Skip to main content
SpringerLink
Log in

An Improved Selfish Node Detection Algorithm for Cognitive Radio Mobile Ad Hoc Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Cognitive radio technology is aimed at utilizing the maximum available licensed spectrum for secondary users. However, the performance of a cognitive radio mobile ad hoc network can be degraded significantly due to selfish nodes that preoccupy idle licensed spectrum. Solving selfish attacks has become an important task in cognitive radio mobile ad hoc networks. We propose a selfish node detection algorithm that can solve the miss detection problem that happened with existing selfish node detection methods. The proposed algorithm reflects network errors to increase detection performance. In the simulation, our proposed algorithm is efficient and reliable for selfish node detection in various network conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

References

  1. Zhang, W., Mallik, R. K., & Letaief, K. B. (2009). Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks. IEEE Transactions on Wireless Communications, 8(9), 5761–5766. https://doi.org/10.1109/TWC.2009.12.081710

    Article  Google Scholar 

  2. Feng, J., Lu, G., & Min, X. (2014). Social incentives for cooperative spectrum sensing in distributed cognitive radio networks. KSII Transactions on Internet and Information Systems, 8(2), 134–144. https://doi.org/10.3837/tiis.2014.02.002

    Article  Google Scholar 

  3. Dai, J., & Wang, S. (2017). Clustering-based spectrum sharing strategy for cognitive radio networks. IEEE Journal on Selected Areas in Communications, 35(1), 228–237. https://doi.org/10.1109/JSAC.2016.2633698

    Article  Google Scholar 

  4. Muñoz, C., Rodriguez-Colina, E., Pedraza, L. F., & Paez, I. P. (2020). Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP. Journal on Wireless Communications Network. https://doi.org/10.1186/s13638-020-1657-0

    Article  Google Scholar 

  5. Kockaya, K., & Develi, I. (2020). Spectrum sensing in cognitive radio networks: threshold optimization and analysis. Journal on Wireless Communications and Networking. https://doi.org/10.1186/s13638-020-01870-7

    Article  Google Scholar 

  6. Lee, K. E., Park, J. G., & Yoo, S. J. (2021). Intelligent cognitive radio ad-hoc network: Planning, learning and dynamic configuration. Electronics, 10(3), 254. https://doi.org/10.3390/electronics10030254

    Article  Google Scholar 

  7. Hyder, C. S., Grebur, B., & Xiao, L. (2012). Defense against spectrum sensing data falsification attacks in cognitive radio networks. In M. Rajarajan, F. Piper, H. Wang, & G. Kesidis (Eds.), Security and Privacy in Communication Networks (LNICST) (pp. 154–171). Cham: Springer.

    Chapter  Google Scholar 

  8. Gao, Z., Zhu, H., Li, S., Du, S., & Li, X. (2012). Security and privacy of collaborative spectrum sensing in CRAHNs. IEEE Wirel Comm, 19(4), 106–112. https://doi.org/10.1109/MWC.2012.6393525

    Article  Google Scholar 

  9. Minho, J., Han, L., Dohoon, K., & Peter, H. I. (2013). Selfish attacks and detection in cognitive radio ad-hoc networks. IEEE Network, 27(3), 46–50. https://doi.org/10.1109/MNET.2013.6523808

    Article  Google Scholar 

  10. Tan, K., Jana, S., Pathak, P. H., & Mohapatra, P. (2013). On insider misbehavior detection in cognitive radio networks. IEEE Network, 27(3), 4–9. https://doi.org/10.1109/MNET.2013.6523801

    Article  Google Scholar 

  11. Zhang, L., Ding, G., Wu, Q., & Song, F. (2016). Defending against Byzantine attack in cooperative spectrum sensing: Defense reference and performance analysis. IEEE Access, 4, 4011–4024. https://doi.org/10.1109/ACCESS.2016.2593952

    Article  Google Scholar 

  12. Qiuming, L., Yong, L., Yun, L., & Jun, Z. (2016). Throughput capacity of selfish wireless ad hoc networks with general node density. Information, 7(1), 1–16. https://doi.org/10.3390/info7010016

    Article  Google Scholar 

  13. Al-Mathehaji, Y., Boussakta, S., Johnston, M., & Fakhrey, H. (2017). Defeating SSDF attacks with trusted nodes assistance in cognitive radio networks. IEEE Sensors Letters, 1(4), 1–4. https://doi.org/10.1109/LSENS.2017.2731623

    Article  Google Scholar 

  14. Nie, G., Ding, G., Zhang, L., & Wu, Q. (2017). Byzantine defense in collaborative spectrum sensing via Bayesian learning. IEEE Access, 5, 20089–20098. https://doi.org/10.1109/ACCESS.2017.2756992

    Article  Google Scholar 

  15. Zhao, F., Li, S., & Feng, J. (2019). Securing cooperative spectrum sensing against DC-SSDF attack using trust fluctuation clustering analysis in cognitive radio networks. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2019/3174304

    Article  Google Scholar 

  16. Yao, D., Yuan, S., Lv, Z., Wan, D., & Mao, W. (2020). An enhanced cooperative spectrum sensing scheme against SSDF attack based on Dempster-Shafer evidence theory for cognitive wireless sensor networks. IEEE Access, 8, 175881–175890. https://doi.org/10.1109/ACCESS.2020.3026738

    Article  Google Scholar 

  17. Xu, Z., Sun, Z., Guo, L., Muhammad, Z. H., & Chintha, T. (2022). Joint spectrum sensing and spectrum access for defending massive SSDF attacks: A novel defense framework. Chinese Journal of Electronics, 31, 240–254. https://doi.org/10.1049/cje.2021.00.090

