ABSTRACT
In a cognitive radio network (CRN), energy detection is one of the most efficient spectrum sensing techniques for the protection of legacy spectrum users, with which the presence of primary users (PUs) can be detected promptly, allowing secondary users (SUs) to vacate the channels immediately. In this paper, we design a novel trust based energy detection model for CRNs. This model extends the widely used energy detection and employs the idea of a trust model to perform spectrum sensing in the CRN. In this model, trust among SUs is represented by opinion, which is an item derived from subjective logic. The opinions are dynamic and updated frequently: If one SU makes a correct decision, its opinion from other SUs' point of view can be increased. Otherwise, if an SU exhibits malicious behavior, it will be ultimately denied by the whole network. A trust recommendation is also designed to exchange trust information among SUs. The salient feature of our trust based energy detection model is that using trust relationships among SUs, this guarantees only reliable SUs will participate in generating a final result. This greatly reduces the computation overheads. Meanwhile, with neighbors' trust recommendations, a SU can make objective judgment about another SU's trust-worthiness to maintain the whole system at a certain reliable level.
- A. Abdul-Rahman and S. Hailes. A distributed trust model. In Proceedings of the 1997 workshop on New security paradigms, pages 48--60. ACM, 1998. Google ScholarDigital Library
- T. Beth, M. Borcherding, and B. Klein. Valuation of trust in open networks. Springer, 1994.Google ScholarCross Ref
- D. Cabric, S. M. Mishra, and R. W. Brodersen. Implementation issues in spectrum sensing for cognitive radios. In Signals, systems and computers, 2004. Conference record of the thirty-eighth Asilomar conference on, volume 1, pages 772--776. IEEE, 2004.Google Scholar
- D. Cabric, A. Tkachenko, and R. W. Brodersen. Spectrum sensing measurements of pilot, energy, and collaborative detection. In Military communications conference, 2006. MILCOM 2006. IEEE, pages 1--7. IEEE, 2006. Google ScholarDigital Library
- R. Chen. Enhancing attack resilience in cognitive radio networks. 2008.Google Scholar
- G. Ganesan and Y. Li. Agility improvement through cooperative diversity in cognitive radio. In Global Telecommunications Conference, 2005. GLOBECOM'05. IEEE, volume 5, pages 5--pp. IEEE, 2005.Google Scholar
- N. Hoven, R. Tandra, and A. Sahai. Some fundamental limits on cognitive radio. Wireless Foundations EECS, Univ. of California, Berkeley, 2005.Google Scholar
- F. Jin, V. Varadharajan, and U. Tupakula. Improved detection of primary user emulation attacks in cognitive radio networks. In Telecommunication Networks and Applications Conference (ITNAC), 2015 International, pages 274--279. IEEE, 2015. Google ScholarDigital Library
- A. Jøsang. Prospectives for modelling trust in information security. In Information Security and Privacy, pages 2--13. Springer, 1997. Google ScholarDigital Library
- A. Jøsang. A subjective metric of authentication. In Computer Security---ESORICS 98, pages 329--344. Springer, 1998. Google ScholarDigital Library
- A. Jøsang. A logic for uncertain probabilities. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(03):279--311, 2001. Google ScholarDigital Library
- S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina. The eigentrust algorithm for reputation management in p2p networks. In Proceedings of the 12th international conference on World Wide Web, pages 640--651. ACM, 2003. Google ScholarDigital Library
- J. Lehtomaki, M. Juntti, H. Saarnisaari, and S. Koivu. Threshold setting strategies for a quantized total power radiometer. IEEE Signal Processing Letters, 12(11):796, 2005.Google ScholarCross Ref
- X. Li, M. R. Lyu, and J. Liu. A trust model based routing protocol for secure ad hoc networks. In Aerospace Conference, 2004. Proceedings. 2004 IEEE, volume 2, pages 1286--1295. IEEE, 2004.Google Scholar
