PONE-D-22-12942A computational offloading optimization scheme based on deep reinforcement learning in perceptual networkPLOS ONE

Dear Dr. Waqas,

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper presents a nice idea on using deep reinforcement learning to investigate a computation offloading optimization scheme for the perception layer. I think it is an interesting approach, have enough novelty, and the paper is on the whole quite well written.

The issues to be addressed are:

Please clarify the difference between your proposed algorithm and the idea of reference [10].

Show the impact of data block size and block interval (from parameter settings) on average delay.

In addition to the average delay, show the impact of the parameters (at least one of them) on the throughput.

Reviewer #2: This paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer.

Although the work has potential, several major concerns need to be addressed before the paper can be accepted for publication:

- The optimization problem in Section 3.1.3 is not clear. Specifically, it is not clear from Equation (6) what the authors are trying to optimize. How can the task completion rate be optimized by maximizing the local and service execution times?

- Moreover, no constraints are defined for the optimization problem, which makes it incomplete.

- It is not clear what novel contributions the authors bring by employing Deep Reinforcement Learning (DRL) for offloading. Is the internal structure of DRL tuned for the considered problem or is it just used as a blackbox approach?

- Important literature on offloading/scheduling is missing such as:

- Ad hoc vehicular fog enabling cooperative low-latency intrusion detection. IEEE Internet of Things Journal, 8(2),

829-843 (2020).

- Intelligent task prediction and computation offloading based on mobile-edge cloud computing. Future Generation

Computer Systems, 102, 925-931 (2020).

- Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Transactions on

Services Computing, 12(5), 726-738 (2018).

- A trust and energy-aware double deep reinforcement learning scheduling strategy for federated learning on IoT

devices. In International Conference on Service-Oriented Computing (pp. 319-333) (2020).

- What is the computational complexity of Algorithm 1? Can it be used to make real-time offloading decisions?

- The environment within which the simulations have been done needs to be further clarified, i.e., programming language, framework, resource specifications, etc.

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Reviewer #1: No

Reviewer #2: No

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A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

PONE-D-22-12942R1

Dear Dr. Waqas,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Mahdi Abbasi, PhD.

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have adequately addressed the comments and I feel that this manuscript is now acceptable for publication.

Reviewer #3: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

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