Modeling and Analysis of Dispersive Propagation of Structural Waves for Vibro-Localization

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Paper Summary

The paper presents a detailed analysis of sensor data accuracy in variable environmental conditions. It investigates how factors like temperature, humidity, and electromagnetic interference impact sensor performance, particularly focusing on IoT devices in urban settings. Through a series of experiments, the authors propose a new calibration method that dynamically adjusts sensor outputs to improve accuracy and reliability under changing environmental conditions.

Strengths

1. The paper introduces a calibration technique that adapts in real-time to environmental changes, which is an advancement over static methods.

2. Extensive experimental setups are used to test the sensors under varied conditions.

3. The focus on IoT devices' robustness to environments is timely, given the growing deployment of these technologies in urban environments where environmental variability is high.

Weakness and Revision Suggestions

1. Limited sensors for evaluation: The experiments in the paper primarily focus on specific types of sensors, which might limit the generalizability of the results. Further validation is needed to determine if the method is equally effective for a broader range of sensor types and different IoT devices. This limitation could affect the widespread deployment of the method in practical IoT applications.

2. Algorithm complexity: While the dynamic calibration method performs well in experiments, it is complex to implement, involving real-time computations and adjustments across multiple variables. Implementing such complex algorithms efficiently in resource-constrained IoT devices poses a challenge, which is not evaluated in their environments. Additionally, high complexity may lead to increased energy consumption and processing time, which may be unacceptable in certain application scenarios.

3. Lack of real-world validation: Although the study involves different environmental conditions, it is primarily conducted in laboratory settings. The real-world environments of IoT applications are more complex and variable, including more extreme changes in temperature and humidity, as well as more complex electromagnetic interference. The absence of testing in these practical application environments might limit the method's usability and broader adoption.

4. Figure 3,4,7 are not clear enough.

Comments on the Quality of English Language

I'm fine with the quality of English. It's easy to follow.

Author Response

Paper Summary 

The paper presents a detailed analysis of sensor data accuracy in variable environmental conditions. It investigates how factors like temperature, humidity, and electromagnetic interference impact sensor performance, particularly focusing on IoT devices in urban settings. Through a series of experiments, the authors propose a new calibration method that dynamically adjusts sensor outputs to improve accuracy and reliability under changing environmental conditions. 

Strengths 

1. The paper introduces a calibration technique that adapts in real-time to environmental changes, which is an advancement over static methods. 

2. Extensive experimental setups are used to test the sensors under varied conditions. 

3. The focus on IoT devices' robustness to environments is timely, given the growing deployment of these technologies in urban environments where environmental variability is high. 

Weakness and Revision Suggestions 

1. Limited sensors for evaluation: The experiments in the paper primarily focus on specific types of sensors, which might limit the generalizability of the results. Further validation is needed to determine if the method is equally effective for a broader range of sensor types and different IoT devices. This limitation could affect the widespread deployment of the method in practical IoT applications. 

The feedback from the reviewers is truly appreciated. Our paper’s focus is not on broad IoT applications but solely vibro-localization. Within the vibro-localization studies, accelerometers are the de-facto standard sensing modality. Broadening the sensor types would be inconsistent with scope of the defined problem setting. As such, our goal was to refine and validate the proposed methodology within a well-defined and representative class of sensors. 

2. Algorithm complexity: While the dynamic calibration method performs well in experiments, it is complex to implement, involving real-time computations and adjustments across multiple variables. Implementing such complex algorithms efficiently in resource-constrained IoT devices poses a challenge, which is not evaluated in their environments. Additionally, high complexity may lead to increased energy consumption and processing time, which may be unacceptable in certain application scenarios. 

Thank you very much for feedback. The algorithm presented in our manuscript is not intended for limited-resource IoT applications. The algorithm presented in the manuscript is much more efficient than the existing state-of-the-art vibro-localization field. This improvement in the computational efficiency is noted as a contribution in the manuscript. Developing a resource-aware calibration technique for constrained IoT devices would be an interesting extension of the current work albeit not in the scope of the current research. 

3. Lack of real-world validation: Although the study involves different environmental conditions, it is primarily conducted in laboratory settings. The real-world environments of IoT applications are more complex and variable, including more extreme changes in temperature and humidity, as well as more complex electromagnetic interference. The absence of testing in these practical application environments might limit the method's usability and broader adoption. 

Thank you for your constructive critique. The two experimental studies presented in our manuscript illustrate two distinct settings: (1) a controlled laboratory environment and (2) a large-scale experiment embedded within a public building on a university campus. The latter represents a real-world dataset, as the sensors are integrated into the building's structure and are subject to real-world interference. 

 To address any potential confusion, we have added paragraphs 2, 3, and 4 in the third section ("Experiments") to provide further clarification and detail regarding these two experimental setups. 

4. Figure 3,4,7 are not clear enough. 

We acknowledge and appreciate the reviewers' feedback. Figures 3, 4, 7 are updated with clear axis labels and appropriate captions. 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This work proposes a new method for localizing occupants in a building by analysis of dispersive propogation of strucutural waves generated by their movement. Overall, this paper is well-written and the reuslts are interesting. Reagrding this work, I have the following two questions.

(1) How do you design the sensor distribution in Figures 3 and 4, e.g., the sensor number and locations?

(2) Is your method generally applicable to other engineering structures, such as multimodal dispersive waves in rails of railway track structures? Please eloborate. 

Author Response

This work proposes a new method for localizing occupants in a building by analysis of dispersive propogation of strucutural waves generated by their movement. Overall, this paper is well-written and the reuslts are interesting. Reagrding this work, I have the following two questions. 

(1) How do you design the sensor distribution in Figures 3 and 4, e.g., the sensor number and locations? 

We acknowledge and appreciate the reviewers' feedback. The experimental studies presented in were conducted by two different research groups; and are considered state-of-the-art for the field. Therefore, these parameter designs differ from each other. In order to clarify the manuscript, we added a new paragraph (Section 3, paragraph 2) that introduces the focus and consideration of the studies and describe the difference between these two experimental design. 

(2) Is your method generally applicable to other engineering structures, such as multimodal dispersive waves in rails of railway track structures? Please eloborate.  

We acknowledge and appreciate the reviewers' feedback. The reviewer’s feedback is a great example (rail roads, bridges, etc.) of highly dispersive one-dimensional wave propagation. We added a brief paragraph explaining the possible applicability in the highly dispersive multi (references added [13], and [14]). The algorithms presented in the added references are not  directly applicable to the vibro-localization problem discussed in the manuscript. In our manuscript, the focus is limited to vibro-localization under weakly dispersive planar wave propagation.  

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thanks to the authors for the response. Generally, my concerns are addressed.

Comments on the Quality of English Language

It's easy to follow and understand the paper. The writing is relatively good.