- M. P. Olivieri, G. Barnett, A. Lackpour, A. Davis, and P. Ngo. A scalable dynamic spectrum allocation system with interference mitigation for teams of spectrally agile software defined radios. In New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, pages 170--179. IEEE, 2005.Google ScholarCross Ref
- S. Parvin, S. Han, L. Gao, F. Hussain, and E. Chang. Towards trust establishment for spectrum selection in cognitive radio networks. In Advanced Information Networking and Applications (AINA), 2010 24th IEEE International Conference on, pages 579--583. IEEE, 2010. Google ScholarDigital Library
- Q. Pei, R. Liang, and H. Li. A trust management model in centralized cognitive radio networks. In Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2011 International Conference on, pages 491--496. IEEE, 2011. Google ScholarDigital Library
- D. Pu, Y. Shi, A. V. Ilyashenko, and A. M. Wyglinski. Detecting primary user emulation attack in cognitive radio networks. In Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE, pages 1--5. IEEE, 2011.Google Scholar
- T. Qin, H. Yu, C. Leung, Z. Shen, and C. Miao. Towards a trust aware cognitive radio architecture. ACM SIGMOBILE Mobile Computing and Communications Review, 13(2):86--95, 2009. Google ScholarDigital Library
- A. Sahai and D. Cabric. Spectrum sensing: fundamental limits and practical challenges. In Proc. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), 2005.Google Scholar
- N. Sai Shankar, C. Cordeiro, and K. Challapali. Spectrum agile radios: utilization and sensing architectures. In New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on, pages 160--169. IEEE, 2005.Google ScholarCross Ref
- H. Tang. Some physical layer issues of wide-band cognitive radio systems. In New frontiers in dynamic spectrum access networks, 2005. DySPAN 2005. 2005 first IEEE international symposium on, pages 151--159. IEEE, 2005.Google Scholar
- Y. Teng, V. Phoha, and B. Choi. Design of trust metrics based on dempstershafer theory, 2000.Google Scholar
- H. Urkowitz. Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4):523--531, 1967.Google ScholarCross Ref
- F. Weidling, D. Datla, V. Petty, P. Krishnan, and G. Minden. A framework for rf spectrum measurements and analysis. In First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005., 2005.Google ScholarCross Ref
- R. Yahalom, B. Klein, and T. Beth. Trust relationships in secure systems-a distributed authentication perspective. In Research in Security and Privacy, 1993. Proceedings., 1993 IEEE Computer Society Symposium on, pages 150--164. IEEE, 1993. Google ScholarDigital Library
- Y. Yuan, P. Bahl, R. Chandra, P. Chou, J. I. Ferrell, T. Moscibroda, S. Narlanka, Y. Wu, et al. Knows: Cognitive radio networks over white spaces. In New Frontiers in Dynamic Spectrum Access Networks, 2007. DySPAN 2007. 2nd IEEE International Symposium on, pages 416--427. IEEE, 2007. Google ScholarDigital Library
- T. Yücek and H. Arslan. A survey of spectrum sensing algorithms for cognitive radio applications. Communications Surveys & Tutorials, IEEE, 11(1):116--130, 2009. Google ScholarDigital Library
- A trust model based energy detection for cognitive radio networks
Recommendations
Continuous Spectrum Sensing in Cognitive Radio Networks
BCGIN '12: Proceedings of the 2012 Second International Conference on Business Computing and Global InformatizationIn cognitive radio (CR) systems, secondary users (SUs) have to detect channel periodically during their data transmission to decide whether the channel is idle in order to avoid unacceptable interferences to primary users (Pus). With the traditional ...
Cooperative Bayesian‐based detection framework for spectrum sensing in cognitive radio networks
In this study, a cognitive radio network is considered in which multiple secondary users intend to detect a primary user frequency band in order to specify whether it is occupied or not. To this end, a blind Bayesian framework is proposed by which ...
Fault Tolerant Spectrum Assignment in Cognitive Radio Networks
AbstractThe cognitive radio network (CRN) has been regarded as a promising approach to enhance the spectrum utilization. It allows unlicensed secondary users (SUs) to opportunistically share unused spectrum bands with licensed primary users (PUs). ...
Comments