HCAug 8, 2016
ActiveCrowd: A Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems
arXiv:1608.02661v1221 citations
Originality Synthesis-oriented
AI Analysis
This addresses the need for optimized task allocation in mobile crowdsensing, but appears incremental as it extends single-task methods to multi-task scenarios.
The paper tackles the problem of worker selection in multi-task mobile crowdsensing systems, proposing the ActiveCrowd framework to improve efficiency in large-scale platforms.
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.