Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments

1. Still results section is weak, so more results can be added for deep and better insight and relations to the exisiting state-of-the art works

2. Quality of Figures 1, 2, 3 and 4 is poor and look blurred , so can be redrawn with better and high quality 

3.Related work section is short, so can be extended by adding all the works

4. What Figure 4 portrays? it does not show any comparision with proposed and exisiting mehtods? please re-extract this by showing the proper comparison otherwise it is useless to include this figure without proper informaiton and justification 

5. Still research contribution is not clear, so can be rewritten in bullets in the end of introduction

6. Motivation and problem statement can be rewritten

Major changes are required 

Author Response

Thank you for your revision.

1. The section Simulation result is updated in the manuscript.
2. The pictures are updated.
3. Section related works is updated.
4. Figure 4 explains the proposed Kalman Filter localization algorithm
5. The research contribution is rewritten at the end of the introduction
6. The manuscript is updated.

Author Response File: Author Response.pdf

Reviewer 2 Report (New Reviewer)

This paper addresses a Kalman filter based on a bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor nodes. A new mobile anchor localization strategy is proposed. Network connectivity measurement and bounding box localization method are used to identify the bounded possible location zone. The Kalman Filter is then used to minimize uncertainty produced by the connectivity process. Before the manuscript is considered for publication, the following concerns should be solved.

1.     This paper focuses on a novel localization algorithm in WSNs with mobile anchor nodes. However, from the abstract, introduction, and related work sections, an explanation of the key issues that this work aims to address is missing. It is difficult for the readers to get a clear picture of the problems with the related work.

2.     In the abstract and introduction, the authors should give the reader a brief impression of the BBLA algorithm, not just mention proposing this BBLA algorithm. Both network connectivity measurement and bounding box localization are mentioned to determine possible localization boundaries, but only bounding box localization is introduced, and the introduction of network connectivity measurement is missing. Furthermore, the proposed bounding box localization method can overcome what challenges should be better clarified. Related works about the range-free localization framework for agents and their limitations should also be discussed.

3.     It is suggested to list the contribution points of this work clearly in order to facilitate readers' understanding.

4.     Figure 1 provides little information. I can’t get useful information from the figure. It is suggested to introduce the task and give the reader an overall impression.

5.     Eq. (2) of Page 5,  is the observation noise vector assumed as a vector of random variables normally distributed with zero mean and covariance matrix R. How to set the parameters of R needs more description in the text.

6.     The number of sensor nodes in the experiment part is set to 90, how to determine this value? If the number of sensor nodes is changed, what effect will it have on the experimental results?

7.     In Experiment 6.1, the performance of this algorithm is tested. However, the experimental results of this part are only for 90 unknown nodes. When increasing the number of unknown nodes, what is the impact on the algorithm time? will the solution time be greatly increased? There are no comparative experiments in this section. Do we need to consider the requirement of real-time performance?

Author Response

Thank you for your revision.

1. The abstract and the introduction are updated in the manuscript.

2. The explanation of network connectivity measurement is introduced in the manuscript.

3. The contribution points of this work are added in the introduction.

4. Figure 1 is updated with more details in the manuscript.

5.  Parameters of R are explained in the manuscript (after equation 2).

6. The value of the number of sensor nodes in the experiment part is assumed to be 90 ( we choose this value similar to the other compared algorithms). 

In section 5.2 we showed the evolution of localization error with the variation of the total number of nodes (Figure 6). 

7. In section 5.1, we conduct simulations by varying the number of nodes, and figure 5 shows the convergence analysis of the proposed algorithm within 100 iterations. This section is updated in the manuscript.

 

 

 

Round 2

Reviewer 1 Report (Previous Reviewer 2)

Paper is improved, so minor changes are required

1. Limitations of the proposed method must be highlighted in the 'Conclusion' section

2. Authors are advised to extend the 'Related Work' section by adding more articles to strengthen their idea and clarify the research gaps

