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dc.contributor.authorEze, Joy C.en
dc.contributor.authorZhang, Sijingen
dc.contributor.authorLiu, Enjieen
dc.contributor.authorEze, Elias Chinedumen
dc.date.accessioned2018-04-25T14:24:21Z
dc.date.available2018-04-25T14:24:21Z
dc.date.issued2018-02-07
dc.identifier.citationEze J, Zhang S, Liu E, Eze E (2018) 'Cognitive radio-enabled Internet of Vehicles (IoVs): a cooperative spectrum sensing and allocation for vehicular communication', IET Networks, 7 (4), pp.190-199.en
dc.identifier.issn2047-4954
dc.identifier.doi10.1049/iet-net.2017.0225
dc.identifier.urihttp://hdl.handle.net/10547/622671
dc.description.abstractInternet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities.
dc.language.isoenen
dc.publisherIETen
dc.relation.urlhttp://digital-library.theiet.org/content/journals/10.1049/iet-net.2017.0225en
dc.relation.urlhttps://ieeexplore.ieee.org/document/8405741
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectcognitive radioen
dc.subjectwireless access in vehicular environment (WAVE)en
dc.subjectvehicular ad hoc networksen
dc.subjectInternet of Thingsen
dc.titleCognitive radio-enabled Internet of Vehicles: a cooperative spectrum sensing and allocation for vehicular communicationen
dc.typeArticleen
dc.identifier.eissn2047-4962
dc.contributor.departmentUniversity of Bedfordshireen
dc.contributor.departmentEbonyi State Universityen
dc.identifier.journalIET Networksen
dc.date.updated2018-04-25T14:16:57Z
html.description.abstractInternet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities.


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