570953