3. It would be great if some more results are extracted and added for better and clear insight to readers/reviewers

Minor Changes are required

Author Response

1. The conclusion is updated.
2. The section related works is extended ( Meng, Y., Zhi, Q., Dong, M., & Zhang, W. (2021). A node localization algorithm for wireless sensor networks based on virtual partition and distance correction. Information, 12(8), 330.                                                                                    -Ghorpade, S., Zennaro, M., & Chaudhari, B. (2021). Survey of localization for internet of things nodes: approaches, challenges and open issues. Future Internet, 13(8), 210.                                                                              - Lalama, Z., Boulfekhar, S., & Semechedine, F. (2022). Localization Optimization in WSNs Using Meta-Heuristics Optimization Algorithms: A Survey. Wireless Personal Communications, 122(2), 1197-1220.                       - Optimized localization of target nodes using single mobile anchor node in wireless sensor network de Oliveira, L. L., Eisenkraemer, G. H., Carara, E. A., Martins, J. B., & Monteiro, J. (2022). Mobile Localization Techniques for Wireless Sensor Networks: Survey and Recommendations. ACM Transactions on Sensor Networks (TOSN).                                                        - Liouane, H., Messous, S., & Cheikhrouhou, O. (2022). Regularized least square multi-hops localization algorithm based on DV-Hop for wireless sensor networks. Telecommunication Systems, 1-10.                                       - Messous, S., Liouane, H., & Liouane, N. (2020). Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor networks. Telecommunication Systems, 73(1), 75-86.                                     -Sabale, K., & Mini, S. (2021). Localization in wireless sensor networks with mobile anchor node path planning mechanism. Information Sciences, 579, 648-666.                                                                                                          - Boukhari, N., Bouamama, S., & Moussaoui, A. (2020). Path Parameters Effect on Localization Using a Mobile Anchor in WSN. International Journal of Informatics and Applied Mathematics, 3(2), 12-22.                                     -Zhang, L., Yang, Z., Zhang, S., & Yang, H. (2019). Three-dimensional localization algorithm of WSN nodes based on RSSI-TOA and single mobile anchor node. Journal of Electrical and Computer Engineering, 2019.             - Silmi, S., Doukha, Z., & Moussaoui, S. (2021). A self-localization range free protocol for wireless sensor networks. Peer-to-Peer Networking and Applications, 14(4), 2061-2071.
3. The cumulative cumulative distribution function (CDF) is used as an indicator to assess the accuracy of our proposed algorithm.  The analysis of simulations results is updated.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

The paper should be edited by someone who is more proficient in English. 

The abstract should focus on summarizing the proposed work. There is no need to the lengthly introduction in the abstract.

The introduction section lacks cohesion as some paragraphs discuss unrelated subjects. It should be restructured. Each paragraph should focus on specific points. Please avoid long paragraphs.

The description of the proposed method in section 3 is not written clearly. Please consider using a flowchart and pseudocode to help in presenting the material.

Please provide more details on the design of the proposed Kalman filter.  

Please explain the justification for considering a fixed position in the Kalman filter design.

In equation (1), "wk" is noise and should not be set to zero.

Section "5.3. Comparison of localization accuracy of localization techniques" is too brief. More experiments should be performed to assess the performance difference. More discussion should be provided.

Reviewer 2 Report

Reviews for Authors

• The abstract can be rewritten to be more meaningful. The authors should add more details about their final results in the abstract. Abstract should clarify what is exactly proposed (the technical contribution) and how the proposed approach is validated.
• What is the motivation of the proposed work?
• Introduction needs to explain the main contributions of the work clearer.
• The novelty of this paper is not clear. The difference between present work and previous Works should be highlighted.
• Authors must explain in detail the introduction section.
• Authors must develop the framework/architecture of the proposed methods
• There is need of flowchart and pseudocode of the proposed techniques
• Proposed methods should be compared with the state-of-the-art existing techniques
• Research gaps, objectives of the proposed work should be clearly justified.
• To improve the Related Work and Introduction sections authors are highly recommended to consider these high quality research works <A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks>, <- Chapter#14 “Energy-efficiency of Tools and Applications on Internet”, Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, Intelligent Data-Centric Systems: Sensor Collected Intelligence, By Elsevier, 2018>, <‘A Novel Energy Optimization Approach for Artificial Intelligence-enabled Massive Internet of Things’>, <‘A review on 802.11 MAC protocols industrial standards, architecture elements for providing QoS guarantee, supporting emergency traffic, and security: Future directions’,  Journal of Industrial Information Integration, Elsevier , Vol.24, No.2021, pp.100225, 2021>
• English must be revised throughout the manuscript.
• Limitations and Highlights of the proposed methods must be addressed properly
• Experimental results are not convincing, so authors must give more results to justify their proposal.

Finally, paper needs major improvements