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Worker selection is a key issue in Mobile Crowd Sensing (MCS). While previousworker selection approaches mainly focus on selecting a proper subset ofworkers for a single MCS task, multi-task-oriented worker selection isessential and useful for the efficiency of large-scale MCS platforms. Thispaper proposes ActiveCrowd, a worker selection framework for multi-task MCSenvironments.

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