QACM: Quality Aware Crowd Sensing in Mobile Computing
, Prabhu Prasad B. M. 4 and Pavan Kumar B. N. 5,*
Round 1
Reviewer 1 Report
This paper proposes a machanism to categorize users for crowd sensing in order to achieve better QOI and improve the efficiency. The manuscript has an interesting topic and good structure. However, some comments as follows may be useful for paper improvement.
1. The description of Fig. 2 seems to be missing, and why does the proposed QACM method reduce the signal strength when the simulation time is 4000.
2. Is there any problem with the expression of formula (5), because each device may have multiple sensors.
3. It is mentioned that 'Fig. 6 shows the crowd sensing efficiency (CSE)' in this manuscript, but Fig. 6 seems to be missing.
4. The layout and format of the manuscript need to be improved.
Author Response
Reviewer 1
We would like to thank you for your thoughtful comments and constructive suggestions, which helped to improve the quality of this manuscript. Our responses for all comments follow.
- The description of Fig. 2 seems to be missing, and why does the proposed QACM method reduce the signal strength when the simulation time is 4000.
Ans: Thank you for your feedback. We have given clarity in the revised manuscript. Here the signal strength increases linearly as from initiation phase to completion phase that involves the available wireless infrastructure at that instance based on the requirements.
- Is there any problem with the expression of formula (5), because each device may have multiple sensors.
Ans: In our research we assumed that each device equipped with single sensing unit. Thank you for your input, we would like to consider this for our future work point.
- It is mentioned that 'Fig. 6 shows the crowd sensing efficiency (CSE)' in this manuscript, but Fig. 6 seems to be missing.
Ans: We apologies for the typo mistake. We have corrected it in the revised manuscript.
- The layout and format of the manuscript need to be improved.
Ans: Thank you, we have improved the layout and format as per your suggestion in the revised manuscript.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments to the authors:
The application of ICT and IoT has been receiving much attention in recent years. The study provided by the authors is interesting and hopefully, closes the gap in this research domain. Overall, the manuscript is properly written and methodologically sound. However, there are a few comments to be addressed.
(i) Abstract: Major modification is required. Too much information about the introduction but less about the findings of the study.
(ii) Introduction: The connection between the paragraphs is weak and the storyline is not smooth to bring out the focus of the study. Please improve. Also, the introduction section is too lengthy. Please revise and provide relevant information. Also, highlight the research gap and research objectives.
(iii) Literature review. Poorly done. No point in doing the literature review section like this without providing your own views/connection among the studies. Please summarize and highlight the similarities/differences and also the gaps that are existing in the current studies.
(iv) Move the background and problem definition to your introduction.
(v) Section 5 should be your methodology section. Please name them correctly.
(vi) Provide a research flow chart in section 5 and also a schematic diagram of your proposed model to ease the readers.
(vii) Section 6 should be results and discussion. Also, not many in-depth comparisons were done. I would suggest the authors compare the findings with existing literature. Unique contributions of research should be highlighted.
(viii) Implications: The authors must develop a subsection for theoretical and practical implications. Implications could be enhanced by providing the results of your work toward the development and adoption of the current findings.
(ix) The conclusion section seems to be a repetition of the results section. Huge modifications are required. Please make sure your ‘conclusion’ section underscores the scientific value added to your paper, and/or the applicability of your findings/results, as indicated previously. Please revise your conclusion part into more detail. Basically, you should enhance your contributions, and limitations, underscore the scientific value added of your paper, and/or the applicability of your findings/results and future study in this section.
(x) Some relevant reference is missing.
(i) DOI: 10.1016/j.jclepro.2021.129230
Author Response
Reviewer 2
We would like to thank you for your thoughtful comments and constructive suggestions, which helped to improve the quality of this manuscript. Our responses for all comments follow.
- Abstract: Major modification is required. Too much information about the introduction but less about the findings of the study.
ANS: Thank you for your feedback. In the revised manuscript, we mentioned quite a few and appropriate information in abstract with our findings for better understanding of the readers.
- Introduction: The connection between the paragraphs is weak and the storyline is not smooth to bring out the focus of the study. Please improve. Also, the introduction section is too lengthy. Please revise and provide relevant information. Also, highlight the research gap and research objectives.
