Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber–Physical–Social Systems

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1. Figure 1, Figure 2, Figure 4, Equation (1), Equation (2), etc. have the problem of plagiarizing other papers (Ref. 16) diagrams and equations.

2. The models and application examples discussed in the paper are relatively abstract, without providing clear formulas and data to support the analysis results, lacking strong persuasiveness.

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Thank you for the opportunity to submit a revised version of my manuscript titled Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber-Physical-Social Systems. I greatly appreciate the time and effort that you and the reviewers have dedicated to providing thoughtful and constructive feedback. Their insights have been invaluable in refining and improving the quality of this work.

 

We have carefully reviewed each of the reviewers' comments and made significant revisions in response. Below, you will find our detailed responses to each comment. The version with corrections marked in orange has been sent to the editor. However, the manuscript you receive includes all the corrections but without the orange color markings.

 

 

Comments and Suggestions for Authors:

Point 1: Figure 1, Figure 2, Figure 4, Equation (1), Equation (2), etc. have the problem of plagiarizing other papers (Ref. 16) diagrams and equations.

Response 1: We appreciate the reviewer’s concern and acknowledge that some references were missing in previous versions of the manuscript. To address this, we have thoroughly reviewed and updated the manuscript to ensure that all figures and equations, specifically Figures 1, 2, 4, as well as Equations (1) and (2), are now appropriately referenced. These elements are derived from our previous work, which is now clearly cited as Ref. 16. We have ensured that proper attribution is explicitly mentioned in key sections, including the Abstract, Introduction, and the opening paragraph of the section titled Model to Quantify Human Uncertainty in Human-Centric Cyber-Physical-Social Systems. This clarifies the origin of these figures and equations and reinforces the transparency of our work, ensuring academic integrity.

Point 2: The models and application examples discussed in the paper are relatively abstract, without providing clear formulas and data to support the analysis results, lacking strong persuasiveness.

Response 2: We appreciate the reviewer’s valuable feedback. In response, we have made several updates throughout the manuscript to provide more concrete formulas and data to strengthen the analysis. Specifically, we have included detailed mathematical formulations of the key components of the MMtQHU model, as well as additional data derived from the simulations in the Ingolstadt Traffic Scenario (InTAS). These updates are designed to provide a clearer and more persuasive demonstration of the model’s applicability and effectiveness. Furthermore, the results section has been expanded to present more specific data and analysis, supported by figures and tables that illustrate the model's performance. We believe these revisions adequately address the reviewer’s concern and significantly enhance the manuscript’s clarity and persuasiveness.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The reviewed work has a contemporary theme. It focuses on the modeling of complex systems involving the interaction of humans with Cyber-Physical-Social Systems (CPSS). The main consideration is the uncertainty caused by people driving cars on an already chosen route. The aim of the work is to evaluate the applicability of a developed model to study this uncertainty.
  The work has the following strengths:
1. A simulation model was created based on real data for the city of Ingolstadt;
2. Multiple simulations were conducted to prove the applicability of the MMtQHU (Multidisciplinary Model to Quantifying Human Uncertainty) model.
  Notes and recommendations to the authors:
1. To add a new section entitled "Related works", in which to consider studies of other authors related to the topic of the article. The review should concludes with an analysis of what the other authors have developed and how this work differs from the other studies;
2. To give more information about the InTAS scenario. The presentation of this additional information should be as if it were for readers who are learning about it for the first time, such as: what it is; how it is made; for which cities, countries and other can be used;
3. To provide more information about the simulation model proving the applicability of MMtQHU - how it was implemented; why this particular modeling platform was used and not some other and so on. Also to describe in detail figure 4. However, this is the platform used and it is desirable to give more information about what is represented in the figure.

Best regards.

Author Response

Thank you for the opportunity to submit a revised version of my manuscript titled Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber-Physical-Social Systems. I greatly appreciate the time and effort that you and the reviewers have dedicated to providing thoughtful and constructive feedback. Their insights have been invaluable in refining and improving the quality of this work.

 

We have carefully reviewed each of the reviewers' comments and made significant revisions in response. Below, you will find our detailed responses to each comment. The version with corrections marked in blue has been sent to the editor. However, the manuscript you receive includes all the corrections but without the blue color markings.

 

Comments and Suggestions for Authors:

Point 1: To add a new section entitled "Related works", in which to consider studies of other authors related to the topic of the article. The review should concludes with an analysis of what the other authors have developed and how this work differs from the other studies;

Response 1: We thank the reviewer for this suggestion. We have added a new section entitled “Related Works” in the manuscript, as recommended. In this section, we include relevant studies by other authors related to uncertainty modeling in Cyber-Physical-Physical-Social Systems (CPSS) and 5G networks.

 

The review includes both theoretical approaches to uncertainty modeling and applied studies on resource allocation in communication networks, highlighting the lack of integration of human factors in many of these works. At the end of the section, we have incorporated a comparative analysis, highlighting how our work addresses the limitations of previous studies by proposing a multidisciplinary framework that integrates human uncertainty in a realistic simulation environment such as Ingolstadt. This analysis highlights the unique contributions of our MMtQHU model compared to traditional approaches.

