Designing an Experimental Setup for Data Provenance Tracking using a Public Blockchain: A Case Study using a Water Bottling Plant

Abstract: Data provenance, in an end-to-end supply chain context, refers to the tracking of the origin and history of every raw material, process, packaging and distribution involved in a manufacturing network. The traditional client-server architecture utilised in centralised systems, stores data in a single location, making it vulnerable to single points of failure, data tampering, and unau-thorised access. As a result, a lack of data provenance and standardisation for products in a manufacturing supply chain. This leads to a lack of traceability and transparency. Therefore, this article presents a hypothesis that these challenges can be overcome by incorporating data provenance into blockchain-based smart contracts for traceability and transparency. This article is structured such that it firstly discusses data prove-nance traceability with a focus on the cloud-based storage sys-tem architecture domains for data provenance traceability across end-to-end supply chains. Secondly, this article sheds more light on the design of an experimental setup for block-chain-based data provenance traceability in a manufacturing supply chain using a case study of a water bottling plant. Final-ly, it showcases and discusses the results of the experiments for this purpose.

O. L. Mokalusi, R. B. Kuriakose and H. J. Vermaak. “Designing an Experimental Setup for Data Provenance Tracking using a Public Blockchain: A Case Study using a Water Bottling Plant”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150630

@article{Mokalusi2024,
title = {Designing an Experimental Setup for Data Provenance Tracking using a Public Blockchain: A Case Study using a Water Bottling Plant},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150630},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150630},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {O. L. Mokalusi and R. B. Kuriakose and H. J. Vermaak}
}