ANS: Thank you for your thoughtful comment, it helped us a lot to improve the quality of the manuscript. In the revised manuscript, we shorten the length of introduction. And we specified the gap clearly in Background study. Most of the existing works in Mobile crowd sensing uses different measures to improve the quality of sensing, in unpredictable environmental conditions such as spatio-temporal aspects, selection of right participants, selfishness of participants, lack of infrastructure and so on. The different applications require different levels of quality expectations. In order to fulfill these qualities for various applications, we introduced the new concept with more relevant parameters such as such as Reliability based on distance Rbd, Transmission range to Sink Trt, Time that requires to send to Sink/destination Trt , and Quality of sensor devices used in mobile devices Mqs,. These parameters introduce remarkable QOI in crowd sensing for various applications of crowd sensing using opportunistic transmission paradigm.
- Literature review. Poorly done. No point in doing the literature review section like this without providing your own views/connection among the studies. Please summarize and highlight the similarities/differences and also the gaps that are existing in the current studies.
Ans: Thank you for your feedback. We conducted thorough literature survey which is relevant to this research. In the revised manuscript, we specified our view connection at the end of every literature survey. For instance,
Gurdit singh et al [10] devised a prototype of finding the in attentive driving behavior and authentication on the urban roads. The data has been sensed and Dynamic Time Wrapping (DWT) is used to detect the events collected from the smart phones with sensors. Here multiple sensors have been used to fuse the more patterns for more precise detection. This prototype helps to detect careless driving events using MCS. Key yan et al [11] provides a framework to select a task in MCS by combining the facts like location privacy preservation, efficient resource consumption and high task profits. Here there is freedom of opting the path, altering their privacy requirements according to their convenience without disclosing the location information and destination inference attack. this work further needs to consider task completion rate payment issues of the users.
Ken yan et al [16] proposed two task selection mechanisms for workers in mobile crowd sensing, based on the study on task bidding and Task Assignment for workers and platform. This work achieved good results on better privacy reservation without much cost compared to earlier works. Weiping Zhu et al [17] investigated multitask allocation problem in which the different participants carry different devices and perform different tasks. And then work is accomplished by introducing A greedy discrete particle swarm optimization with genetic algorithm. This algorithm assumes that the assigned tasks are static.
- Move the background and problem definition to your introduction.
Ans: We moved the background and problem definition to the introduction section as per your suggestion.
- Section 5 should be your methodology section. Please name them correctly.
Ans: Thank you for your suggestion. We have named it correctly in the revised manuscript.
- Provide a research flow chart in section 5 and also a schematic diagram of your proposed model to ease the readers.
ANS: Thank you for your comment. Here the System Architecture clearly indicates the research flow and also we provided clear explanation for the readers in the Fig.1. It depicts the System architecture of proposed model for QACM. This Architecture is divided into five phases. The mobile users are identified based on quality of sensing elements used in their devices in phase 1. Here incentives are proportional to their quality of sensing elements present their mobile devices. Phase 2 and phase 3 represents the categorization of Mobile users based on QOI parameters. Mu1, Mu2 and Mu3 are the three categories which are ready for deployment in subsequent phase 4. The Phase 5 involves transmission of data to the Sink/destination through crowd sensing.
- Section 6 should be results and discussion. Also, not many in-depth comparisons were done. I would suggest the authors compare the findings with existing literature. Unique contributions of research should be highlighted.
Ans: Thank you for your valuable feedback. We have added and highlighted in the revised manuscript. We have considered your valuable comment regarding in-depth comparison to address in our future work direction.
- Implications: The authors must develop a subsection for theoretical and practical implications. Implications could be enhanced by providing the results of your work toward the development and adoption of the current findings.
Ans: Thank you for your valuable input. We have provided theoretical and practical implications in Simulation and Performance analysis section.
- The conclusion section seems to be a repetition of the results section. Huge modifications are required. Please make sure your ‘conclusion’ section underscores the scientific value added to your paper, and/or the applicability of your findings/results, as indicated previously. Please revise your conclusion part into more detail. Basically, you should enhance your contributions, and limitations, underscore the scientific value added of your paper, and/or the applicability of your findings/results and future study in this section.
Ans: Thanks a lot for your valuable input. This comment helped us to improve the quality of the manuscript. We have modified conclusion section as per your suggestions. Further, we have also discussed our implementation and results in methodology, and we concluded with very fever points relevant to our research.
