ActiveCrowd: A Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems
Bin Guo, Yan Liu, Wenle Wu, Zhiwen Yu, Qi Han
TL;DR
ActiveCrowd is a framework designed to optimize worker selection across multiple tasks in mobile crowdsensing systems, enhancing efficiency for large-scale platforms.
Contribution
It introduces a novel multi-task worker selection framework tailored for large-scale mobile crowdsensing environments.
Findings
Improves task allocation efficiency
Enhances scalability of crowdsensing platforms
Supports multi-task worker selection
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.
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Human Mobility and Location-Based Analysis · Evacuation and Crowd Dynamics