  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.

Human-Computer Interaction