Trustworthy Digital Twins in the Industrial Internet of Things with Blockchain
This work addresses data trustworthiness and security challenges for industrial systems, but it appears incremental as it combines existing technologies like blockchain and Digital Twins without introducing a fundamentally new approach.
The paper tackles the problem of ensuring trustworthy data for decision-making in industrial processes by proposing a blockchain-based framework integrated with Digital Twins to address data management and security issues in the Industrial Internet of Things, enabling fault diagnosis and precautionary measures.
Industrial processes rely on sensory data for critical decision-making processes. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the trustworthiness of data. To this end, we envision a blockchain-based framework for the Industrial Internet of Things (IIoT) to address the issues of data management and security. Once the data collected from trustworthy sources are recorded in the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, we leverage Digital Twins (DTs) that can draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Furthermore, we discuss the integration of DTs and blockchain to target key challenges of disparate data repositories, untrustworthy data dissemination, and fault diagnosis. Finally, we identify outstanding challenges faced by the IIoT and future research directions while leveraging blockchain and DTs.