Trustworthy Digital Twins in the Industrial Internet of Things With Blockchain

Abstract

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 blockchainbased 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 datadriven 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.

Implementation of Cognitive Digital Twins in Connected and Agile Supply Networks—An Operational Model

“…This is a combination of all technologies, models and applicable regulations (e.g., GDPR) to ensure that data are generated, transmitted, and received among CDTs under a holistic trustworthy framework [31]. This includes data security, traceability, transparency, confidentiality and integrity.…”

Towards Trusted and Intelligent Cyber-Physical Systems: A Security-by-Design Approach

“…While stringent security guarantees are inherited from the blockchain, ensuring the trustworthiness of data-generating sources is equally important for critical infrastructures. Because the data is input to other entities (such as DTs, TI) to make decisions, ignoring such measures may lead to the insertion of false data into the system resulting in Garbage-In, Garbage-Out (GIGO) problem [11]. Therefore, three-fold Integrity Checking Mechanisms (ICMs) (engineering knowledge and domain knowledge) is established by: collecting data only from registered devices; (ii) cross-validating the device data against threshold values; and (iii) minimizing the Age of Information (AoI) based on time elapsed since the generation of latest status update received at destination.…”

“…Nevertheless, in real-life scenarios, data breaches could occur due to several reasons, both maliciously or mistakenly [2]. Therefore, curating and mining actionable insights from the collected data calls for a data storage infrastructure that can manifest the dissemination of trustworthy and secure data [11]. In this regard, provenance-enabled blockchain-based DTs can facilitate digital identity and data traceability from disparate data repositories while reasoning about the current state and the chained actions on a data object (such as who, when, where, and how), thereby ensuring trustworthy DTs throughout the product lifecycle [11].…”

“…Considering resourceconstrained IoT devices, deploying blockchain on fog or edge nodes to carry out resource-intensive tasks (such as running a consensus algorithm or executing smart contracts) can provide a trade-off between trust centralization and overhead. Additionally, we limit the frequent time-consuming access to the blockchain-based storage system by explicitly separating the real-time dynamic (or behavioral) data and the less dynamic (or static) data [11] due to time-sensitive tasks in CPSs. This strategy is adopted due to the fact that the real-time data evolves frequently with each lifecycle phase and usually amasses in intervals ranging from minutes to milliseconds, such as deployed sensors and actuators data providing asset's actual state, while static data changes infrequently with time along the lifecycle of the real-world counterpart, such as provenance data, system historical data, device configuration settings, access levels, and policies.…”

“…Moreover, provenance-enabled blockchain-based digital twins can aid in identifying the faulty node in the network. Thus, combining DTs and blockchain can reshape the CPSs where blockchain acts as a trustworthy data dissemination source to physical processes while DTs extract actionable insights for predictive maintenance and situational-awareness [11].…”