Review History


All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.

Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.

View examples of open peer review.

Summary

  • The initial submission of this article was received on September 4th, 2024 and was peer-reviewed by 2 reviewers and the Academic Editor.
  • The Academic Editor made their initial decision on October 22nd, 2024.
  • The first revision was submitted on January 15th, 2025 and was reviewed by the Academic Editor.
  • The article was Accepted by the Academic Editor on January 22nd, 2025.

Version 0.2 (accepted)

· Jan 22, 2025 · Academic Editor

Accept

Dear authors,

The previous feedback indicated only minor revisions were needed. Your revised version of the paper including a cover letter with clear replies to reviewers satisfied the standard of the journal. This version of the paper can be considered for publication.

[# PeerJ Staff Note - this decision was reviewed and approved by Yilun Shang, a 'PeerJ Computer Science' Section Editor covering this Section #]

Version 0.1 (original submission)

· Oct 22, 2024 · Academic Editor

Minor Revisions

Dear authors,

Your paper has been reviewed by two reviewers, and all of them required major revisions. Please correct the manuscript according to their suggestions, mark all changes, and provide a cover letter with replies to reviewers.

Reviewer 1 ·

Basic reporting

This research article presents a novel framework utilizing fuzzy linear programming. The peer selection problem is framed as a fuzzy linear programming task, with fuzzy logic employed to handle vagueness and imprecision in the decision-making process.

Experimental design

However, the paper has the following weaknesses:
1. The research motivation in the abstract is not clearly articulated, particularly the reasoning behind choosing the Fuzzy Linear Programming approach.
2. The literature review is insufficiently structured, and distinct topics such as the peer selection problem and fuzzy linear programming should be discussed separately.
3. Such as Granular computing-based decision models

Validity of the findings

4. The paper lacks a comprehensive pseudocode framework, and a time complexity analysis should be included.
5. Both the language quality and formatting require improvement.

Reviewer 2 ·

Basic reporting

- The manuscript is written in professional and scientifically accessible English, but there are several instances where the phrasing is unclear or awkward, leading to potential misinterpretation. For example, in the abstract, the phrase “with compared to traditional approaches” should be revised to “compared to traditional approaches.” The manuscript would benefit from a thorough proofreading to smoothen the language.

- The introduction provides sufficient context on Peer-to-Peer (P2P) networks and content distribution. However, more recent references could strengthen the background section, particularly regarding advancements and applications of fuzzy logic (e.g., a recent article "Comparison of fuzzy and crisp decision matrices: An evaluation on PROBID and sPROBID multi-criteria decision-making methods"), as well as linear programming in network optimization.

- Additionally, the literature review mentions various peer selection approaches but could expand on more contemporary alternatives to highlight the novelty of the proposed approach.

- Overall, I am generally satisfied with the basic reporting of this manuscript.

Experimental design

- The research question is well-defined and focuses on addressing the peer selection problem in P2P content distribution using FLP. The novelty lies in applying fuzzy logic to handle uncertainties in peer selection, which fills a knowledge gap in how dynamic network conditions are managed.

- The experimental design is sound, but some aspects of the methodology could be described in more detail. For example, the explanation of how the fuzzy decision variables are converted to crisp variables using alpha-cuts could be expanded to make it clearer for readers unfamiliar with this approach.

- Besides, while the use of SciPy for simulation is a valid choice, the manuscript should include a more detailed description of the specific SciPy functions or libraries used, as well as custom implementations (if any).

Validity of the findings

- The results appear to be statistically sound, with comparisons to traditional methods showing improvements in download speed, download time, and peer reliability. However, the statistical tests used to validate these improvements are not mentioned (e.g., confidence intervals, p-values or any other suitable metrics).

All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.