Point 2: To give more information about the InTAS scenario. The presentation of this additional information should be as if it were for readers who are learning about it for the first time, such as: what it is; how it is made; for which cities, countries and other can be used;

Response 2: Thank you for this insightful suggestion. In response, we have expanded the description of the InTAS (Ingolstadt Traffic and Simulation) scenario in the manuscript. We now include a detailed explanation of what InTAS is, how it was created, and its key components. Specifically, we describe how the model was developed using real-world geographical and demographic data from Ingolstadt, Germany, integrating detailed urban elements such as roads, buildings, traffic lights, public transportation routes, and parking areas.

Furthermore, we explain the tools and software used to create the simulation, such as OpenStreetMap and SUMO (Simulation of Urban Mobility), which allow for accurate replication of traffic and communication systems in urban environments. We also clarify that, although InTAS was initially designed for Ingolstadt, the model can be adapted to other cities and countries by replicating the same data collection and simulation processes, making it a versatile tool for urban traffic management and vehicular communication studies globally.

These enhancements have been included in Section 4 of the manuscript to provide readers who are unfamiliar with the scenario a comprehensive understanding of its relevance and adaptability.

 

 

Point 3: To provide more information about the simulation model proving the applicability of MMtQHU - how it was implemented; why this particular modeling platform was used and not some other and so on. Also to describe in detail figure 4. However, this is the platform used and it is desirable to give more information about what is represented in the figure.

 

Response 3: We appreciate the reviewer’s observation and have accordingly expanded the section on the simulation model to provide further details on the implementation of the MMtQHU model. Specifically, we now explain how the MMtQHU was integrated into the simulation environment using the Artery-C framework, which is highly suitable for vehicular communication (V2X) scenarios in 5G networks. This platform was chosen due to its advanced capabilities to handle real-time communications in dynamic urban settings, as well as its compatibility with SUMO (Simulation of Urban Mobility), which accurately models complex traffic behavior. The flexibility and precision of these tools make them ideal for studying the effects of human uncertainty on network resource allocation.

We also included an explanation as to why this combination of platforms was preferred over other alternatives. The Artery-C and SUMO platforms together provide a balance between communication simulation and traffic modeling, allowing for a comprehensive assessment of the impact of human behavior on both traffic and network performance. Other platforms lacked this integrated approach, making Artery-C and SUMO the optimal choice for our study.

Additionally, we have provided a more detailed description of Figure 4, explaining the key components of the simulation framework it represents. Figure 4 illustrates the flow of data between the simulation platforms and how vehicular mobility, 5G communication, and human uncertainty factors interact within the model. Each element in the figure is now described to clarify its role in the simulation process and its relevance to the study.

 

These modifications have been incorporated into Section 5 of the manuscript to address the reviewer’s concerns and improve the clarity of the simulation model’s presentation.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. It would be better use the third person, not the first person such as we.

2. Multidisciplinary Model should be addressed with more details in introduction.

3. What is the InTAS? The full name should be given at its first appearance.

4. 4. Simulation parameters, this should be experimental scheme, not only the parameters.

5. What are the performance? The results should present it with more details.

6. Only simulation models, so how is it related with the actual applications?

7. Fig .5 -10 should be improved with the clear axis.

8. The highlights in this paper are not clear, so what are the highlights?

9. Conclusions should be improved with the clear results from the experiments.

10. The abstracts and conclusions should be improved with more highlights. 

Author Response

Thank you for the opportunity to submit a revised version of my manuscript titled Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber-Physical-Social Systems. I greatly appreciate the time and effort that you and the reviewers have dedicated to providing thoughtful and constructive feedback. Their insights have been invaluable in refining and improving the quality of this work.

 

We have carefully reviewed each of the reviewers' comments and made significant revisions in response. Below, you will find our detailed responses to each comment. The version with corrections marked in green has been sent to the editor. However, the manuscript you receive includes all the corrections but without the green color markings.

 

Comments and Suggestions for Authors:

Point 1:  It would be better use the third person, not the first person such as we.

Response 1: The authors agree with the reviewer’s suggestion. The manuscript has been revised to consistently use the third person throughout. Instances where the first person was previously used have been replaced with neutral, objective language to maintain a formal academic tone. This adjustment has been applied across all sections of the document to improve readability and align with standard scientific writing practices.

Point 2: Multidisciplinary Model should be addressed with more details in introduction.

Response 2: We appreciate the reviewer's insightful comment regarding the need for more details about the Multidisciplinary Model in the introduction. In response, we have revised the introduction to provide a more comprehensive description of the Multidisciplinary Model to Quantify Human Uncertainty (MMtQHU). The added details include an explanation of the model's conceptual framework, its three main contextual layers (Natural Context, Social Context, and Personal Context), and how it facilitates interdisciplinary collaboration through Boundary Objects. We believe these enhancements offer clearer insights into the model's significance and its role in our research.

 

 

Point 3: What is the InTAS? The full name should be given at its first appearance.

