The Blockchain Framework for the Sharing of Data between Training Providers Based on the Internet of Things

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Abstract

Blockchain technology is recognised as being a key focus when it comes to modern-day studies and interest on a global scale, with the new block of transactions responsible for the continued growth and expansion of a distributed ledger that details transactional data, complete with immutable and verifiable structures. A greater degree of transparency, privacy, security, and traceability is provided by blockchain when a comparison is drawn with more conventional strategies. As a result of its secure and more progressive elements, blockchain is applied in a number of different arenas, including digital transactions, education, the healthcare industry, the Internet of Things (IoT), trade finance, and the training sector. Importantly, a notable effect on all applied fields has been witnessed as a result of blockchain technology, with data and transactional reliability, as well as privacy, recognised as the most important aspects when it comes to data-sharing in the training arena specifically. Very few models and frameworks are aligned with the guidelines put forth in this sector; as such, this report seeks to consider the pressing need to determine a model and framework to facilitate the sharing of data between training providers in line with the IoT. This study, therefore, centres on the creation of a blockchain model, with the creation of a sole recognition system for professionals seen to be advantageous for all such providers, specifically in the mind of providing a greater degree of clarity and simplification to the system in line with the IoT. The suggested model is focused on a number of different elements that have been identified as a result of studied theories, with consideration of the three different standpoints and professional assessments of 16 unique items.

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CITATION STYLE

APA

Jeza Alotaibi, S. (2022). The Blockchain Framework for the Sharing of Data between Training Providers Based on the Internet of Things. Scientific Programming, 2022. https://doi.org/10.1155/2022/3944200

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