Skip to main content
Log in

Secure centralized spectrum sensing for cognitive radio networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Spectrum utilization becomes more and more important while new communication techniques keep increasing and the spectrum bands remain finite. Cognitive radio is a revolutionary technology to make use of the spectrum more effectively. In order to avoid the interference to the primary user, spectrum sensing must be sensitive and reliable. Cooperative spectrum sensing (CSS) is one of the ways to increase the reliability of spectrum sensing. The information fusion technique is a key component of CSS. In this paper, we proposed a novel fusion scheme based on spatial correlation technique. We utilize geographical information with reputational weights to propose a two-level fusion scheme called secure centralized spectrum sensing (SCSS). The simulation results show that as the attackers present high density aggregation at some areas, the correct sensing ratio of SCSS is increasing as well even when the number of attackers is very large.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Article  Google Scholar 

  2. Khozeimeh, F., & Haykin, S. (2009). Dynamic spectrum management for cognitive radio: An overview. Wireless Communications and Mobile Computing, 9(11), 1447–1459.

    Google Scholar 

  3. Kay, S. M. (1998). Fundamentals of statistical signal processing: Detection theory. Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  4. Poor, H. V. (1994). An introduction to signal detection and estimation. New York: Springer.

    MATH  Google Scholar 

  5. Enserink, S., & Cochran, D. (1994). A cyclostationary feature detector. In Proceedings of 28th Asilomar conference on signals, systems, and computers, Pacific Grove, CA, Oct. 1994.

  6. Akyildiz, I. F., Lee, W.-Y., Vuran, M. C., & Mohanty, S. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50(13), 2127–2159.

    Article  MATH  Google Scholar 

  7. Chen, R., Park, J.-M., Hou, Y. T., & Reed, J. H. (2008). Toward secure distributed spectrum sensing in cognitive radio networks. IEEE Communications Magazine, 46(4), 50–55.

    Article  Google Scholar 

  8. Chen, R., Park, J.-M., & Bian, K. (2008). Robust distributed spectrum sensing in cognitive radio networks. In Proceedings of IEEE Infocom 2008 mini-conference, April 2008.

  9. Varshney, P. K. (1997). Distributed detection and data fusion. New York: Springer.

    Book  Google Scholar 

  10. Sun, H., Laurenson, D. I., Thompson, J., & Wang, C.-X. (2008). A novel centralized network for sensing spectrum in cognitive radio. In Proceedings of IEEE International Conference Communications, ICC 2008, Beijing, China, 19–23 May, 2008.

  11. Bettstetter, C., Resta, G., & Santi, P. (2003). The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Transactions Mobile Computing, 2(3), 257–269.

    Google Scholar 

  12. Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transaction Vehicular Technology, VT-29, 317–325.

    Google Scholar 

  13. Visotsky, E., Kuffner, S., & Peterson, R. (2005). On collaborative detection of TV transmissions in support of dynamic spectrum sharing. In Proceedings IEEE DySPAN, Nov. 2005, pp. 338–345.

  14. Lu, L., Chang, S.-Y., Zhang, J., Qian, L., Wen, J., Lau, V. K. N., Cheng, R. S., Murch, R. D., Mow, W. H., & Letaief, K. B. (2006). Technology Proposal Clarifications for IEEE 802.22 WRAN Systems, Mar. 2006.

  15. Hillenbrand, J., Weiss, T. A., & Jondral, F. K. (2005). Calculation of detection and false alarm probabilities in spectrum pooling systems. IEEE Communications Letters, 9(4), 349–351.

    Article  Google Scholar 

  16. Mitola III, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio, Ph.D. Thesis, KTH Royal Institute of Technology, 2000.

  17. Chen, C.-Y., Chou, Y.-H., Chao, H.-C., & Lo, C.-H. (2010). Toward secure centralized spectrum sensing by utilizing geographical information. The 5th International Conference on Future Information Technology (FutureTech 2010), Busan, Korea, May 21–23, 2010.

  18. Wassim, E., Haidar, S., & Mohsen, G. (2011). Survey of security issues in cognitive radio networks. Journal of Internet Technology, 12(2), 181–198.

    Google Scholar 

  19. Penna, F., & Garello, R. (2011). Detection of discontinuous signals for cognitive radio applications. IET Communications, 5(10), 1453–1461.

    Article  MathSciNet  Google Scholar 

  20. Shen, J., Liu, S., Zeng, L., Xie, G., Gao, J., & Liu, Y. (2009). Optimisation of cooperative spectrum sensing in cognitive radio network. IET Communications, 3(7), 1170–1178.

    Google Scholar 

Download references

Acknowledgments

The authors acknowledge Kuan-Cheng Ho for his contributions to the simulations. This research was partly funded by the National Science Council of the ROC under grants NSC 99-2219-E-197-001 and NSC 99-2219-E-197-002.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Han-Chieh Chao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, CY., Chou, YH., Chao, HC. et al. Secure centralized spectrum sensing for cognitive radio networks. Wireless Netw 18, 667–677 (2012). https://doi.org/10.1007/s11276-012-0426-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-012-0426-3

Keywords

Navigation