 

Response 3: The authors appreciate the reviewer’s attention to detail. The full name of InTAS, Ingolstadt Traffic and Simulation, has now been provided at its first mention in the manuscript. This change ensures that readers unfamiliar with the acronym will immediately understand its meaning and relevance.

Point 4: Simulation parameters, this should be experimental scheme, not only the parameters.

Response 4: The authors appreciate the reviewer’s suggestion. The section previously titled Simulation Parameters has been renamed Experimental Scheme to better reflect its content. In addition to the parameters, this section now provides a more comprehensive explanation of the experimental setup, including the design and configuration of the simulation environment, the tools used, and the sequence of experiments conducted.

Point 5: What are the performance? The results should present it with more details.

Response 5: The authors appreciate the reviewer’s insightful observation. To address this, we have expanded the Results section and provided a more detailed analysis of the performance metrics, focusing on key indicators of network performance. Specifically, the Call Drop Rate (CDR), which measures the percentage of calls dropped due to insufficient network resources, and Resource Allocation Efficiency, which evaluates how effectively the available network frequencies are utilized under varying conditions, have been elaborated as critical metrics. These indicators are essential for assessing the impact of human-induced variability on network resource management.

To further clarify how these metrics are applied in the study, we have introduced subsection 5.4 Performance Metrics, which provides a thorough explanation of these performance indicators. This section details how CDR and Resource Allocation Efficiency were measured across different congestion scenarios and varying driver behaviors. Additionally, it highlights the methodology used to capture these metrics during multiple simulation runs, ensuring the robustness of the results.

Point 6: Only simulation models, so how is it related with the actual applications?

Response 6: The authors appreciate the reviewer’s question regarding the relevance of the simulation models to actual applications. To clarify this connection, new sections of text have been added to the manuscript. In the Introduction and Section 4 (Ingolstadt Traffic Scenario), we now explain in more detail how the Ingolstadt Traffic and Simulation (InTAS) scenario is built using real-world geographical, demographic, and traffic data. This ensures that the simulations accurately reflect the actual conditions in urban environments like Ingolstadt, making the findings highly relevant for real-world applications.

Additionally, in Section 6 (Results and Discussion), further text has been added to explain how the Multidisciplinary Model to Quantify Human Uncertainty (MMtQHU) is designed to be directly applicable to real-world 5G-enabled Cyber-Physical-Social Systems (CPSS). The results highlight the impact of human behavior on network performance and resource allocation, providing insights that are transferable to real deployments, such as in vehicle-for-hire applications and urban traffic management.

Point 7:  Fig .5 -10 should be improved with the clear axis.

Response 7: The authors appreciate the reviewer’s observation. In response, Figures 5 through 10 have been updated to include clearer and more detailed axis labels. This ensures that the data presented in these figures is easier to interpret and that the axis titles accurately reflect the variables being measured. Additionally, the font size and formatting of the labels have been improved for better readability.

Point 8:  The highlights in this paper are not clear, so what are the highlights?

Response 8: The authors appreciate the reviewer’s feedback. To improve clarity, we have revised the Abstract, Introduction, and Conclusion sections to better highlight the main contributions of the paper.

In particular, the Introduction now includes a new section that explicitly outlines the relevance of this study to real-world applications. Specifically, this section emphasizes how the multidisciplinary simulation framework, which integrates vehicular mobility and network communications, provides critical insights into managing dynamic environments like Cyber-Physical-Social Systems (CPSS). This highlights the study’s potential impact on traffic management and 5G network resource allocation, showing that the simulations are not just theoretical but serve as a vital tool for predicting and optimizing system performance before real-world deployment​(Version_corregida).

Additionally, the key contributions, such as the development of the MMtQHU model and the application of real-world data from the InTAS scenario, are now explicitly highlighted in the Conclusion to ensure the paper’s main findings are clearly communicated.

Point 9:  Conclusions should be improved with the clear results from the experiments.

Response 9: The authors appreciate the reviewer’s suggestion. In response, the Conclusions section has been revised to explicitly include the key findings from the experiments. We have now clearly summarized the main results, such as the impact of human uncertainty on network performance, specifically highlighting the Call Drop Rate (CDR) and Resource Allocation Efficiency across different congestion scenarios. The experiments demonstrated that the Multidisciplinary Model to Quantify Human Uncertainty (MMtQHU) significantly improves resource allocation strategies under varying human behaviors, leading to a more robust network performance in 5G-enabled systems.

Additionally, we have emphasized how the simulations showed a reduction in performance variability and better handling of peak congestion periods when using the MMtQHU model compared to traditional methods. These results provide clear evidence of the practical benefits of incorporating human uncertainty into network management strategies.

These improvements ensure that the conclusions reflect the experimental outcomes more clearly, tying them directly to the paper’s contributions.

Point 10:  The abstracts and conclusions should be improved with more highlights. 

Response 10: The authors thank the reviewer for the valuable suggestion. In response, both the Abstract and Conclusions sections have been revised to better emphasize the key highlights of the paper.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The paper now is improved and is OK. I do not have any remarks and suggestions.

Best regards.

Reviewer 3 Report

Comments and Suggestions for Authors

Revised well.