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

arXiv: https://arxiv.org/abs/1608.02661 · HTML: https://mlsanity.com/paper/1608.02661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2016.

## Built on by (most cited first, 1 of 1)

- [Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks](https://mlsanity.com/paper/2106.00717/) (2021)

## Abstract (arXiv)

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.