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dc.contributor.authorSimpson, Oluyomi
dc.contributor.authorSun, Yichuang
dc.date.accessioned2020-10-21T14:15:01Z
dc.date.available2020-10-21T14:15:01Z
dc.date.issued2020-07-27
dc.identifier.citationSimpson , O & Sun , Y 2020 , Robust Statistics Evidence Based Secure Cooperative Spectrum Sensing for Cognitive Radio Networks . in 2020 International Wireless Communications and Mobile Computing Conference, IWCMC 2020 . 2020 International Wireless Communications and Mobile Computing Conference, IWCMC 2020 , Institute of Electrical and Electronics Engineers (IEEE) , 16th IEEE International Wireless Communications and Mobile Computing Conference , 15/06/20 . https://doi.org/10.1109/IWCMC48107.2020.9148381
dc.identifier.citationconference
dc.identifier.isbn9781728131290
dc.identifier.urihttp://hdl.handle.net/2299/23296
dc.description© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractCognitive radio networks (CRNs), an assemble of smart schemes intended for permitting secondary users (SUs) to opportunistically access spectral bands vacant by primary user (PU), has been deliberated as a solution to improve spectrum utilization. Cooperative spectrum sensing (CSS) is a vital technology of CRN systems used to enhance the PU detection performance by exploiting SUs' spatial diversity, however CSS leads to spectrum sensing data falsification (SSDF), a new security threat in CR system. The SSDF by malicious users can lead to a decrease in CSS performance. In this work, we propose a CSS scheme in which the presence and absence hypotheses distribution of PU signal is estimated based on past sensing received energy data incorporating robust statistics, and the data fusion are performed according to an evidence based approach. Simulation results show that the proposed scheme can achieve a significant malicious user reduction due to theabnormality of the distribution of malicious users compared with that of other legitimate users. Furthermore, the performance of our data fusion scheme is improved by supplemented nodes' credibility weight.en
dc.format.extent7
dc.format.extent397778
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof2020 International Wireless Communications and Mobile Computing Conference, IWCMC 2020
dc.relation.ispartofseries2020 International Wireless Communications and Mobile Computing Conference, IWCMC 2020
dc.subjectCognitive Radio
dc.subjectSecurity
dc.subjectRobust Statistics
dc.subjectCooperative Spectrum Sensing
dc.subjectEvidence
dc.subjectElectrical and Electronic Engineering
dc.titleRobust Statistics Evidence Based Secure Cooperative Spectrum Sensing for Cognitive Radio Networksen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
rioxxterms.versionofrecord10.1109/IWCMC48107.2020.9148381
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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