TADDA-4i: A Scalable and Secure Tangle-Assisted Decentralized Framework for Industrial Analytics in Industry 4.0

International Journal of Electronics and Communication Engineering
© 2026 by SSRG - IJECE Journal
Volume 13 Issue 2
Year of Publication : 2026
Authors : Milton Samadder, Anup Kumar Barman, Shiladitya Munshi, Utpal Madhu
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How to Cite?

Milton Samadder, Anup Kumar Barman, Shiladitya Munshi, Utpal Madhu, "TADDA-4i: A Scalable and Secure Tangle-Assisted Decentralized Framework for Industrial Analytics in Industry 4.0," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 2, pp. 40-52, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I2P104

Abstract:

Exponentially growing data generated by networked devices in Industry 4.0 environments requires industrial analytics that are secure, scalable, and decentralized. This article proposes TADDA-4i, a new multi-layered architecture based on IOTA's Tangle-Directed Acyclic Graph (DAG)-based Distributed Ledger Technology (DLT)-combined with federated learning and edge computing to provide real-time, secure, reliable, and self-sovereign industrial analytics. The architecture minimizes centralized bottlenecks via feeless, asynchronous data validation and tamper-evident model update verification using the Tangle ledger. Adaptive Tip-Aware Data Prioritization (ATDP) and Tangle-Validated Federated Aggregation (TVFA) are two new algorithms proposed for improving responsiveness and securing federated learning integrity. Experimental evaluation in emulated industrial edge environments showed that transactions take 30 percent less time, almost all of the misbehaving updates are detected, the model is about 10 percent more accurate, and output is not reduced even if the number of devices reaches 50. These findings make TADDA-4i an executable solution for the future generations of decentralized industrial intelligence.

Keywords:

Directed Acyclic Graph (DAG), Distributed Ledger Technology (DLT), Internet of Things (IoT), Internet of Things Application (IOTA), Tangle.

References:

[1] Feng Zhang et al., “An IoT-Based Online Monitoring System for Continuous Steel Casting,” IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1355-1363, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Laha Ale et al., “D3PG: Dirichlet DDPG for Task Partitioning and Offloading with Constrained Hybrid Action Space in Mobile-Edge Computing,” IEEE Internet of Things Journal, vol. 9, no. 19, pp. 19260-19272, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Friedrich Volz et al., “On the Role of Digital Twins in Data Spaces,” Sensors, vol. 23, no. 17, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Lenny Koh, Guido Orzes, and Fu (Jeff) Jia, “The Fourth Industrial Revolution (Industry 4.0): Technologies Disruption on Operations and Supply Chain Management,” International Journal of Operations & Production Management, vol. 39, no. 6-7-8, pp. 817-828, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Joana Duarte, Fernanda Rodrigues, and Jacqueline Castelo Branco, “Sensing Technology Applications in the Mining Industry—A Systematic Review,” International Journal of Environmental Research and Public Health, vol. 19, no. 4, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Orhan Can Görür, Xin Yu, and Fikret Sivrikaya, “Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process,” Applied Sciences, vol. 11, no. 11, pp. 1-24, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Abdul Wahid, John G. Breslin, and Muhammad Ali Intizar, “Prediction of Machine Failure in Industry 4.0: A hybrid CNN-LSTM Framework,” Applied Sciences, vol. 12, no. 9, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Sung Wook Kim et al., “Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review,” International Journal of Precision Engineering and Manufacturing, vol. 23, pp. 111-129, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ye Yuan et al., “A General End-to-End Diagnosis Framework for Manufacturing Systems,” National Science Review, vol. 7, no. 2, pp. 418-429, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Heiner Lasi et al., “Industry 4.0,” Business & Information Systems Engineering, vol. 6, pp. 239-242, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jayavardhana Gubbi et al., “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Maribel Yasmina Santos et al., “A Big Data Analytics Architecture for Industry 4.0,” World Conference on Information Systems and Technologies, Madeira, Portugal, pp. 175-184, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hana Pačaiová et al., “Principles of Management and Position of Maintenance in the I4.0 Environment,” Acta Mechanica Slovaca, vol. 25, no. 1, pp. 14-19, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Sri Nikhil Gupta Gourisetti et al., “Standardization of the Distributed Ledger Technology Cybersecurity Stack for Power and Energy Applications,” Sustainable Energy, Grids and Networks, vol. 28, pp. 1-23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Bokolo Anthony Jnr. et al., “A Framework for Standardization of Distributed Ledger Technologies for Interoperable Data Integration and Alignment in Sustainable Smart Cities,” Journal of the Knowledge Economy, vol. 15, no. 3, pp. 12053-12096, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Lam Duc Nguyen et al., “Analysis of Distributed Ledger Technologies for Industrial Manufacturing,” Scientific Reports, vol. 12, no. 1, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Reza Soltani et al., “Distributed Ledger Technologies and Their Applications: A Review,” Applied Sciences, vol. 12, no. 15, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Hussein Hellani et al., “Tangle the Blockchain: Toward IOTA and Blockchain Integration for IoT Environment,” Conference Proceedings of 19th International Conference on Hybrid Intelligent Systems, Bhopal, India, pp. 429-440, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Khikmatullo Tulkinbekov, and Deok-Hwan Kim, “Deletion-based Tangle Architecture for Edge Computing,” Electronics, vol. 11, no. 21, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Houssein Hellani et al., “Computing Resource Allocation Scheme for DAG-based IOTA Nodes,” Sensors, vol. 21, no. 14, pp. 1-18, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Hongwei Zhang et al., “A Novel Distributed Ledger Technology Structure for Wireless Sensor Networks based on IOTA Tangle,” Electronics, vol. 11, no. 15, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Changlin Yang et al., “A Novel Two-Layer DAG-based Reactive Protocol For IoT Data Reliability in the Metaverse,” 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS), Hong Kong, pp. 25-36, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Xiaofeng Xue, Haokun Mao, and Qiong Li, “DAG-ACFL: Asynchronous Clustered Federated Learning Based on DAG-DLT,” arXiv preprint, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Xing-Shuo Song et al., “A Markov Process Theory for Network Growth Processes of DAG-based Blockchain Systems,” arXiv preprint, pp. 1-49, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Misbah Khan, Frank den Hartog, and Jiankun Hu, “Toward Verification of DAG-Based Distributed Ledger Technologies through Discrete-Event Simulation,” Sensors, vol. 24, no. 5, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Dzmitry Kliazovich et al., “CA-DAG: Modeling Communication-Aware Applications for Scheduling in Cloud Computing,” Journal of Grid Computing, vol. 14, pp. 23-39, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Mohd Javaid et al., “Blockchain Technology Applications for Industry 4.0: A Literature-based Review,” Blockchain Research and Applications, vol. 2, no. 4, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Sinan Kahveci et al., “An End-to-End Big Data Analytics Platform for IoT-Enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles,” Journal of Manufacturing Systems, vol. 63, pp. 214-223, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] NASA C-MAPSS-1 Turbofan Engine Degradation Dataset, Kaggle. [Online]. Available: https://www.kaggle.com/datasets/bishals098/nasa-turbofan-engine-degradation-simulation
[30] Gas Sensor Array Drift Dataset, UC Irivine Machine Learning Repository. [Online]. Available: https://archive.ics.uci.edu/dataset/224/gas+sensor+array+drift+dataset
[31] Awesome Industrial Datasets, GitHub. [Online]. Available: https://github.com/jonathanwvd/awesome-industrial-datasets