Abstract
The Internet of Things (IoT) is a remarkable data producer and these data may be used to prevent or detect security vulnerabilities and increase productivity by the adoption of statistical and Artificial Intelligence (AI) techniques. However, these desirable benefits are gained if data from IoT networks are dependable—this is where blockchain comes into play. In fact, through blockchain, critical IoT data may be trusted, i.e., considered valid for any subsequent processing. A simple formal model named “the Mirror Model” is proposed to connect IoT data organized in traditional models to assets of trust in a blockchain. The Mirror Model sets some formal conditions to produce trusted data that remain trusted over time. A possible practical implementation of an application programming interface (API) is proposed, which keeps the data and the trust model in synch. Finally, it is noted that the Mirror Model enforces a top-down approach from reality to implementation instead of going the opposite way as it is now the practice when referring to blockchain and the IoT.
Highlights
One subtle aspect of the alert recently raised by Dr Geneveva Allen about a “science crisis” [1] is the observation that current machine learning algorithms may discover patterns in data that exist only in data but not in the real world
The infrastructure network of devices that forms the base of any Internet of Things (IoT) system is a remarkable data producer and Artificial Intelligence (AI) is aimed at discovering patterns in these data that, in turn, may provide hints for detecting or preventing security breaches or provide working optimization or reliability
The article is organized as follows: In Section 2 we present the state of the art related to the trust of data and the contribution of blockchain in the IoT domain
Summary
One subtle aspect of the alert recently raised by Dr Geneveva Allen about a “science crisis” [1] is the observation that current machine learning algorithms may discover patterns in data that exist only in data but not in the real world This is true of data obtained by external sources where a validation process is barely exposed or accomplished. The infrastructure network of devices that forms the base of any IoT system is a remarkable data producer and AI is aimed at discovering patterns in these data that, in turn, may provide hints for detecting or preventing security breaches or provide working optimization or reliability (e.g., detecting those devices that are not working properly).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
