Review Reports - Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network

Round 1

Reviewer 1 Report

In this work, authors present a blockchain assisted adaptive reconfiguration method for trusted UAV network. Paper is nicely written and presented. However, before acceptance, there are several points that need to be addressed. Here is a list of those points.

11. Authors need to thoroughly check the quality of grammar. Authors need to rigorously review the paper and check for mistakes. For example, some sentences are very long. On page 1, a sentence starts at line 28 and goes all the way until line 35. Same comment applies to many other sentences in subsequent pages.

22. References are not numbered in chronological order. The order should be observed.

33. The related work section needs more detailed comparison with existing state of the art. The novelty of proposed work should be highlighted and explicitly compared with the existing work.

44. The system model lacks necessary technical details. It needs to be discussed in detail with all in-depth technical details. Moreover, authors also need to discuss how it is different from other existing models?

55. Results need to be discussed in more detail. For example, you need to discuss in figure 6 that despite the increase in number of error nodes, how the proposed technique maintains a low average end to end delay? This is a bit surprising for me. For the other techniques under consideration, this delay goes up but not for the proposed one?

66. The energy consumption results for other techniques should also be presented.

Author Response

Please see the attachment，thank you！

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors proposed a lightweight blockchain system with temporary lightweight storage in the UAV network, starting with mission generation and ending with mission completion. The idea is interesting. However, I have the following concerns.

1. Please revise the grammatical errors of this paper.

2. Recent blockchain-based UAV research are missing. Some are mentioned as follows.

-> "Blockchain-Enhanced Spatiotemporal Data Aggregation for UAV-Assisted Wireless Sensor Networks," in IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4520-4530, July 2022, doi: 10.1109/TII.2021.3120973.

-> "FBI: A Federated Learning-Based Blockchain-Embedded Data Accumulation Scheme Using Drones for Internet of Things," in IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 972-976, May 2022, doi: 10.1109/LWC.2022.3151873.

3. Fig improve quality of Fig. 1,2,3

4. Add a summary table for section 2 including their limitations.

5. A complexity analysis is required.

6. Add details in Algorithm 1, 2, 3.

7. Which blockchain is considered during experiments? Important metrics regarding blockchain are missing, for example, latency for adding blockchain.

8. A scalability analysis is required.

9. A threat model is missing. A security analysis is required based on the threat model.

10. The proof for Theorem 3_1 is missing.

11. Theorem and definition numbering are wrong.

12. How data is stored in the blockchain is missing.

Author Response

Please seetheattachment， thank you.

Author Response File: Author Response.pdf

Reviewer 3 Report

- Please fix this typo in the abstract (QualNe--> Qualnet)

- The paper needs careful proof reading and language checking.

-What is the motivation behind implementing blockchain with a UAV network?

- Will the energy limitation of the UAVs affect the proposed BC_TZRP performance? if yes, explain remedies for this challenge.

- Can you discuss why the curve of BC_TZRP in Figs 5 and 6 are constant and does not change with increasing the malicious nodes, while the other algorithms change with the number of malicious nodes?

Author Response

Please see the attachment, thank you.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Authors have addressed most of the previously raised comments. Authors have added significant new details. Authors have also added a new section and have given more details about some previous sections. I believe the paper can be accepted in its current form.

Author Response

Thank you very much for your approval!

Reviewer 2 Report

I am recommending to accept this paper.

Author Response

Your comments have been a great help in improving this paper. Thank you very much for your recognition of our efforts.

Reviewer 3 Report

The authors improved the paper and addressed my comments. However, please ask the author to add the response they put for my comment (comment 4) to the revised paper. I am referring to the below comment:

Comment 4. Can you discuss why the curve of BC_TZRP in Figs 5 and 6 are constant and does not change with increasing the malicious nodes, while the other algorithms change with the number of malicious nodes?

Also, can you please clarify why the works the authors are used for the comparison have different behaviour then the proposed work in this paper,

Author Response

Response：Your comments have helped us a lot to improve our paper, thank you very much. The response section of comment 4 we will integrate into the revised manuscript.

Also, can you please clarify why the works the authors are used for the comparison have different behaviour then the proposed work in this paper,

Response：We offer the following explanation to your above question.

1, the configuration information of the UAV network networking in this method is saved in the blockchain, and in the route discovery phase, the selection of nodes must first meet the network configuration information in the blockchain chain. Nodes that are not members of the network or have been included in the untrustworthy blacklist with a legitimate identity will be directly excluded.

2, nodes are identified as untrustworthy, recorded to the blockchain through consensus, and eventually isolated from the network is the process, especially the nodes that do not tamper with the information conspiracy to lie. However, the scheme sets a shorter consensus cycle and if it has been found to be untrustworthy at the time of local detection, consensus is initiated in advance. The malicious nodes in the experimental design of this scheme are nodes with tampering behavior and selfish behavior, and their untrustworthiness is quickly detected and recorded to the blockchain, thus the chance of participating in routing is low.

A big difference between this scheme and other schemes is whether the malicious nodes remain in the network. This scheme continuously reorganizes the network by adding new blocks to the blockchain, and the probability of malicious nodes participating in network transmission is small. In contrast, the malicious nodes in its scheme are not tagged and still participate in the network activities. Therefore the probability of being involved in each routing and being a member of the transmission path is still high, so the interference to the network remains.