A novel blockchain federated safety-as-a-service scheme for industrial IoT using machine learning

【Author】 Hasan, Nabeela; Chaudhary, Kiran; Alam, Mansaf

【Source】MULTIMEDIA TOOLS AND APPLICATIONS

【影响因子】2.577

【Abstract】Blockchains are costly in terms of computing and involve high overhead bandwidth and delays that are not suitable for smart appliances. Enhancing the precision of output, quality, and delivery of data is particularly critical in Machine Learning. The combination of Machine Learning and Blockchain technologies may create accurate results. The Industrial IoT (IIoT), has quickly been established and is getting huge attention in educational areas and manufacturing, but IoT solitude danger and privacy exposures are developing by lack of important security technology. Because blockchain technique's regionalization and information revelation were planned as a decentralized and distributed method to give assurance security and motivate the development of the IoT and IIoT. The Blockchain Driven Cyber-Physical system (BDCPS) is supported by IoT and cloud services. BDCPS will confirm the statement utilizing the Intelligent Agreements functionality and the trust-less peer-to-peer centrally controlled database showcase by a tiny-scale real-life Blockchain to the IoT system. In this study, a private Blockchain can be run on a separate board system and paralleled to a microcontroller with Smart devices. The suggested system uses blockchain technology to resolve issues such as lightweight, evaporation, warehousing transactions, and shipment time. The data flow of Blockchain is intended to demonstrate the application of machine learning to food traceability. Finally, to extend shelf life, a supply chain employs dependable and accurate data. This paper shows a relevant blockchain and machine learning research that identifies numerous key elements of combining the two technologies such as Blockchain and Machine Learning, including an overview, benefits, and applications.

【Keywords】Blockchain; IoT; Security of information; Machine learning; Blockchain technology; Cloud storage; Industry 4; 0

【发表时间】

【收录时间】2022-08-28

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-物联网