کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
407588 | 678158 | 2013 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A sensing and etiquette reputation-based trust management for centralized cognitive radio networks A sensing and etiquette reputation-based trust management for centralized cognitive radio networks](/preview/png/407588.png)
In centralized cognitive radio networks (CRNs), the secondary user base station (SUBS), as a fusion center for sensing data, makes spectrum allocation decisions based on the sensing information from secondary users (SUs) around. However, secondary users’ changeable environment and easy compromise make the SUBS vulnerable to sensing data falsification and primary user emulation attack, which will mislead its global decision making. In addition, after SUs acquire the right to use spectrum, they may behave maliciously out of selfishness or for other reasons. In this paper, we propose a novel trust management mechanism for CRNs. In the mechanism, we measure the trustworthiness of SUs in spectrum sensing and spectrum use with sensing reputation and etiquette reputation respectively in accordance with the two roles of SUs during the cognition cycle. Spectrum allocation schemes are built based on the two reputations. Specifically, the sensing reputation-based data fusion can effectively reduce the effect of users’ malicious behaviors on network decision making. And sensing reputation combined with etiquette reputation to act as the basis for spectrum allocation will encourage SUs to engage in positive and truthful sensing activities and obey spectrum use etiquette. Meanwhile, the probability of spectrum bands being allocated to malicious users will be reduced, which is of great benefit to the enhancement of the fairness and robustness of CRNs. Simulation results show that the sensing reputation-based data fusion can significantly reduce the SUBS’ error times in decision making and has strong anti-attack ability. The sensing and etiquette reputation-based multiple-factor resource allocation enables CRNs to adjust the resource allocation strategy flexibly according to the network's requirements on security and profits.
Journal: Neurocomputing - Volume 101, 4 February 2013, Pages 129–138