RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

Round 1

Reviewer 1 Report

Dear Authors,

I provide the following high level comment upon your paper based on the typical sections of a review paper. Then I've listed matters of detail that require attention. Overall, the papere was not yet of a standard suitable for submission.

Title:

Abstract specifies that the paper is about solutions for indoors and I judge that that has substantial value. I suggst that 'indoor' be inserted in the title.

Abstract:

Well written.

Introduction:

No particular issues but would be better if 'Background' section was included in the Introduction to make clear the problen you are seking to address, and the potential of the RSSI and machine learning/AI to resolve the problem.

Your 3. Existing Surveys of Indoor Localization:

This is nothing more than an annotated bibliography and makes your paper unpublishable. Not well written. in some instances not clear which authors or what study you are describing. Delete. Use a few parts of this to support what you explain in '4. Parameter based positioning'.

4. Parameter based positioning.

This section is one of the three where you paper can bring value to the reader. For me the other two sections are 'Machine Learning/AI' and 'Discussion'

Use some of the stories from the research you listed in S3. to explain these measures.

You provide tables but do not discuss the content. You can do that in a Discussion Section

Your 5. Radio Signals-based Positioning section.

YOu have carried out a lot work work, developing a comparison of technologies advantages/disadvantages etc.. Mny of those issues are physical context specific. For me this is the topic of a separate paper. Think about leaving it out, using only small bits to explain soe of the typical issues experienced in the indoor context.

Your '6. Overview of machine Learning Based Indoor location'

Perhaps change to Machine Lerning for the indoor application.

Some e.g Table 3 adv/disadv may be used to contribute to the Discussion section.

Your '7. Performance Evaluation Matrices'

Again, high quality work. These Performance Evaluation Matrices could be separated out as a paper in their own right. As your paper is about making the argument for the use of certain technologies and machine learning to bring precision to determining location indoors then lightly mention one or two measures, leaving the more detailed discussion to a subsequent paper.

Have a Discussion section with the purposes that I have outlined earlier. This can be a combined Discussion/Conclusions. make sure that you say what the future research must be. You ahve already established some of that i.e. choice between propriety technologies and Performance Matricies.

kind regards

Author Response

COVER LETTER FOR SUBMISSION OF A MANUSCRIPT

 

To,

Editorial Office,
Eng.

 

Subject: SUBMISSION OF A MANUSCRIPT FOR EVALUATION

       Date: 04/03/2023

I have attached the manuscript “RSSI and Machine learning Based Indoor Localization System for Smart Cities”, by Valmik Tilwari et al., to be submitted to the Eng for consideration of publication.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the undersigned has approved the order of authors listed in the manuscript.

All co-authors have seen and agree with the manuscript's content, and there is no financial interest to report. We certify that the submission is original work and is not under review elsewhere.

We believe our manuscript could be of interest to the readers of the Eng, particularly those working in the areas of Wireless communication networks.

We hope that the editorial board will agree on the interest of this study.

Sincerely yours,

 

Sincerely,

Corresponding author:

Dr. Valmik Tilwari  (Research Professor)

School of Electrical Engineering,

Korea University, Seoul, South Korea.

Contact No: +82030452217

Email: [email protected]

 

Author Response File: Author Response.pdf

Reviewer 2 Report

This manuscript presents a thorough examination of Radio Signal Strength Indicator (RSSI) and machine learning-based techniques for indoor localization and navigation in smart cities. The proliferation of Internet of Things (IoT) and the need for indoor positioning has escalated the fascination with GPS-deprived settings. The study encompasses numerous technologies and algorithms employed in these methodologies, comprising RSSI-based triangulation and machine learning algorithms such as k-NN, SVM, and decision trees. The analysis explores the benefits and drawbacks of each methodology and culminates by addressing unresolved issues and forthcoming directions in this domain.

The article provides an in-depth explanation of the alternative solutions for indoor localization and how machine learning algorithms have improved its accuracy and reliability. The text is well-organized and easy to follow. 

In order to improve the quality of the manuscript, some minor remarks need to be addressed before the paper is to be considered for publishing.

1) Review should identify the potential gaps for further research. In the current version, this chapter does not convey this information.

2) Introduction doesn't provide a description of the novelty/importance of the study.

3) Conclusions need to be improved by listing the main findings and by indicating the current trends in the field. (Section 8 is too general)

Author Response

COVER LETTER FOR SUBMISSION OF A MANUSCRIPT

 

To,

Editorial Office,
Eng.

