Online context-aware task assignment in mobile crowdsourcing via adaptive discretization

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buir.contributor.authorElahi, Sepehr
buir.contributor.authorNika, Andi
buir.contributor.authorTekin, Cem
buir.contributor.orcidElahi, Sepehr|0000-0001-5494-6465
buir.contributor.orcidNika, Andi|0000-0002-7453-4975
buir.contributor.orcidTekin, Cem|0000-0003-4361-4021
dc.citation.epage320en_US
dc.citation.issueNumber1en_US
dc.citation.spage305en_US
dc.citation.volumeNumber10en_US
dc.contributor.authorElahi, Sepehr
dc.contributor.authorNika, Andi
dc.contributor.authorTekin, Cem
dc.date.accessioned2023-02-16T11:09:39Z
dc.date.available2023-02-16T11:09:39Z
dc.date.issued2022-09-22
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractMobile crowdsourcing is rapidly boosting the Internet of Things revolution. Its natural development leads to an adaptation to various real-world scenarios, thus imposing a need for wide generality on data-processing and task-assigning methods. We consider the task assignment problem in mobile crowdsourcing while taking into consideration the following: (i) we assume that additional information is available for both tasks and workers, such as location, device parameters, or task parameters, and make use of such information; (ii) as an important consequence of the worker-location factor, we assume that some workers may not be available for selection at given times; (iii) the workers' characteristics may change over time. To solve the task assignment problem in this setting, we propose Adaptive Optimistic Matching for Mobile Crowdsourcing (AOM-MC), an online learning algorithm that incurs O~(T(D¯+1)/(D¯+2)+ϵ) regret in T rounds, for any ϵ>0 , under mild continuity assumptions. Here, D¯ is a notion of dimensionality which captures the structure of the problem. We also present extensive simulations that illustrate the advantage of adaptive discretization when compared with uniform discretization, and a time- and location-dependent crowdsourcing simulation using a real-world dataset, clearly demonstrating our algorithm's superiority to the current state-of-the-art and baseline algorithms.en_US
dc.identifier.doi10.1109/TNSE.2022.3207418en_US
dc.identifier.eisbn2327-4697
dc.identifier.eissn2327-4697
dc.identifier.urihttp://hdl.handle.net/11693/111439
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://www.doi.org/10.1109/TNSE.2022.3207418en_US
dc.source.titleIEEE Transactions on Network Science and Engineeringen_US
dc.subjectCrowdsourcingen_US
dc.subjectOnline learningen_US
dc.subjectTask assignmenten_US
dc.subjectContextual multi-armed banditsen_US
dc.subjectAdaptive discretizationen_US
dc.titleOnline context-aware task assignment in mobile crowdsourcing via adaptive discretizationen_US
dc.typeArticleen_US
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