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• W4296211693 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 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Adaptive Optimistic Matching for Mobile Crowdsourcing</i> (AOM-MC), an online learning algorithm that incurs <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$tilde{O}(T^{(bar{D}+1)/(bar{D}+2)+epsilon })$</tex-math></inline-formula> regret in <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$T$</tex-math></inline-formula> rounds, for any <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$epsilon >0$</tex-math></inline-formula> , under mild continuity assumptions. Here, <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$bar{D}$</tex-math></inline-formula> 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." @default.
• W4296211693 created "2022-09-18" @default.
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• W4296211693 date "2023-01-01" @default.
• W4296211693 modified "2023-09-25" @default.
• W4296211693 title "Online Context-Aware Task Assignment in Mobile Crowdsourcing via Adaptive Discretization" @default.
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• W4296211693 doi "https://doi.org/10.1109/tnse.2022.3207418" @default.
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