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Article
Peer-Review Record

Spatial-Temporal Value-of-Information Maximization for Mobile Crowdsensing in Wireless Sensor Networks

Electronics 2022, 11(19), 3224; https://doi.org/10.3390/electronics11193224
by Xiaoling Luo 1,2, Che Chen 3, Wenjie Zhang 3, Chunnian Zeng 1, Chengtao Li 2 and Jing Xu 4,*
Reviewer 1:
Reviewer 2:
Electronics 2022, 11(19), 3224; https://doi.org/10.3390/electronics11193224
Submission received: 2 August 2022 / Revised: 19 September 2022 / Accepted: 20 September 2022 / Published: 8 October 2022
(This article belongs to the Special Issue Wireless Powered Communications for Internet of Things)

Round 1

Reviewer 1 Report

The authors present a novel spatial-temporal crowdsensing model, aiming to maximize the value of information transmitted by the workers to the requester.

 

Overall the paper is well written with a few phrases that can be re-written to improve the understanding: lines 1-2 and line 40-41. Also, I would like to suggest the authors to replace some sentences or words to improve the scientific soundness, such as "efficacy" to effectiveness and "Unlike the above work" to "Comparing the previous works" (or something else in the same sense).

 

In terms of the paper structure, just a short note to point that the Introduction is quite extensive and should be more focused on the authors advances and novelties promoted by their work. The majority of the Introduction, lines 65 to 144, could be addressed in a new section with Related Work.

 

Section 2 presents the system model and although it is a quite clear presentation it lacks an association with practical examples. All the described application scenarios (Sum, Max and Min values) should be described with sensor networks examples of use.

 

Section 3 and 4 are both quite dense. The authors did omit some parts for brevity and, also, transferred some of the proofs to the Appendix but they should try to condense these sections into a single section.

 

Section 5 presents the results and discussion. Overall, the authors prove that the novel crowd sensing model works accordingly to what was designed in Section 2 but again the simulation results do not consider a practical example of model. The results are mostly numerically-based. The authors should address their model in one of the many existing sensor network simulation frameworks. Results are also limited to their model and not compared with other similar models, described in their related works review.

 

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

In current paper, the authors study a spatial-temporal system consisting of a mobile access point (MAP) and a series of wireless sensor nodes to optimize the utility of the information requester through various parameters and decisions.

To this end, I urge the authors to take into consideration the following points in order to improve their document.

Consider a more comprehensive abstract around 200 words.

Regarding the statement "In particular, in energy constrained wireless sensor networks, the information requester can transfer RF power to all sensing nodes by emitting RF signals. Via energy harvesting, the energy-limited sensor nodes will become active and have sufficient energy to report their sensing information to the MAP." in lines 190-192, it is not straight forward how this can be implemented, and a feasibility and deployment analysis is need on how to use RF technology cause it raises more limitations that you neglect.

What are the value space of parameter l and what happens in different cases. Also, picking a single value in simulation section can be consider as convenient, and thus i suggest presenting other cases too. The latter can also vary from sensor node to sensor node. This also holds for a parameter.

There is no link to an actual communication protocol that can implement the proposed protocol, not even mentioned., while the use of a custom made simulation rather than existing can be consider as a disadvantage. Also, we do not have any idea about the network deployment.

Distance D does not have measurement unit. If we assume that we are talking for a maximum 2 meters of distance, this is not really considered as crowdsensing as sparsity is limited, while also a mobile sink device is also found in typical wireless sensor networks. In such case you should also consider energy consumption due to mobility, and present different path decision approaches (which currently misses). Finally, as a reader I would like to see a benchmark with sota solutions so there is a direct comparison.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I am satisfied that all pointed issues were addressed as proposed. Just please correct this small issue:

Line 66 - "comparing the previous works", add *to* before "the previous works"

Author Response

Thanks for the new comment of the reviewer. I have double checked the manuscript and revised the mistakes. 
Refer to lines 66
“Comparing to the previous works, ... ”

Reviewer 2 Report

I hope the comments were useful for the authors, and i am satisfied that are addressed sufficiently.

Finally, regarding RF-based energy transfer technology, consider also works that exploit more realistic models rather than one-dimension ones for wireless energy transfer, such as "Power efficient algorithms for wireless charging under phase shift in the vector model", where power utilization is maximized.

Author Response

Thanks for the new comment of the reviewer. We have added references and
revised the sentences in the manuscript as follows:
“In many one-dimensional or vector models [31], the information requester
can transmit RF power to all sensing nodes by transmitting RF signals, and most of the energy harvesting process obeys Rayleigh distribution”
[31] Katsidimas, I.; Nikoletseas, S.; Raptopoulos, C. Power efficient algorithms for
wireless charging under phase shift in the vector model. 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) 2019 131–138.

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