An Efficient Collaborative Spectrum Sensing Method in Cognitive Radio Networks: Software-Defined Data Fusion Approach

  • Abbas Ali Sharifi University of Bonab
  • Hamed Alizadeh Ghazijahani
Keywords: Cognitive Radio (CR), Cooperative Spectrum Sensing, Software-Defined

Abstract

Cognitive Radio (CR) technology has been suggested as a solution to the serious problem of spectrum scarcity in recent years. Cooperative Spectrum Sensing (CSS) is the key function to overcome the destructive effect of hidden station, multipath fading and shadowing problems. As many previous studies have shown, the trustworthiness of the CSS can be strictly degraded under Spectrum Sensing Data Falsification (SSDF) attack. In this paper, we introduce an important dynamic fusion rule called Software-Defined CSS (SD-CSS). The main contribution is to analyze the SSDF attack strategy against the CR network and apply the best fusion rule to increase the cooperative sensing performance. Two important SSDF attack parameters, attack strategy and attack ratio, are estimated and the obtained parameters are then used to choose an appropriate fusion rule to improve the CSS performance. The obtained results confirm considerable improvement in correct sensing ratio in massive attack.

References

R. Chen, J.-M. J. Park, and K. Bian, “Robustness against Byzantine failures in distributed spectrum sensing,” Computer Communications, vol. 35, pp. 2115-2124, 2012.

A. A. Sharifi, J. Musevi Niya, and H. Alizadeh Ghazijahani, “Secure collaborative spectrum sensing for distributed cognitive radio networks,” Majlesi Journal of Electrical Engineering, vol. 9, pp. 59-66, 2015.

S. Kumar, J. Sahay, G. K. Mishra, and S. Kumar, “Cognitive radio concept and challenges in dynamic spectrum access for the future generation wireless communication systems,” Wireless Personal Communications, vol. 59, pp. 525-535, 2011.

W. Wang, H. Li, Y. L. Sun, and Z. Han, “Securing collaborative spectrum sensing against untrustworthy secondary users in cognitive radio networks,” EURASIP Journal on Advances in Signal Processing, vol. 2010, p. 695750, 2009.

M. A. Abdulsattar and Z. A. Hussein, “Energy detection technique for spectrum sensing in cognitive radio: a survey,” International Journal of Computer Networks & Communications, vol. 4, p. 223, 2012.

S. Maric, S. Reisenfeld, and L. Goratti, “A simple and highly effective SSDF attacks mitigation method,” in Signal Processing and Communication Systems (ICSPCS), 10th International Conference on, pp. 1-7, 2016.

H. Li and Z. Han, “Catch me if you can: An abnormality detection approach for collaborative spectrum sensing in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol. 9, pp. 3554-3565, 2010.

W. Wang, H. Li, Y. Sun, and Z. Han, “Attack-proof collaborative spectrum sensing in cognitive radio networks,” in Information Sciences and Systems, CISS, 43rd Annual Conference on, pp. 130-134, 2009.

Q. Pei, B. Yuan, L. Li, and H. Li, “A sensing and etiquette reputation-based trust management for centralized cognitive radio networks,” Neurocomputing, vol. 101, pp. 129-138, 2013.

I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer networks, vol. 50, pp. 2127-2159, 2006.

H. Alizadeh et al. “Attack-Aware Cooperative Spectrum Sensing in Cognitive Radio Networks under Byzantine Attack,” Journal of Communication Engineering, vol. 6, pp. 81-98, 2017.

H. Chen, M. Zhou, L. Xie, and J. Li, “Cooperative Spectrum Sensing with M-ary Quantized Data in Cognitive Radio Networks under SSDF Attacks,” IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 5244-5257, 2017.

H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceedings of the IEEE, vol. 55, pp. 523-531, 1967.

G. Chouinard. IEEE P802.22 Wireless RANs: Mminutes of Channel Model Subgroup Teleconference, 2005. Available: http://www.ieee802.org/22/

T. S. Rappaport, Wireless communications: principles and practice vol. 2: Prentice Hall PTR New Jersey, 1996.

R. Chen, J.-M. Park, and K. Bian, “Robust distributed spectrum sensing in cognitive radio networks,” in INFOCOM 2008. The 27th Conference on Computer Communications. pp. 1876-1884, 2008.

P. K. Varshney, Distributed detection and data fusion: Springer Science & Business Media, 2012.

L. Zhang, Q. Wu, G. Ding, S. Feng, and J. Wang, “Performance analysis of probabilistic soft SSDF attack in cooperative spectrum sensing,” EURASIP Journal on Advances in Signal Processing, vol. 2014, p. 81, 2014.

C. S. Hyder, B. Grebur, L. Xiao, and M. Ellison, “ARC: Adaptive reputation based clustering against spectrum sensing data falsification attacks,” IEEE Transactions on mobile computing, vol. 13, pp. 1707-1719, 2014.

C. Bettstetter, G. Resta, and P. Santi, “The node distribution of the random waypoint mobility model for wireless ad hoc networks,” IEEE Transactions on mobile computing, vol. 2, pp. 257-269, 2003.

L. Gavrilovska and V. Atanasovski, “Spectrum sensing framework for cognitive radio networks,” Wireless Personal Communications, vol. 59, pp. 447-469, 2011.

Q. Pei, H. Li, and X. Liu, “Neighbor Detection-Based Spectrum Sensing Algorithm in Distributed Cognitive Radio Networks,” Chinese Journal of Electronics, vol. 26, pp. 399-406, 2017.

Published
2018-12-01
How to Cite
Sharifi, A. A., & Alizadeh Ghazijahani, H. (2018). An Efficient Collaborative Spectrum Sensing Method in Cognitive Radio Networks: Software-Defined Data Fusion Approach. Majlesi Journal of Telecommunication Devices, 7(4), 171-177. Retrieved from http://journals.iaumajlesi.ac.ir/td/index/index.php/td/article/view/483
Section
Articles