Industrial Data Homogenization and Monitoring Scheme with Blockchain Oracles - Denis Stefanescu; Patxi Galán-García; Leticia Montalvillo; Juanjo Unzilla; Aitor Urbieta (Smart Cities, 2023)
Research efforts on Distributed Ledger Technologies (DLTs) for industrial applications have constantly been increasing over the last years. The use of DLTs in the Industry 4.0 paradigm provides traceability, integrity, and immutability of the generated industrial data. However, Industry 4.0 ecosystems are typically composed of multiple smart factory clusters belonging to several companies, which are immersed in constant interaction with other business partners, clients, or suppliers. In such complex ecosystems, multiple DLTs are necessarily employed to maintain the integrity of the data throughout the whole process, from when the data is generated until it is processed at higher levels. Moreover, industrial data is commonly heterogeneous, which causes compatibility issues, along with security and efficiency issues in the homogenization process. Thus, the data needs to be pre-processed and homogenized in a secure manner before being exploited. Consequently, in this work, we address the issues mentioned above by providing an industrial raw data pre-processing and homogenization process according to a standard data model. We employ decentralized blockchain oracles to guarantee the integrity of the external data during the homogenization process. Hereafter, we design an interoperable plant blockchain for trustworthy storage and processing of the resulting homogenized data across several industrial plants. We also present a prototype implementation of the aforementioned scheme and discuss its effectiveness. Finally, we design a monitoring scheme to overview the usage the performance of the architecture processes and identify possible performance and security issues.