  Industrial processes rely on sensory data for decision-making processes, risk
assessment, and performance evaluation. Extracting actionable insights from the
collected data calls for an infrastructure that can ensure the dissemination of
trustworthy data. For the physical data to be trustworthy, it needs to be
cross-validated through multiple sensor sources with overlapping fields of
view. Cross-validated data can then be stored on the blockchain, to maintain
its integrity and trustworthiness. Once trustworthy data is recorded on the
blockchain, product lifecycle events can be fed into data-driven systems for
process monitoring, diagnostics, and optimized control. In this regard, Digital
Twins (DTs) can be leveraged to draw intelligent conclusions from data by
identifying the faults and recommending precautionary measures ahead of
critical events. Empowering DTs with blockchain in industrial use-cases targets
key challenges of disparate data repositories, untrustworthy data
dissemination, and the need for predictive maintenance. In this survey, while
highlighting the key benefits of using blockchain-based DTs, we present a
comprehensive review of the state-of-the-art research results for
blockchain-based DTs. Based on the current research trends, we discuss a
trustworthy blockchain-based DTs framework. We highlight the role of Artificial
Intelligence (AI) in blockchain-based DTs. Furthermore, we discuss current and
future research and deployment challenges of blockchain-supported DTs that
require further investigation.
