Title: PoMeS : profit-maximizing sensor selection for crowd-sensed spectrum discovery
Authors : Bayhan, Suzan
Gür, Gürkan
Zubow, Anatolij
et. al : No
Proceedings: Cognitive Radio-Oriented Wireless Networks
Pages : 3
Pages to: 16
Conference details: 14th International Conference on Cognitive Radio Oriented Wireless Networks, CrownCom 2019, Poznan, Poland, 11-12 June 2019
Publisher / Ed. Institution : Springer
Publisher / Ed. Institution: Cham
Issue Date: Aug-2019
License (according to publishing contract) : Licence according to publishing contract
Series : Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST)
Series volume: 291
Type of review: Peer review (Publication)
Language : English
Subject (DDC) : 004: Computer science
Abstract: In 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.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference Paper
DOI : 10.1007/978-3-030-25748-4_1
ISBN: 978-3-030-25747-7
URI: https://digitalcollection.zhaw.ch/handle/11475/17906
Appears in Collections:Publikationen School of Engineering

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