A Repeated Games-Based Secure Multiple-Channels Communications Scheme for Secondary Users with Randomly Attacking Eavesdroppers

Round 1

Reviewer 1 Report

The authors propose a technique to mitigate eavesdropping, repeated-game based scheme that selects the best channel for secondary users. The major weaknesses of the paper are the weak literature review and the quality of the presentation. 

Major Revisions

1.      The first sentence of in the introduction is very long.

2.      Unlicensed users are often called secondary users. Are the authors refereeing to secondary users as cognitive radio users (CUs) ? If yes, the authors should change that to secondary users because cognitive radio users include both the primary users and secondary users.

3.      The literature review in this paper is very weak. The authors neglected some important work regarding channel selection. The authors must provide a substantial literature review and then highlight the weaknesses related to related work to justify the contributions and the work done in this research paper.

a.      Zhai, Y.B.; Wu, X.; Huang, X.L.; Wu, J. Channel Quality Ranking in Cognitive Radio Networks. In Proceedings of the Wireless Communications, Networking and Mobile Computing Conference, Beijing, China, 26–28 September 2014; pp. 191–194.

b.      Arjoune, Y; Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks, Applied Sciences 2018, 8(4), 628; doi:10.3390/app8040628

c.      Sakhare, A.; Hwang, M.; Suh, D.Y. A novel channel indexing-based channel selection algorithm for cognitive radio networks. In Proceedings of the IEEE ICT Convergence Conference, Jeju, Korea, 14–16 October 2013; pp. 682–687.

d.      Torabi, N.; Rostamzadeh, K.; Leung, V.C. Rank-optimal channel selection strategy in cognitive networks. In Proceedings of the IEEE Global Communications Conference, Anaheim, CA, USA, 3–7 December 2012; pp. 410–415.

e.      Xing, X.; Jing, T.; Huo, Y.; Li, H.; Cheng, X. Channel quality prediction based on Bayesian inference in cognitive radio networks. In Proceedings of the IEEE Proceedings INFOCOM, Turin, Italy, 14–19 April 2013; pp. 1465–1473.

f.       Sengottuvelan, S.; Ansari, J.; Mähönen, P.; Venkatesh, T.G.; Petrova, M. Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times. IEEE Trans. Mob. Comput. 2017, 16, 1258–1271.

g.      Aslam, S.; Lee, K.G. CSPA: Channel selection and parameter adaptation scheme based on genetic algorithm for cognitive radio ad hoc networks. EURASIP J. Wirel. Commun. Netw. 2012, 2012–2349.

4.      The system model provided in this paper is not supported by references. Please give some references from which the authors got this model.

5.      The authors need to provide the mathematical expression related to Markov chain.

6.      The authors need to provide a more descriptive caption for Figure 2.

7.      The sensing technique used is not described.

8.      Definitions of the evaluation metrics should be given before using them and their importance in this context too should be highlighted.

9.      The conclusion is very short, and it does not reflect the importance of the problem and its solution.

10.   The authors need also to acknowledge the limitations of the proposed schemes and provide future research directions

11.   The provided mechanism is not evaluated in the context of the Internet of Things. The paper provides only some simulation results and those simulations do not consider specific characteristic of the Internet of Things such as limited energy and computational resources. The authors must provide a complexity study of the algorithm and show that the provided schemes can be used for IoT networks.

Author Response

Dear Reviewer;

Thanks to the reviewers’ valuable comments that help to improve the paper’s quality, the paper has been extensively revised according to the reviewers’ comments. 

BR/Insoo Koo. 

Author Response File: Author Response.pdf

Reviewer 2 Report

In this work, a technique for securing the transmission of multiple Cognitive users when are randomly attacked by eavesdroppers is proposed. The proposed solutions are given via game-based algorithms whose performance is evaluated via simulations. I have the following comments.

1.    The input-output relationship of the system model is not given along with important information, like what is the assumed models for the channels. Please revise accordingly.

2.    To me, it is not clear what the cognitive radio setup is bringing on the problem here. What are the differences of the proposed algorithms if no Primary users are presented on the wireless environment. Has the latter case been already considered in the literature? Please elaborate more.

3.    The computation complexity analysis of the proposed approaches should be provided.

4.    In the simulations, apart from the secrecy rate, it is interesting to show the actual rate that the CUs achieve when the proposed approaches are applied and compare it to the case where no measurements for the eavesdroppers are considered (benchmark case).

5.    The literature survey of the cognitive radio techniques is quite dated. Please find below some recommended recent works

[R1] C. Politis, S. Maleki, C. G. Tsinos, K. P. Liolis, S. Chatzinotas and B. Ottersten, "Simultaneous Sensing and Transmission for Cognitive Radios With Imperfect Signal Cancellation," in IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 5599-5615, Sept. 2017.

[R2] C. G. Tsinos and K. Berberidis, "Decentralized Adaptive Eigenvalue-Based Spectrum Sensing for Multiantenna Cognitive Radio Systems," in IEEE Transactions on Wireless Communications, vol. 14, no. 3, pp. 1703-1715, March 2015.

[R3] C. G. Tsinos and K. Berberidis, "Blind Opportunistic Interference Alignment in MIMO Cognitive Radio Systems," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 3, no. 4, pp. 626-639, Dec. 2013.

Author Response

Dear Reviewer;

Thanks to the reviewers’ valuable comments that help to improve the paper’s quality, the paper has been extensively revised according to the reviewers’ comments. 

BR/Insoo Koo. 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The major weaknesses of this paper is the evaluation of the proposed methods. The authors tried to address some problems that the reviewers raised in the last round of review, but here are still a few comments that need to be addressed. These comments are the following:

1.      The authors neglected some important work regarding channel selection. The authors must provide a substantial literature review and then highlight their weaknesses to justify the contributions in this research paper. It is true that the authors included some suggested papers but omitted to include others such as.

a.      Arjoune, Y; Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks, Applied Sciences 2018, 8(4), 628; doi:10.3390/app8040628

b.      Sengottuvelan, S.; Ansari, J.; Mähönen, P.; Venkatesh, T.G.; Petrova, M. Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times. IEEE Trans. Mob. Comput. 2017, 16, 1258–1271.

c.      Aslam, S.; Lee, K.G. CSPA: Channel selection and parameter adaptation scheme based on genetic algorithm for cognitive radio ad hoc networks. EURASIP J. Wirel. Commun. Netw. 2012, 2012–2349.

2.      The authors need to provide a definition of the evaluation metrics before using them. They need also to highlight their importance in this context too.

3.      The provided mechanism is not evaluated in the context of the Internet of Things. Only the simulation results are provided and even those simulations did not consider specific characteristic of the Internet of Things such as limited energy and computational resources. The authors must also provide a complexity study of the algorithm and show that the provided schemes can be used for IoT networks.

Author Response

Dear Reviewer; 

Thanks to the reviewers’ valuable comments that help to improve the paper’s quality, the paper has been extensively revised according to the reviewers’ comments.

Please find the attached file. 

Author Response File: Author Response.pdf

Round 3

Reviewer 1 Report

All comments have been addressed by the authors.