kumoh national institute of technology
Networked Systems Lab.

Sanjay Bhardwaj and Dong-Seong Kim," Dragonfly-Based Swarm System Model for Node Indetification in URLLC", Neural Comuting and Application, 2020
By : sanjay
Date : 2020-04-06
Views : 820

Dear Dr. Kim,

We have received the reports from our advisors on your manuscript, "Dragonfly-Based Swarm System Model for Node Identification in Ultra-reliable Low-latency Communication", which you submitted to Neural Computing and Applications.

Based on the advice received, the Editor feels that your manuscript could be accepted for publication should you be prepared to incorporate the revisions recommended by the reviewers. When preparing your revised manuscript, you are asked to carefully consider the reviewer comments which are attached, and submit a list of responses to the comments. Your list of responses should be uploaded as a file in addition to your revised manuscript.

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We look forward to receiving your revised manuscript before 04 Jun 2020.

Best regards,
Haijun Zhang
Associate Editor
Neural Computing and Applications


Reviewer #1: Dear Authors,

In the manuscript, Dragonfly Node Identification Algorithm is used for Node Identification in Ultra-reliable Lowlatency Communication. The paper is well-structured and well-written. But, some comments should be addressed to improve the quality of the paper.

1. Proper reference should be provided fopr the benchmark problems selected in the paper and it should be described the reason of choosing this set of benchmark problems.
2. The literature should be modified to show the impact of recent metaheuristic optimization algorithms such as: A novel random walk grey wolf optimizer, A hybrid self-adaptive sine cosine algorithm with opposition based learning, Improved sine cosine algorithm with crossover scheme for global optimization, A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons, Cauchy Grey Wolf Optimiser for continuous optimisation problems, Harmonized salp chain-built optimization.
3. It will be better to represent the results on benchmark for only two decimal places.
4. A statistical test should be done in the paper to represent the significance improvement in the results.
5. Why, only some selected test problems are reported in the paper of CEC 2014. Justify?
6. Please indicate the motivation and major contribution of the paper in terms of aims at introduction and in terms of results in the conclusion.

Reviewer #2: Dragonfly-Based Swarm System Model is implemented for Node Identification to get the reliable and low-latency communication. Experimental results show the usefulness of the proposed scheme. I recommend the acceptance of this paper after minor revision.
1. The authors need describle clearly the fitness function and the parameters for the optimization in this paper.
2. This paper mainly formulate the optimization in the cluster of the sensor nodes, some papers related to the clustering of the sensor nodes, the authors may introduce such as the following.
2.1 Trong-The Nguyen, Jeng-Shyang Pan and Thi-Kien Dao, "A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks", Appl. Sci. 2019, 9(10), 1973; https://doi.org/10.3390/app9101973
2.2 Jeng-Shyang Pan, Lingping Kong, Tien-Wen Sung, Pei-Wei Tsai, Václav Snášel, "A Clustering Scheme for Wireless Sensor Networks Based on Genetic Algorithm and Dominating Set," Journal of Internet Technology, vol. 19, no. 4 , pp. 1111-1118, Jul. 2018.
3. Some figures are not very clear such as Fig. 14, the auythors may try to enlarge some parts of the figure.

Reviewer #3: This paper investigated an optimization method for Node Identification in Ultra-reliable Low-latency Communication. The idea sounds interesting. In general, the paper is well-written and -organized. The results look strong and convincing. To further improve the paper standard, I only have some minor comments:

1) "[7] proposed a resource..." this is not a good way to cite a reference. You can use the first author's surname to indicate the paper. Please check other places and correct this.

2) in related work, you may some subsections to clearly review the existing works and highlight the features of your method together with the main difference with other extant methods. Here, for topology optimization, this work may be discussed and deserve a cite: Nature-Inspired Self-Organization, Control and Optimization in Heterogeneous Wireless Networks. IEEE Transactions on Mobile Computing, 2012, vol.11, no. 7, pp. 1207-1222.

3) You give many results which look convincing. For clarity and for the benefit to readers, it is better to summarize some important observations from your results or some experiences on the selection of parameters at the end of Section 6. You may set up a subsection 6.7 entitled "discussion".

Comments from AE:

Based on the reviewers' comments, my recommendation is accept with amendments. To make the paper in a higher standard, authors should revise their paper according to these comments and prepare a detailed point-to-point response letter accordingly.