Hybrid Blockchain Architectures for Securing Industrial IoT Data

Industrial Internet of Things (IIoT) has grown tremendously to transform industries with real-time sensing, predictive repairs, and decision-making with insights across manufacturing, logistics, energy, and healthcare. Adoption of massive networks of connected devices, however, creates serious concerns related to security, privacy, and trust. Traditional centralized security systems suffer from issues of single points of failure, low scalability, and operational costs that are too high. Blockchain technology has come to offer a tenable substitute with its inherent properties of decentralization, immutability, and openness of ledgers. In its single versions of either public, private, or consortium ones, however, blockchain schemes suffer from imperfections when reassailed for deployment in heterogeneous and resource-scarce IIoT space. The article creates hybrid blockchain architectures that bring together public and private blockchain advantages to solve these issues. Public chains ensure auditability and trust with external partners while private chains ensure efficiency and confidentiality of information for industrial processes. The article overviews existing techniques, describes key building blocks of architectures such as consensus models, smart contracts, and cross-chain messaging, and shows hybrid models’ benefits in solving IIoT-specific requirements. Besides, it covers applied issues of interoperability, governance, and adoption, while future research directions in such areas of lightweight consensus, AI-aided anomaly detection, and standards for interoperability are represented. The conclusion is that hybrid blockchain is a strong direction to securing IIoT environments with scalability, privacy, and openness in balance.

Industrial IoT (IIoT) hybrid blockchain designs are only just starting to materialize, and most of these prospective future directions show tremendous potential. Following that list is the building of lightweight consensus algorithms that are resource-optimized for IIoT devices that are resource-constrained. In their nature, traditional algorithms, while being secure, are usually power- and compute-thirsty. In preparation for their futures, new technologies must focus on low-power protocols that balance efficiency with speed. Another direction of studies lies in integrating artificial intelligence (AI) and machine learning (ML) with hybrid blockchain networks. Predictive analytics and real-time AI-powered anomaly detection can improve IIoT security by identifying malicious activity or suspicious flows of data in real time, while blockchain offers tamper-proof records of such events. Standards for interoperability are also a major area of future study. Establishing world-recognized standards for cross-chain communications can simplify adoption and ensure consistency across multiple industrial settings. Moreover, developing simulation settings and testbeds for hybrid blockchain for IIoT will enable researchers and practitioners to experiment with system performance prior to deployment in real settings. In sum, hybrid blockchain architectures hold immense potential in addressing both issues of system openness and information secrecy in IIoT. Combining operational effectiveness with private chains and accountability with public chains, hybrid systems offer a balanced approach to securing industrial data. Although adoption, governance, and interoperability concern still persist, ongoing advancements with blockchain, industrial automation, and artificial intelligence are laying down foundations for scalable, resilient, and reliable IIoT systems. Hybrid blockchain can thus be regarded not only as a security upgrade, but also a strategic enabler of Industry 4.0 and its successors.

1. Malik PK, Sharma R, Singh R, et al (2021) Industrial Internet of Things and its Applications in Industry 4.0: State of The Art. Comput Commun 166:. https://doi.org/10.1016/j.comcom.2020.11.016 2. Sarker IH (2023) Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects. Annals of Data Science 10 3. Lin SY, Zhang L, Li J, et al (2022) A survey of application research based on blockchain smart contract. Wireless Networks 28:. https://doi.org/10.1007/s11276-021-02874-x 4. Akash Abaji Kadam, Tejaskumar Vaidya, & Subba rao katragadda. (2025). Digital Transformation of Supply Chain Quality Management: Integrating AI, IoT, Blockchain, and Big Data. Journal of Economics, Finance and Accounting Studies , 7(3), 41-49. https://doi.org/10.32996/jefas.2025.7.3.5 5. Kadam AA, Thaker H, Gundeti R, et al (2025) A Comprehensive Review on Digital Twin Integration in Smart Manufacturing Technologies, Challenges, and Future Trends. Journal of Artificial Intelligence & Cloud Computing 1–10. https://doi.org/10.47363/JAICC/2025(4)470 6. Mamodiya, U., Kishor, I., Garine, R. et al. Artificial intelligence based hybrid solar energy systems with smart materials and adaptive photovoltaics for sustainable power generation. Sci Rep 15, 17370 (2025). https://doi.org/10.1038/s41598-025-01788-4 7. Khraisat A, Alazab A (2021) A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cybersecurity 4:. https://doi.org/10.1186/s42400-021-00077-7 8. Xu M, Chen X, Kou G (2019) A systematic review of blockchain. Financial Innovation 5 9. Bak O, Braganza A, Chen W (2025) Exploring blockchain implementation challenges in the context of healthcare supply chain (HCSC). Int J Prod Res 63:. https://doi.org/10.1080/00207543.2023.2286491 10. Balto KE, Yamin MM, Shalaginov A, Katt B (2023) Hybrid IoT Cyber Range. Sensors 23:. https://doi.org/10.3390/s23063071 11. Sedlmeir J, Lautenschlager J, Fridgen G, Urbach N (2022) The transparency challenge of blockchain in organizations. Electronic Markets 32:. https://doi.org/10.1007/s12525-022-00536-0 12. Akash Abaji Kadam, Ramakrishna Garine, Supriya Akash Kadam (2024) Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0. World Journal of Advanced Research and Reviews 24:477–488. https://doi.org/10.30574/wjarr.2024.24.1.3035 13. Kaur S, Chaturvedi S, Sharma A, Kar J (2021) A Research Survey on Applications of Consensus Protocols in Blockchain. Security and Communication Networks 2021 14.Himeur Y, Elnour M, Fadli F, et al (2023) AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives. Artif Intell Rev 56:. https://doi.org/10.1007/s10462-022-10286-2 15.Anthony Jnr B (2024) Enhancing blockchain interoperability and intraoperability capabilities in collaborative enterprise-a standardized architecture perspective. Enterp Inf Syst 18:. https://doi.org/10.1080/17517575.2023.2296647 16.Fan X, Chai Q, Xu L, Guo D (2020) DIAM-IoT: A decentralized identity and access management framework for internet of things. In: BSCI 2020 – Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, Co-located with AsiaCCS 2020 17. Fang J, Feng T, Guo X, et al (2024) Blockchain-cloud privacy-enhanced distributed industrial data trading based on verifiable credentials. Journal of Cloud Computing 13:. https://doi.org/10.1186/s13677-023-00530-7

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