Online Context-Aware Task Assignment in Mobile Crowdsourcing via Adaptive Discretization
IEEE Transactions on Network Science and Engineering(2023)
摘要
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
$\tilde{O}(T^{(\bar{D}+1)/(\bar{D}+2)+\epsilon })$
regret in
$T$
rounds, for any
$\epsilon >0$
, under mild continuity assumptions. Here,
$\bar{D}$
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.
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关键词
Crowdsourcing,online learning,task assignment,contextual multi-armed bandits,adaptive discretization
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