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

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arxiv 1608.02661 v1 pith:GWEYDUVM submitted 2016-08-08 cs.HC

Bin Guo , Yan Liu , Wenle Wu , Zhiwen Yu , Qi Han This is my paper · ORCID
classification cs.HC
keywords selectionworkeractivecrowdframeworkmobilemulti-taskallocationapproaches
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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.

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