    Article  Google Scholar 

  18. Fu, Y., & He, Z. (2023). Massive SSDF attackers identification in cognitive radio networks by using consistent property. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2023.3253865

    Article  Google Scholar 

  19. Mohsen, R. M., & Naima, K. (2018). Security threats and countermeasures of MAC layer in cognitive radio networks. Ad Hoc Networks, 70, 85–102. https://doi.org/10.1016/j.adhoc.2017.11.003

    Article  Google Scholar 

  20. Zhang, L., Nie, G., Ding, G., Wu, Q., Zhang, Z., & Han, Z. (2019). Byzantine attacker identification in collaborative spectrum sensing: A robust defense framework. IEEE Transactions on Mobile Computing, 18(9), 1992–2004. https://doi.org/10.1109/TMC.2018.2869390

    Article  Google Scholar 

  21. Fu, Y., & He, Z. (2019). Bayesian-inference-based sliding window trust model against probabilistic SSDF attack in cognitive radio networks. IEEE Systems Journal, 14(2), 1764–1775. https://doi.org/10.1109/JSYST.2019.2936263

    Article  Google Scholar 

  22. Singh, H., Kumar, V., Saxena, K., Boncho, B., & Prasad, R. (2020). Proposed model for radio wave attenuation due to rain (RWAR). Wireless Personal Communications, 115, 791–807. https://doi.org/10.1007/s11277-020-07598-3

    Article  Google Scholar 

  23. Khaled, H., Ahmad, I., Habibi, D., & Phung, Q. V. (2020). A secure and energy-aware approach for cognitive radio communications. IEEE Open Journal of the Communications Society, 1, 900–915. https://doi.org/10.1109/OJCOMS.2020.3009241

    Article  Google Scholar 

  24. Kumar, S., & Majhi, S. (2020). Blind symbol timing offset estimation for offset-QPSK modulated signals. ETRI Journal, 42, 324–332. https://doi.org/10.4218/etrij.2019-0199

    Article  Google Scholar 

  25. Singh, H., Prasad, R., & Bonev, B. (2018). The studies of millimeter waves at 60 GHz in outdoor environments for IMT applications: A state of art. Wireless Personal Communications, 100, 463–474. https://doi.org/10.1007/s11277-017-5090-6

    Article  Google Scholar 

  26. Fahiem, A., Kumar, P., & Soumyadev, M. (2022). Beacon non-transmission attack and its detection in intelligent transportation systems. Internet of Things. https://doi.org/10.1016/j.iot.2022.100602

    Article  Google Scholar 

  27. Pirayesh, H., & Zeng, H. (2022). Jamming attacks and anti-jamming strategies in wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 24(2), 767–809. https://doi.org/10.1109/COMST.2022.3159185

    Article  Google Scholar 

  28. Nidhi, A. M., & Prasad, R. (2020). Overview of 5G new radio and carrier aggregation: 5G and beyond networks. International Symposium on Wireless Personal Multimedia Communications, Okayama, Japan, pp 1–6. https://doi.org/10.1109/WPMC50192.2020. 9309496

  29. Quy, V. K., Nam, V. H., Linh, D. M., Ngoc, L. A., & Gwanggil, J. (2022). Wireless communication technologies for IoT in 5G: Vision, applications, and challenges. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2022/3229294

    Article  Google Scholar 

  30. Xiaozhen, L., Liang, X., Pengmin, L., Xiangyang, J., Chenren, X., Shui, Y., & Weihua, Z. (2023). Reinforcement learning-based physical cross-layer security and privacy in 6G. IEEE Communications Surveys & Tutorials, 25(1), 425–466. https://doi.org/10.1109/COMST.2022.3224279

    Article  Google Scholar 

  31. Quy, V. K., Chehri, A., Quy, N. M., Han, N. D., & Ban, N. T. (2023). Innovative trends in the 6G Era: A comprehensive survey of architecture, applications, technologies, and challenges. IEEE Access, 11, 39824–39844. https://doi.org/10.1109/ACCESS.2023.3269297

    Article  Google Scholar 

Download references

Acknowledgements

N/A

Funding

N/A

Author information

Authors and Affiliations

  1. Hung Yen University of Technology and Education, Hungyen, 160000, Vietnam

    Vu Khanh Quy, Van-Hau Nguyen & Dao Manh Linh

  2. Posts and Telecommunications Institute of Technology, Hanoi, 100000, Vietnam

    Nguyen Tien Ban

  3. Hanoi University of Science and Technology, Hanoi, 100000, Vietnam

    Nguyen Dinh Han

Authors
  1. Vu Khanh Quy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Van-Hau Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dao Manh Linh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nguyen Tien Ban
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nguyen Dinh Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The study conception and methodology are performed by V.K. Quy and N.D. Han. Material preparation, data collection and analysis were performed by V.K. Quy, N.V. Hau, D.M. Linh, N.T. Ban, and N.D. Han. The first draft of the manuscript was written by V.K. Quy and N.D. Han. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The corresponding author is Dr. Nguyen Dinh Han.

Corresponding author

Correspondence to Nguyen Dinh Han.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Quy, V.K., Nguyen, VH., Linh, D.M. et al. An Improved Selfish Node Detection Algorithm for Cognitive Radio Mobile Ad Hoc Networks. Wireless Pers Commun 133, 683–697 (2023). https://doi.org/10.1007/s11277-023-10788-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10788-4

Keywords

Search

Navigation