IMPROVED DISTRIBUTED COOPERATIVE SPECTRUM SENSING (DCSS) FOR COGNITIVE RADIO ADHOC NETWORK
DOI:
https://doi.org/10.20894/IJMSR.117.007.001.016Keywords:
Dynamic spectrum access, Cognitive radio ad hoc networks, Distributed consensus-based cooperative spectrum sensing, Trust management, Insistent spectrum sensing data falsification attackAbstract
Cooperation among cognitive radios for spectrum sensing is deemed essential for environments with deep shadows. In this paper, we study cooperative spectrum sensing for cognitive radio ad hoc networks where there is no fusion center to aggregate the information from various secondary users. We propose a novel consensus-inspired cooperative sensing scheme based on linear iterations that is fully distributed and low-cost. In addition, the trade-offs on the number of consensus iterations are explored for scenarios with different shadow fading characteristics. Furthermore, we model Insistent Spectrum Sensing Data Falsification (ISSDF) attack aimed at consensus-based iterative schemes and show its destructive effect on the cooperation performance which accordingly results in reduced spectrum efficiency and increased interference with primary users. We propose a trust management scheme to mitigate these attacks and evaluate the performance improvement through extensive Monte Carlo simulations for large-scale cognitive radio ad hoc networks in TV white space. Our proposed trust management reduces the harm of a set of collusive ISSDF attackers up to two orders of magnitude in terms of missed-detection and false alarm error rates. Moreover, in a hostile environment, integration of trust management into cooperative schemes considerably relaxes the sensitivity requirements on the cognitive radio devices.
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References
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[23] S. Liu, H. Zhu, S. Li, X. Li, C. Chen, and X. Guan, “An adaptive deviation-tolerant secure scheme for distributed cooperative spectrum sensing,” in Global Communications Conference (GLOBECOM), 2012 IEEE, Dec 2012, pp. 603–608.
[24] Q. Yan, M. Li, T. Jiang, W. Lou, and Y. Hou, “Vulnerability and protection for distributed consensus-based spectrum sensing in cognitive radio networks,” in INFOCOM, 2012 Proceedings IEEE, March 2012, pp. 900–908.
[2] Q. Wu, G. Ding, Y. Xu, S. Feng, Z. Du, J. Wang, and K. Long, “Cognitive internet of things: A new paradigm beyond connection,” Internet of Things Journal, IEEE, vol. 1, no. 2, pp. 129– 143, 2014.
[3] H. ElSawy and E. Hossain, “Two-tier hetnets with cognitive femtocells: Downlink performance modeling and analysis in a multichannel environment,” Mobile Computing, IEEE Transactions on, vol. 13, no. 3, pp. 649–663, March 2014.
[4] D. Cabric, S. Mishra, and R. Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” in Signals, Systems and Computers, 2004. Conference Record of the Thirty- Eighth Asilomar Conference on, vol. 1, Nov 2004, pp. 772–776 Vol.1.
[5] “Cognitive wireless RAN medium access control (MAC) and physical layer (PHY) specifications: Policies and procedures for operation in the TV bands,” IEEE Standard 802.22, 2011.
[6] L. Xiao, S. Boyd, and S. Lall, “A scheme for robust distributed sensor fusion based on average consensus,” in Proceedings of the 4th International Symposium on Information Processing in Sensor Networks. IEEE Press, 2005.
[7] D. Kempe, A. Dobra, and J. Gehrke, “Gossip-based computation of aggregate information,” in 44th Annual IEEE Symposium on Foundations of Computer Science Proceedings, Oct 2003, pp. 482–491.
[8] R. Olfati-Saber, J. Fax, and R. Murray, “Consensus and cooperation in networked multi- agent systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 215–233, Jan 2007.
[9] R. Chen, J.-M. Park, and K. Bian, “Robust distributed spectrum sensing in cognitive radio networks,” in INFOCOM. The 27th Conference on Computer Communications, April 2008, pp. 31–35.
[10] L. Xiao and S. Boyd, “Fast linear iterations for distributed averaging,” in 42nd IEEE Conference on Decision and Control, vol. 5, Dec 2003, pp. 4997–5002.
[11] N. Ahmed, D. Hadaller, and S. Keshav, “Guess: Gossiping updates for efficient spectrum sensing,” in Proceedings of the 1st International Workshop on Decentralized Resource Sharing in Mobile Computing and Networking. New York, NY, USA: ACM, 2006, pp. 12–17.
[12] A. Vosoughi, J. Cavallaro, and A. Marshall, “A cooperative spectrum sensing scheme for cognitive radio ad hoc networks based on gossip and trust,” in 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Dec 2014, pp. 1175–1179.
[13] Z. Li, F. R. Yu, and M. Huang, “A distributed consensus-based cooperative spectrum- sensing scheme in cognitive radios,” IEEE Transactions on Vehicular Technology, vol. 59, no. 1, pp. 383–393, 2010.
[14] W. Zhang, Y. Guo, H. Liu, Y. Chen, Z. Wang, and J. Mitola, “Distributed consensus-based weight design for cooperative spectrum sensing,” Parallel and Distributed Systems, IEEE Transactions on, vol. 26, no. 1, pp. 54–64, Jan 2015.
[15] S. Sundaram and C. Hadjicostis, “Distributed function calculation via linear iterations in the presence of malicious agents - part I: Attacking the network,” in American Control Conference, June 2008, pp. 1350– 1355.
[16] H. Zhang and S. Sundaram, “Robustness of complex networks with implications for consensus and contagion,” in Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, Dec 2012, pp. 3426– 3432.
[17] E. Yildiz, D. Acemoglu, A. E. Ozdaglar, A. Saberi, and A. Scaglione, “Discrete opinion dynamics with stubborn agents,” Available at SSRN: http://ssrn.com/abstract=1744113, Jan 2011.
[18] M. Pirani and S. Sundaram, “Spectral properties of the grounded laplacian matrix with applications to consensus in the presence of stubborn agents,” in American Control Conference (ACC), 2014, June 2014, pp. 2160–2165.
[19] J. Ghaderi and R. Srikant, “Opinion dynamics in social networks: A local interaction game with stubborn agents,” in American Control Conference (ACC), 2013, June 2013, pp. 1982– 1987.
[20] T. Qin, H. Yu, C. Leung, Z. Shen, and C. Miao, “Towards a trust aware cognitive radio architecture,” SIGMOBILE Mob. Comput. Commun. Rev., vol. 13, no. 2, pp. 86–95, Sep. 2009. [21] R. Zhang, J. Zhang, Y. Zhang, and C. Zhang, “Secure crowdsourcingbased cooperative spectrum sensing,” in INFOCOM, 2013 Proceedings IEEE, April 2013, pp. 2526–2534.
[22] S. Kalamkar, P. Singh, and A. Banerjee, “Block outlier methods for malicious user detection in cooperative spectrum sensing,” in Vehicular Technology Conference (VTC Spring), 2014 IEEE 79th, May 2014, pp. 1–5.
[23] S. Liu, H. Zhu, S. Li, X. Li, C. Chen, and X. Guan, “An adaptive deviation-tolerant secure scheme for distributed cooperative spectrum sensing,” in Global Communications Conference (GLOBECOM), 2012 IEEE, Dec 2012, pp. 603–608.
[24] Q. Yan, M. Li, T. Jiang, W. Lou, and Y. Hou, “Vulnerability and protection for distributed consensus-based spectrum sensing in cognitive radio networks,” in INFOCOM, 2012 Proceedings IEEE, March 2012, pp. 900–908.
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2015-12-16
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