ActiveCrowd: A Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems
arXiv:1608.02661
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.