Active Sparse Mobile Crowd Sensing Based on Matrix Completion
Summary: AS-MCS uses active bipartite-graph sampling to select locations and times, with bipartite-graph matrix completion for robust recovery. Integrates sensing cost and incentives; yields accurate recovery at ~11% sampling, with 4–9× fewer samples than MC on PM2.5/traffic data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kun Xie (Hunan University)
- 2. Xiaocan Li (Hunan University)
- 3. Xin Wang (State University of New York at Stony Brook)
- 4. Gaogang Xie (Chinese Academy of Sciences; University of Chinese Academy of Sciences)
- 5. Jigang Wen (Chinese Academy of Sciences)
- 6. Dafang Zhang (Hunan University)
BibTeX Citation
@inproceedings{xie_sigmod19,
title = {{Active Sparse Mobile Crowd Sensing Based on Matrix Completion}},
author = {Xie, Kun and Li, Xiaocan and Wang, Xin and Xie, Gaogang and Wen, Jigang and Zhang, Dafang},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3319856},
url = {https://dl.acm.org/doi/10.1145/3299869.3319856},
year = {2019}
}
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