Context-aware hierarchical online learning for performance maximization in mobile crowdsourcing

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buir.contributor.authorTekin, Cem
dc.citation.epage1347en_US
dc.citation.issueNumber3en_US
dc.citation.spage1334en_US
dc.citation.volumeNumber26en_US
dc.contributor.authorMuller, S. K.en_US
dc.contributor.authorTekin, Cemen_US
dc.contributor.authorSchaar, M.en_US
dc.contributor.authorKlein, A.en_US
dc.date.accessioned2019-02-21T16:05:44Zen_US
dc.date.available2019-02-21T16:05:44Zen_US
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance 1) may fluctuate, depending on both the worker's current personal context and the task context and 2) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. In addition, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.en_US
dc.description.provenanceMade available in DSpace on 2019-02-21T16:05:44Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipManuscript received May 8, 2017; revised November 6, 2017 and March 10, 2018; accepted March 29, 2018; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor J. Huang. Date of publication May 2, 2018; date of current version June 14, 2018. The work of S. Klos née Müller and A. Klein was supported by the German Research Foundation (DFG) under Project B3 within the Collaborative Research Center 1053-MAKI. The work of C. Tekin was supported by the Scientific and Technological Research Council of Turkey under 3501 Program under Grant 116E229. The work of M. van der Schaar was supported in part by ONR Mathematical Data Sciences Grant and in part by the NSF under Grant 1524417 and Grant 1462245. (Corresponding author: Sabrina Klos née Müller.) S. Klos née Müller and A. Klein are with the Communications Engineering Laboratory, Technische Universität Darmstadt, 64289 Darmstadt, Germany (e-mail: s.klos@nt.tu-darmstadt.de; a.klein@nt.tu-darmstadt.de).en_US
dc.identifier.doi10.1109/TNET.2018.2828415en_US
dc.identifier.eissn1558-2566en_US
dc.identifier.issn1063-6692en_US
dc.identifier.urihttp://hdl.handle.net/11693/50270en_US
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttps://doi.org/10.1109/TNET.2018.2828415en_US
dc.relation.projectTechnische Universität Darmstadt - Deutsche Forschungsgemeinschaft, DFG: B3 - National Science Foundation, NSF: 1462245 - National Science Foundation, NSF: 1524417 - Deutsche Forschungsgemeinschaft, DFG - Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 116E229en_US
dc.source.titleIEEE/ACM Transactions on Networkingen_US
dc.subjectContextual multi-armed banditsen_US
dc.subjectCrowdsourcingen_US
dc.subjectOnline learningen_US
dc.subjectTask assignmenten_US
dc.titleContext-aware hierarchical online learning for performance maximization in mobile crowdsourcingen_US
dc.typeArticleen_US

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