 

Subject: SUBMISSION OF A MANUSCRIPT FOR EVALUATION

       Date: 04/03/2023

I have attached the manuscript “RSSI and Machine learning Based Indoor Localization System for Smart Cities”, by Valmik Tilwari et al., to be submitted to the Eng for consideration of publication.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the undersigned has approved the order of authors listed in the manuscript.

All co-authors have seen and agree with the manuscript's content, and there is no financial interest to report. We certify that the submission is original work and is not under review elsewhere.

We believe our manuscript could be of interest to the readers of the Eng, particularly those working in the areas of Wireless communication networks.

We hope that the editorial board will agree on the interest of this study.

Sincerely yours,

 

Sincerely,

Corresponding author:

Dr. Valmik Tilwari  (Research Professor)

School of Electrical Engineering,

Korea University, Seoul, South Korea.

Contact No: +82030452217

Email: [email protected]

 

Author Response File: Author Response.pdf

Reviewer 3 Report

MDPI Eng (Manuscript ID: eng-2327740)

 

Comments to the Author

 

This paper presents a comprehensive review of the state-of-art RSSI and machine learning-based approaches for indoor localisation and navigation in smart cities. It is an interesting and relevant topic. However, there are several points that needs to be addressed to improve the quality of the manuscript.

 

Suggestions to improve the quality of the paper are provided below:

 

1)     Since this is a review paper, the authors should include a brief paragraph discussing about the search terms that were used by the authors, the publication databases that are referred to, any filtering criteria that was used, etc to verify the comprehensiveness of the review.

 

2)     In the Introduction section, please briefly mention the different application areas for indoor localisation systems. Some examples of these applications include emergency management, smart energy management and HVAC controls and occupancy detection. Please review and reference these established works as a starting point to highlight the important applications where indoor localisation systems are leveraged.

 

Indoor localisation for building emergency management

Filippoupolitis, A. et al. (2016, December). Bluetooth low energy based occupancy detection for emergency management. In 2016 15th international conference on ubiquitous computing and communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS) (pp. 31-38). IEEE.

 

Indoor localisation for smart energy management

Tekler, Z.D. et al. 2022. Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings. Building and Environment, p.109472.

 

Indoor localisation for smart HVAC controls

Balaji, B. et al., 2013, November. Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (pp. 1-14).

 

Indoor localisation for occupancy prediction

Tekler, Z.D. et al., 2022. Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy. Building and Environment, 226, p.109689.

 

3)     In the last paragraph of the Introduction section, the authors should clearly highlight the objective of this manuscript, which is to “comprehensively review the state-of-the-art RSSI and machine learning-based approaches for indoor localization and navigation in smart cities”. It is also strongly encouraged for the authors to clearly state the contributions of this work and how it extends upon the existing review papers found in the literature.

 

4)     Table 1 is currently 4 pages long. The authors should consider discussing the detailed advantages and disadvantages of each parameter in their respective paragraphs and provide the summarised in bullet points in Table 1 instead.

 

5)     Similarly, the authors should discuss the detailed advantages and disadvantages of different signal-based positioning technologies in their respective paragraphs and provide the summarised version in Table 2 instead. Additionally, the authors should also focus on highlighting the advantages and disadvantages of these technologies, specifically within the context of indoor localisation. Please refer to the literature review performed by the paper below [1] as a good reference.

 

[1] Tekler, Z.D. et al., 2020. A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces. Building and Environment, 171, p.106681.

 

6)     Same comment as Comment 5 for Tables 3 and 4.

 

7)     The description in Section 5.1 on performance metrics should be more specific to the context of indoor localisation. Furthermore, the authors have listed performance metrics for classification and regression problems. In which scenario should each performance metric be utilised? What are their pitfalls? All of these potential discussions would greatly improve the quality of the manuscript and make it more relevant to researchers working on indoor localisation.

 

8)     In Section 5.2 on Performance Issues, the authors should highlight a few more studies and provide more details on how these studies attempted to address the issues of scalability and robustness.

 

9)     Section 6 on Open Issues and Future Directions is quite lacking and only elaborates on one problem but states that there are “several unresolved problems”. Please elaborate on those unresolved problems as well.

Author Response

COVER LETTER FOR SUBMISSION OF A MANUSCRIPT

 

To,

Editorial Office,
Eng.

 

Subject: SUBMISSION OF A MANUSCRIPT FOR EVALUATION

       Date: 10/05//2023

I have attached the manuscript “RSSI and Machine learning Based Localization System for Smart Cities”, by Valmik Tilwari et al., to be submitted to the eng for consideration of publication.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the undersigned has approved the order of authors listed in the manuscript.