Most of the previous works in Mobile Crowd sensing utilized more infrastructure-based transmission available at the respective locality or geographical limits during the Sensing time. QACM exploits the opportunistic transmission by hiring the Mobile users that provide greater flexibility in providing the necessary hardware resources like advanced sensors bandwidth and software support due to advanced mobiles with fastest growing technologies. However the Reliability, Transmission time and crowd sensing efficiency are most important factors in improving QOI in MCS. Many previous works attempt to bring the QOI to the required level. In QACM, very effective parameters have been devised to increase the reliability, reduce the transmission time and increase the Efficiency of crowd sensing. The parameters such as Rbd, Tri, Trt , and Mqs strengthen the QACM to bring more efficiency in MCS. Here categorization of Sensing mobiles based on their sensing capacity brought great changes in sensing efficiency and Bandwidth allocations based on high, medium and low range sensitivity requirement applications. This work can be extended to highly sensitive MCS applications like Defense surveillance system, Medical applications, Forest surveillance system, Agriculture crops security and monitoring and etc..
(x) Some relevant reference is missing. And DOI: 10.1016/j.jclepro.2021.129230
Ans: Thank you! We have included in the revised manuscript as per your suggestion.
Author Response File: Author Response.pdf
Reviewer 3 Report
1. How do you categorize the applications based on their sensitivity, as you mention in the problem statement? Justify.
2. Can you suggest an idea to increase the transmission rate to sink? That is
Trt
3. Do you have any mechanism to select the mobile users based on incentive mechanism? If so suggest your idea.
4. What are the high sensitivity applications may be considered for the future work, as you mentioned in conclusion?
5. How much your research work is relevant to present day application scenario.
6. Can you incorporate any emerging technologies like IOT in future work.
Comments for author File: Comments.pdf
Author Response
Reviewer 3
We would like to thank you for your thoughtful comments and constructive suggestions, which helped to improve the quality of this manuscript. Our responses for all comments follow.
- How do you categorize the applications based on their sensitivity, as you mention in the problem statement? Justify.
Ans: Thank you for your valuable feedback. As the new advancement in the sensing devices which are used for various applications. For example, IOT applications at home, defence, forest surveillance system and so on. In these applications the collection and monitoring parameters based on the seriousness of applications.
- Can you suggest an idea to increase the transmission rate to sink? That is Trt
Ans: It is possible by using strong radio communication with high bandwidth can certainly increase Transmission rate.
- Do you have any mechanism to select the mobile users based on incentive mechanism? If so suggest your idea.
Ans: As Such there are no specific mechanisms to select the mobile users. However, By designing attractive mobile applications with good incentives, it is possible to attract the mobile users to involve in more and more applications. And we can involve in the name social and moral responsibilities. (For Example, in Recent COVID time the involvement of mobile computing is remarkable….)
- What are the high sensitivity applications may be considered for the future work, as you mentioned in conclusion?
Ans: Thank you for pointing this out. In order to eradicate the diseases like COVID, Defence Surveillance system, Natural disaster Management and health care issues.
- How much your research work is relevant to present day application scenario.
Ans: This Research work is more relevant as advanced mobile hardware devices emerging with new futures, and new applications, especially for Smart city concepts can exploit this research for more relevant and instantaneous information’s. Example, electricity meter reading, water meter reading, traffic monitoring, theft monitoring at home/office etc.,
- Can you incorporate any emerging technologies like IOT in future work.
Ans: Yes. There are lot of Emerging technologies like, Virtual Reality, Augmented Reality, Cloud Computing, Data Science, Machine Learning are more relevant technologies that can support Mobile Crowd Sensing.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments to the authors:
The authors have substantially addressed the comments, however still many issues need to be addressed:
(i) The paper requires extensive language and formatting improvements.
Line 348-350
As Shown in Figure 2. Here the signal strength increases linearly as from initiation phase to completion phase that involves the available wireless infrastructure at that instance based on the requirements.
(ii) Why there is an addition of 2 extra authors after revision? Please provide concrete reasons for what they have performed. Else, this is misconduct in research/academics.
(iii) Where is the theoretical and practical implications added???? Do not simply address the reviewer's comment like this!!
(iv) The reference [26] was added, however was not found in the main text. Please cite properly in the main text.
(v) No in-depth comparison was really found in the study.
Author Response
Reviewer 2
We would like to thank you for your thoughtful comments and constructive suggestions, which helped to improve the quality of this manuscript. Our responses for all comments follow.
The authors have substantially addressed the comments, however still many issues need to be addressed:
(i) The paper requires extensive language and formatting improvements.
Line 348-350
As Shown in Figure 2. Here the signal strength increases linearly as from initiation phase to completion phase that involves the available wireless infrastructure at that instance based on the requirements.
Ans: Thank you for your feedback. We have corrected the grammatical issues in the revised manuscript.
