H
Haoyi Xiong
Researcher at Missouri University of Science and Technology
Publications - 33
Citations - 1784
Haoyi Xiong is an academic researcher from Missouri University of Science and Technology. The author has contributed to research in topics: Computer science & Linear discriminant analysis. The author has an hindex of 16, co-authored 28 publications receiving 1461 citations. Previous affiliations of Haoyi Xiong include Université Paris-Saclay & Institut Mines-Télécom.
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Proceedings ArticleDOI
CrowdRecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint
TL;DR: The results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% -- 73.5% fewer participants on average under the same probabilistic coverage constraint.
Journal ArticleDOI
4W1H in Mobile Crowd Sensing
TL;DR: A four-stage life cycle is proposed (i.e., task creation, task assignment, individual task execution, and crowd data integration) to characterize the mobile crowd sensing process, and 4W1H is used to sort out the research problems in the mobile community sensing domain.
Proceedings ArticleDOI
CCS-TA: quality-guaranteed online task allocation in compressive crowdsensing
TL;DR: A novel framework called CCS-TA is proposed, combining the state-of-the-art compressive sensing, Bayesian inference, and active learning techniques, to dynamically select a minimum number of sub-areas for sensing task allocation in each sensing cycle, while deducing the missing data of unallocated sub-wereas under a probabilistic data accuracy guarantee.
Journal ArticleDOI
iCrowd : Near-Optimal Task Allocation for Piggyback Crowdsensing
TL;DR: iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals.
Journal ArticleDOI
Multi-Task Allocation in Mobile Crowd Sensing with Individual Task Quality Assurance
TL;DR: This paper proposes a novel multi-task allocation framework named MTasker, which adopts a descent greedy approach, where a quasi-optimal allocation plan is evolved by removing a set of task-worker pairs from the full set.