All co-authors have seen and agree with the manuscript's content, and there is no financial interest to report. We certify that the submission is original work and is not under review elsewhere.

We believe our manuscript could be of interest to the readers of eng, particularly those working in the areas of Wireless communication networks.

We hope that the editorial board will agree on the interest of this study.

Sincerely yours,

 

Sincerely,

Corresponding author:

Dr. Valmik Tilwari  (Research Professor)

School of Electrical Engineering,

Korea University, Seoul, South Korea.

Contact No: +82030452217

Email: [email protected]

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear Authors, I am disappointed that you have not taken all my advice, and that you have not taken the care to write the paper with the reader in mind. It appears to me to be mostly a quick cut and paste with no attempt to explain wto the reader what is coming and why it is important. In this regard by way of your Abstract you have a succinct explanation of what your paper is about and why that is important, and then goes on to its other tasks of saying what you found to be the case. In contrast your Introduction fails to cover the same territory with your first para not even mentioning RSSI and machine learning.

If your Introduction was fully specific about what the problem was, what was the benefit of finding a solution and what your paper does to find a solution then your paper would then have only the necessary content and would flow between sections i.e. it would be logical to talk about the next section content.

You have not attempted that and I am unable to hlp you further.

Author Response

COVER LETTER FOR SUBMISSION OF A MANUSCRIPT

 

To,

Editorial Office,
Eng.

 

Subject: SUBMISSION OF A MANUSCRIPT FOR EVALUATION

       Date: 10/05//2023

I have attached the manuscript “RSSI and Machine learning Based Localization System for Smart Cities”, by Valmik Tilwari et al., to be submitted to the eng for consideration of publication.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the undersigned has approved the order of authors listed in the manuscript.

All co-authors have seen and agree with the manuscript's content, and there is no financial interest to report. We certify that the submission is original work and is not under review elsewhere.

We believe our manuscript could be of interest to the readers of eng, particularly those working in the areas of Wireless communication networks.

We hope that the editorial board will agree on the interest of this study.

Sincerely yours,

 

Sincerely,

Corresponding author:

Dr. Valmik Tilwari  (Research Professor)

School of Electrical Engineering,

Korea University, Seoul, South Korea.

Contact No: +82030452217

Email: [email protected]

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Thank you for addressing my comments and concerns in great detail.

1. I have noticed one issue on the reference list (my 2nd comment), which I believe was wrongly cited. Currently, references [9] and [10] indicate the same paper on the revised manuscript, in which the author list is also compiled wrongly. To avoid misrepresentation of the existing works, please carefully correct these two references as below: 

[9] Zhuang, Dian, et al. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning." Applied Energy 338 (2023): 120936.

[10] Tekler, Zeynep Duygu, and Adrian Chong. "Occupancy prediction using deep learning approaches across multiple space types: A minimum sensing strategy." Building and Environment 226 (2022): 109689.

Adding on to this comment, please also include the journal DOI for reference [8]; instead of the ResearchGate link, the appropriate doi should reflect directly from the respective journal. (https://doi.org/10.1016/j.buildenv.2022.109472)

2. Another minor issue is the use of the period "." before references. In academic papers, citations should be included before ending the sentences, in other words as part of the sentences, such as "XX [1]." instead of "XX.[1]" Please see the subtle difference and update the manuscript accordingly. 

 

Author Response

COVER LETTER FOR SUBMISSION OF A MANUSCRIPT

 

To,

Editorial Office,
Eng.

 

Subject: SUBMISSION OF A MANUSCRIPT FOR EVALUATION

       Date: 13/05//2023

I have attached the manuscript “RSSI and Machine Learning Based Localization System for Smart Cities”, by Valmik Tilwari et al., to be submitted to the Eng for consideration of publication.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the undersigned has approved the order of authors listed in the manuscript.

All co-authors have seen and agree with the manuscript's content, and there is no financial interest to report. We certify that the submission is original work and is not under review elsewhere.

We believe our manuscript could be of interest to the readers of Eng, particularly those working in the areas of Wireless communication networks.

We hope that the editorial board will agree on the interest of this study.

Sincerely yours,

 

Sincerely,

Corresponding author:

Dr. Valmik Tilwari  (Research Professor)

School of Electrical Engineering,

Korea University, Seoul, South Korea.

Contact No: +82030452217

Email: [email protected]

 

Author Response File: Author Response.pdf