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      • Department of Electrical and Electronics Engineering
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      Online context-aware task assignment in mobile crowdsourcing via adaptive discretization

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      Author(s)
      Elahi, Sepehr
      Nika, Andi
      Tekin, Cem
      Date
      2022-09-22
      Source Title
      IEEE Transactions on Network Science and Engineering
      Electronic ISSN
      2327-4697
      Publisher
      IEEE
      Volume
      10
      Issue
      1
      Pages
      305 - 320
      Language
      English
      Type
      Article
      Item Usage Stats
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      6
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      Abstract
      Mobile 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.
      Keywords
      Crowdsourcing
      Online learning
      Task assignment
      Contextual multi-armed bandits
      Adaptive discretization
      Permalink
      http://hdl.handle.net/11693/111439
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/TNSE.2022.3207418
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