(ii) Why there is an addition of 2 extra authors after revision? Please provide concrete reasons for what they have performed. Else, this is misconduct in research/academics.
Ans: Thank you for pointing this out. At the time of submission some of the co-contributors have not given consent to mention their names in the manuscript.
Prof. Shanawaz Ahamed J has contributed significantly to the revised version of the paper. He has contributed in reviewing and revising the paper in highlighting the research gaps and objectives more clearly. Also, he has contributed to the revised literature review section wherein the similarities/differences and the gaps with respect to the current studies. And, he has worked in the direction of extracting the inferences from the results section.
Mr. Murtuza Ahamed Khan Mohammed as there was a miscommunication about the consent for including name in the original manuscript, hence his name was not included. He has provided his consent only after initial submission of the manuscript.
(iii) Where is the theoretical and practical implications added???? Do not simply address the reviewer's comment like this!!
Ans: Thank you for your valuable input. We have provided theoretical and practical implications in Simulation and Performance analysis section (Section 4). The same content has been reproduced below for your kind reference.
- Simulation and Performance Analysis
4.1 Performance Metrics
The following performance metrics are considered in our QACM algorithm.
Signal Reliability (SR): It is the measure of reachability signal transmission in crowd sensing between source node/s to sink node. This metric is compared with pre-vious works and found that signal reliability increased significantly as Shown in Figure 2. And the signal strength increases linearly as from initiation phase to completion phase that involves the available wireless infrastructure at that instance based on the requirements. Transmission Time (TT): It is defined as the time taken to send the sensed event signal information to the sink through different intermediate sensing nodes. As shown in Figure 3. Crowd Sensing Efficiency (CSE): It is the level of crowd sensing efficiency based on different application requirements. Table 5 depicts the comparison of SR and TT Of QACM with QIMC and QOSA. Fig.3. Plotted with comparison values. The simulation results indicate that QACM demonstrates greater signal reliability overall, but specifically between 6000 and 14000s, its reliability is significantly higher than that of QIMC and QOSA. This is a clear indication of enhanced reliability due to the consideration of proper measures such as categorizing the mobile sensing node based on the parameter Rbd. Fig. 4. Shows the values of TT comparison values of QACM with the previous works of QICM and QOSA. There is remarkable changes TT because of the two param-eters of QACM Algorithm such as Tri and Trt. There highly reduced delay in transmission special due to application-based Transmission.
(iv) The reference [26] was added, however was not found in the main text. Please cite properly in the main text.
Ans: We apologies for this mistake. We have included in line number 32 – 33 in the revised manuscript.
(v) No in-depth comparison was really found in the study.
Ans: Thank you for your comment. The comparisons are found in the Table 5 of the manuscript. The relevant text has been reproduced below for your kind reference along with the Table.
|
Simulation Time |
SR |
TT |
||||
|
QIMC |
QOSA |
QACM |
QIMC |
QOSA |
QACM |
|
|
2000 |
0.41 |
0.51 |
0.59 |
0.89 |
0.91 |
0.76 |
|
4000 |
0.47 |
0.50 |
0.55 |
0.86 |
0.88 |
0.74 |
|
6000 |
0.49 |
0.52 |
0.59 |
0.84 |
0.86 |
0.72 |
|
8000 |
0.51 |
0.54 |
0.61 |
0.83 |
0.84 |
0.70 |
|
10000 |
0.53 |
0.57 |
0.65 |
0.81 |
0.83 |
0.69 |
|
12000 |
0.56 |
0.63 |
0.71 |
0.78 |
0.81 |
0.68 |
|
14000 |
0.58 |
0.65 |
0.74 |
0.76 |
0.79 |
0.67 |
Table 5 depicts the comparison of SR and TT Of QACM with QIMC and QOSA. Fig.3. Plotted with comparison values. The simulation results indicate that QACM demonstrates greater signal reliability overall, but specifically between 6000 and 14000s, its reliability is significantly higher than that of QIMC and QOSA. This is a clear indication of enhanced reliability due to the consideration of proper measures such as categorizing the mobile sensing node based on the parameter Rbd. We are very grateful for your comment. We are planning to conduct more comparative studies by considering various parameters in our extended version of this work.
Author Response File: Author Response.pdf
Round 3
Reviewer 2 Report
The authors have substantially addressed my comments. The revised paper is in good form for publication.
Author Response
We would like to thank you for your valuable comments and constructive suggestions, which helped to improve the quality of this manuscript.
