ActiveCrowd: A Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems
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Abstract (arXiv)
Worker selection is a key issue in Mobile Crowd Sensing (MCS). While previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, multi-task-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multi-task MCS environments.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 8 Aug 2016.
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1 paper.