Industrial Data Homogenization and Monitoring Scheme with Blockchain Oracles

Round 1

Reviewer 1 Report

The authors have done a very good job addressing the current research challenges in IIoT. The overall organization and presentation of the paper are good.

Blockchain technology, along with IPFS, is used to address the problem, which makes the contribution high.

The implementation details are also well explained, along with the result analysis.

Some minor comments like:

1. Is it essential to cite 60+ papers in this paper? Only use the required papers.

2. Give one comparative analysis with the existing work

3. The security analysis of the proposed code is not given.

The rest is look good for me.

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

This research is very interesting, but will be improved using a correct multivariable analysis, to this type of study as in: Shahinaz Kamal Ezzat, Yasmine N. M. Saleh, Ayman A. Abdel-Hamid:

Blockchain Oracles: State-of-the-Art and Research Directions. IEEE Access 10: 67551-67572 (2022)

Is very important describe better this case of study, to understand better a complexity of this research, as in:

https://www.springerprofessional.de/blockchain-model-implementation-to-select-the-best-bid-in-an-ind/19320294

In addition is important describe a Design of Experiments.

Explain with more detail the multivariable analysis to specify another type of implementations as in:

Michael Bartholic, Aron Laszka, Go Yamamoto, Eric William Burger:
A Taxonomy of Blockchain Oracles: The Truth Depends on the Question. ICBC 2022: 1-15

Determine adequate comparative with another similar problems in Industry 5.0

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

The paper presents a method for data homogenization and monitoring of manufacturing data in Industry 4.0 scenarios. There are the following key concerns with the paper:

1. It is not clear what are the key Industry 4.0 application scenarios that are targeted, and how the proposed method benefits such targeted scenarios. For instance, if Industry 4.0 application scenarios that have real-time requirements are among the target scenarios, it is not clear how the presented results relate to them. A high TPS alone does not guarantee real-time application performance

2. While it is claimed in section 5.1 that real-world IIoT data is used for building a prototype, section 5.3 suggests that only a simulation based study was conducted, and lacks any detail on the type of machine data used. It is difficult to assess the effectiveness of the proposed method without a clear discussion on manufacturing scenario used, and what are the challenges therein that may benefit from the proposed method.

3. The results will benefit from a comparison with similar techniques in literature, as well as a performance comparison with non-DLT systems.

Author Response

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Author Response File: Author Response.pdf

Reviewer 4 Report

The authors in this paper tackled a very interesting topic by providing an industrial raw data pre-processing and homogenization process using the technologies of decentralized blockchain oracles. However, several points need to be addressed for higher quality version:

1- The link between DAG-DLT and IPFS on Figure 1 is not clear, may be the authors need to think of linking them (on the figure).

2- The authors need to expand their explanation for Figure 4 as well as section 4.1 with more (one or two) real-life examples to clarify the steps and the sequence.

3- The assumption mentioned on page 12 "the monitoring system must have a secure-by-design zero trust approach ...." is a rough assumption and unclear.

4- The purpose of using cheap lightweight FPGA devices is not clear why.

5- The section/subsection below figure 6 is not clear, the authors should rewrite it as an algorithm (or flow-chart) to show the different steps involved.

 

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

The authors have addressed all the reviewer comments.