2025· International Journal of Modern Research in Science & Engineering· 0 citations
TL;DR
A secure blockchain-enabled IIoT architecture that integrates industrial sensing, edge computing, distributed ledger technology, cloud analytics, and intelligent decision-making is proposed that employs device authentication, encrypted communication, decentralized consensus, smart contracts, and machine learning-based anomaly detection.
Abstract
Industry 4.0 has accelerated the adoption of the Industrial Internet of Things (IIoT), enabling intelligent communication among industrial devices, edge systems, and cloud platforms for smart manufacturing. However, conventional centralized security approaches are increasingly vulnerable to cyber threats, data tampering, and single-point failures. This paper proposes a secure blockchain-enabled IIoT architecture that integrates industrial sensing, edge computing, distributed ledger technology, cloud analytics, and intelligent decision-making. The framework employs device authentication, encrypted communication, decentralized consensus, smart contracts, and machine learning-based anomaly detection to enhance data integrity, secure information sharing, and cyber resilience. Experimental evaluation demonstrates improvements in communication security, authentication accuracy, transparency, scalability, latency, and throughput, making the proposed architecture a robust and scalable solution for secure next-generation smart engineering and industrial automation.
The growth of the Internet of Things (IoT) has introduced significant security challenges, mainly due to the resource constraints of devices and the limitations of centralized architectures. This paper proposes a blockchain-based Zero-Trust framework for secure and scalable IoT systems. The approach is architecture-agnostic and combines decentralized identity management, hybrid data storage, and edge-assisted computation. To optimize resource usage, raw data are stored off-chain while cryptographic hashes are anchored on the blockchain, ensuring integrity and immutability. A Merkle tree structure is employed to aggregate data efficiently, reducing communication overhead and blockchain transaction costs. Experimental results demonstrate that lightweight cryptographic mechanisms, combined with Merkle-based aggregation, provide strong security guarantees with low energy consumption. The proposed framework achieves improved scalability, robustness, and efficiency, making it suitable for resource-constrained IoT environments.
Florian Bonelli, Alexandre dos Santos Roque, E. P. de Freitas· International Conference on...· 0 citations
Overall, this review demonstrates that blockchain-based cybersecurity frameworks provide a secure, transparent, and resilient foundation for protecting smart digital environments against increasingly sophisticated cyber threats while supporting trustworthy and scalable digital transformation.
M. Kayla, Crispinus Ode, Marion Sanaipei· The Eastasouth Journal of In...· 0 citations
Industrial robotics has become one of the fundamental pillars of Industry 4.0 and the emerging Industry 5.0 paradigm, where intelligent automation, collaborative robots, and cyber-physical production systems continuously exchange large volumes of operational data through Industrial Internet of Things (IIoT) networks. While IoT-enabled robotic platforms significantly improve manufacturing efficiency, predictive maintenance, remote monitoring, and autonomous decision-making, they simultaneously introduce substantial cybersecurity risks arising from heterogeneous communication protocols, distributed edge devices, and cloud-based control infrastructures. Traditional industrial communication architectures primarily focused on reliability and deterministic performance, often overlooking advanced security mechanisms capable of defending against sophisticated cyberattacks such as spoofing, replay attacks, distributed denial-of-service (DDoS), ransomware, and unauthorized robotic command injection. This study proposes a secure IoT communication framework specifically designed for industrial robotic environments by integrating lightweight encryption, blockchain-assisted device authentication, edge-based intrusion detection, artificial intelligence-enabled anomaly detection, and secure MQTT/OPC UA communication protocols. The proposed architecture enhances confidentiality, integrity, authentication, availability, and real-time communication while minimizing computational overhead. Comparative analysis demonstrates improvements in communication latency, packet delivery ratio, authentication accuracy, intrusion detection rate, and network resilience when compared with conventional industrial IoT security mechanisms. The proposed framework provides a scalable and intelligent cybersecurity solution suitable for autonomous manufacturing, collaborative robotics, smart factories, and Industry 5.0 applications where secure machine-to-machine communication is critical.
Ritu Agarwal· International Journal of Int...· 0 citations
The smart grid systems are increasingly becoming digital where the system is taking a new direction into greater operational efficiency and real time energy management. The increased number of Internet of Things (IoT) devices and communication networks has widened the area of attack as well, thus smart grids are vulnerable to more sophisticated cyber attacks like false data injection, denial-of-data manipulation and service attacks. The traditional centralized security controls are not sufficient because of the limitation of single point failure, scalability limitations, and demand decentralized and resilient security controls. The use of blockchain technology in this regard has become a potential solution in order to improve the level of security by utilizing the transparent, decentralized, and immutable features of the technology. To provide cybersecurity approaches of smart grid systems, a taxonomical structure of blockchain technology is proposed in this paper. Besides that it shows the categorization of the various techniques into secure data management, secure energy trading, device authentication, data integrity assurance and communication protection. A detailed analysis of existing works was undertaken to determine their efficiency based on the security, scale and complexity of implementation. The results of the review describe some of the major challenges, such as latency and energy overhead and provide future research opportunities on effective smart grid security frameworks.
F. Basheer, Hari Gobind Pathak, Meena Malik et al.· International Conference on...· 0 citations
The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs.
Marbiyat Tahir Gidado, Bashiru Abdulganiyu, Mohammed Nasir Musa et al.· Journal of Advanced Science...· 0 citations
A blockchain-enabled cybersecurity framework for ITS that integrates distributed ledger technology with secure vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-everything communication is proposed that incorporates identity management, consensus-based validation, trust evaluation, secure data sharing, intrusion detection, and privacy-preserving mechanisms.
Rashmi Soni· Journal of Intelligent Decis...· 0 citations
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