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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorBayhan, Suzan-
dc.contributor.authorGür, Gürkan-
dc.contributor.authorZubow, Anatolij-
dc.date.accessioned2019-08-14T09:22:24Z-
dc.date.available2019-08-14T09:22:24Z-
dc.date.issued2019-08-
dc.identifier.isbn978-3-030-25747-7de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17906-
dc.description.abstractIn contrast to conventional network planning where a mobile network operator (MNO) has to overprovision its network resources according to its peak load, an MNO can alternatively expand its capacity whenever, wherever needed with secondary spectrum discovered via spectrum sensing. While outsourcing the spectrum discovery to a crowd of sensing units may be more advantageous compared to deploying sensing infrastructure itself, the MNO has to offer incentives in the form of payments to the units participating in the sensing campaign. A key challenge for this crowdsensing environment is to decide on how many sensing units to employ given a certain budget under some performance constraints. In this paper, we present a profit-maximizing sensor selection scheme for crowdsensed spectrum discovery (PoMeS) for MNOs who want to take sensing as a service from the crowd of network elements and pay these sensors for their service. Compared to sensor selection considering the strict sensing accuracy required by the regulations, our simulations show that an MNO can increase its profit by deciding itself the level of sensing accuracy based on its traffic in each cell site as well as the penalty it has to pay for not satisfying the required sensing accuracy.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST)de_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc004: Informatikde_CH
dc.titlePoMeS : profit-maximizing sensor selection for crowd-sensed spectrum discoveryde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-25748-4_1de_CH
zhaw.conference.details14th International Conference on Cognitive Radio Oriented Wireless Networks, Poznan, Poland, 11-12 June 2019de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end16de_CH
zhaw.pages.start3de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number291de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsCognitive Radio-Oriented Wireless Networksde_CH
zhaw.webfeedInformation Securityde_CH
zhaw.author.additionalNode_CH
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Bayhan, S., Gür, G., & Zubow, A. (2019). PoMeS : profit-maximizing sensor selection for crowd-sensed spectrum discovery [Conference paper]. Cognitive Radio-Oriented Wireless Networks, 3–16. https://doi.org/10.1007/978-3-030-25748-4_1
Bayhan, S., Gür, G. and Zubow, A. (2019) ‘PoMeS : profit-maximizing sensor selection for crowd-sensed spectrum discovery’, in Cognitive Radio-Oriented Wireless Networks. Cham: Springer, pp. 3–16. Available at: https://doi.org/10.1007/978-3-030-25748-4_1.
S. Bayhan, G. Gür, and A. Zubow, “PoMeS : profit-maximizing sensor selection for crowd-sensed spectrum discovery,” in Cognitive Radio-Oriented Wireless Networks, Aug. 2019, pp. 3–16. doi: 10.1007/978-3-030-25748-4_1.
BAYHAN, Suzan, Gürkan GÜR und Anatolij ZUBOW, 2019. PoMeS : profit-maximizing sensor selection for crowd-sensed spectrum discovery. In: Cognitive Radio-Oriented Wireless Networks. Conference paper. Cham: Springer. August 2019. S. 3–16. ISBN 978-3-030-25747-7
Bayhan, Suzan, Gürkan Gür, and Anatolij Zubow. 2019. “PoMeS : Profit-Maximizing Sensor Selection for Crowd-Sensed Spectrum Discovery.” Conference paper. In Cognitive Radio-Oriented Wireless Networks, 3–16. Cham: Springer. https://doi.org/10.1007/978-3-030-25748-4_1.
Bayhan, Suzan, et al. “PoMeS : Profit-Maximizing Sensor Selection for Crowd-Sensed Spectrum Discovery.” Cognitive Radio-Oriented Wireless Networks, Springer, 2019, pp. 3–16, https://doi.org/10.1007/978-3-030-25748-4_1.


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