Blockchain–AI Convergence Framework for Secure and Intelligent Data Analysis in Smart Manufacturing IIoT Networks
Abstract
The Industrial Internet of Things (IIoT) poised at the frontier of innovative automation in industrial systems via the interconnected infrastructure of smart sensors and devices is indeed a game changer. With untold volumes of data generated by IIoT systems, the data presents a myriad of opportunities to drive AI-based solutions for proactive maintenance, fault identification, and optimization of operational processes, among other solutions. Unfortunately, most centralized data architectures leave to a myriad of issues, one of which is loss of trust in the AI-driven decision processes. This paper presents a Blockchain-AI Convergence Framework to address the issues of trust, security, and the intelligent processing of data, specifically for the smart manufacturing HoT systems. Blockchain technology combined with smart contracts and distributed ledger technology, provides a way to guarantee tamper-proof integrity of data in an IoT system, while at the same time, allows for trust based AI to perform the tasks of fault diagnosis, equipment failure prognosis, and optimal operation of a manufacturing system. Edge technology is incorporated to further improve the latency issues for real-time processing and the advanced automation of industrial operations. The framework also addresses the needs of the industrial operatives for secure reliable data and most importantly, improves the trust and integrity of data for AI-based predictions. The proposed system can be validated using industrial data sets available in the public domain like NASA Data Set, and the CWRU Bearing Data Set. Assessment of the proposed system can be done using metrics like accuracy, precision, recall, F1 measure, latency of detection, blockchain transactions, throughput of the system, and storage cost. Combining blockchain and AI is likely to create the first secure, scalable, and efficient framework for IIoT networks, bringing the ability to conduct predictive maintenance with minimal downtime and maximizing industrial efficiency.