On the Integration of Standard Deviation and Clustering to Promote Scalable and Precise Wi-Fi Round-Trip Time Positioning

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. RTT can be accurately measured when LoS is fine, and the experimental result shows in only open-space. I wonder if the proposed method works in another environments other than open-space in which LoS is bad.

 2. I wonder if RTT is commonly available for normal smartphones except Google Pixel. And also as far as I know, there are not many commercial WiFi APs which can use RTT. Is this method applicable in practice?

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

This paper focuses on the integration of standard deviation and clustering techniques to improve the scalability and precision of Wi-Fi RTT positioning systems. The authors propose novel methods to reduce network traffic while maintaining accurate positioning estimates using a combination of Wi-Fi RTT and RSSI, along with clustering to distribute the overhead among multiple access points. But before recommendations, I have the following major concerns:

1. The abstract and introduction are well-written, but the objective could be more explicitly stated to make the contribution clearer from the start.

2. The use of standard deviation (STD) in RTT measurements is innovative, but its practical benefits should be better highlighted with real-world scenarios.

3. The related work section is comprehensive, but the authors could explore recent advances in alternative indoor positioning systems like LiDAR or UWB to provide a more holistic view of the field.

4. The experiment setup with Wi-Fi RTT-compatible access points is solid, but the justification for the specific hardware choices (Google APs and Linksys Velop) needs to be elaborated in more detail, especially considering their influence on the system performance.

5. While the clustering approach to improve scalability is well-explained, the criteria for determining the number of clusters (k) could be further clarified, as well as its impact on real-time application.

6. The use of RMSE as the primary performance metric is standard, but it would be beneficial to include other metrics like precision-recall or latency to better assess the system's trade-offs in high-traffic environments.

7. The paper mentions scalability improvements with clustering, but there is limited discussion on the limitations or upper bounds of scalability, especially in dense urban environments or multi-floor buildings.

8. The heatmaps and figures are useful in understanding the RMSE performance, but clearer labeling and a more intuitive color scheme could help non-expert readers interpret the results more easily.

9. Although the results indicate significant improvements, it would be helpful to see a direct comparison with existing multilateration-based or hybrid systems in terms of real-world accuracy and cost-efficiency.

10.  The conclusion could offer a more detailed roadmap for future research directions, such as the potential for integrating the system with 5G networks or other emerging IoT technologies.

Comments on the Quality of English Language

1. Some sentences are long and complex, which can make them harder to follow. Breaking them into shorter, more direct sentences could improve clarity.

2. Make sure terms like "standard deviation," "Wi-Fi RTT," and "clustering" are consistently capitalized and referred to throughout the paper. Consistency improves readability.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The current quality of the paper might be published to Technologies.

Reviewer 2 Report

Comments and Suggestions for Authors

All changes have been made so I recommend it for the publication