English

ActiveCrowd: A Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems

Human-Computer Interaction 2016-08-10 v1
Authors: Bin Guo , Yan Liu , Wenle Wu , Zhiwen Yu , Qi Han
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Abstract

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.

Keywords

crowdsourcing

Cite

@article{arxiv.1608.02661,
  title  = {ActiveCrowd: A Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems},
  author = {Bin Guo and Yan Liu and Wenle Wu and Zhiwen Yu and Qi Han},
  journal= {arXiv preprint arXiv:1608.02661},
  year   = {2016}
}

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