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

2016/08/08 by Bin Guo, Yan Liu, Guo, Bin +7 · 2 citations
Computer Science · Engineering · Social Sciences · #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing

paper · pdf · doi:10.48550/arxiv.1608.02661

openalex publication_date 2016/08/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Spatial Crowdsourcing-based Task Allocation for UAV-assisted Maritime Data Collection
• Approximation Algorithms for Scheduling Crowdsourcing Tasks in Mobile Social Networks

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