research-article

Active Sparse Mobile Crowd Sensing Based on Matrix Completion

Authors:

Dafang ZhangAuthors Info & Claims

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

Pages 195 - 210

https://doi.org/10.1145/3299869.3319856

Published: 25 June 2019 Publication History

Abstract

A major factor that prevents the large scale deployment of Mobile Crowd Sensing (MCS) is its sensing and communication cost. Given the spatio-temporal correlation among the environment monitoring data, matrix completion (MC) can be exploited to only monitor a small part of locations and time, and infer the remaining data. Rather than only taking random measurements following the basic MC theory, to further reduce the cost of MCS while ensuring the quality of missing data inference, we propose an Active Sparse MCS (AS-MCS) scheme which includes a bipartite-graph-based sensing scheduling scheme to actively determine the sampling positions in each upcoming time slot, and a bipartite-graph-based matrix completion algorithm to robustly and accurately recover the un-sampled data in the presence of sensing and communications errors. We also incorporate the sensing cost into the bipartite-graph to facilitate low cost sample selection and consider the incentives for MCS. We have conducted extensive performance studies using the data sets from the monitoring of PM 2.5 air condition and road traffic speed, respectively. Our results demonstrate that our AS-MCS scheme can recover the missing data at very high accuracy with the sampling ratio only around $11%$, while the peer matrix completion algorithms with similar recovery performance requires up to 4-9 times the number of samples of ours for both the data sets.

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Wang ESong ZWu MLiu WYang BYang YWu J(2025)A New Data Completion Perspective on Sparse CrowdSensing: Spatiotemporal Evolutionary Inference ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2024.348098324:3(1357-1371)Online publication date: Mar-2025
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Index Terms

Active Sparse Mobile Crowd Sensing Based on Matrix Completion
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Non-negative matrix factorization

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cover image ACM Conferences

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

June 2019

2106 pages

ISBN:9781450356435

DOI:10.1145/3299869

General Chairs:
Peter Boncz
CWI & Vrije Universiteit Amsterdam, The Netherlands
,
Stefan Manegold
CWI & Universiteit Leiden, The Netherlands
,
Program Chairs:
Anastasia Ailamaki
EPFL, Switzerland
,
Amol Deshpande
University of Maryland, USA
,
Tim Kraska
MIT, USA

Copyright © 2019 ACM.

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Published: 25 June 2019

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U.S. NSF CNS
the China Scholarship Council Foundation
National Natural Science Foundation of China
the open project funding of CAS Key Lab of Network Data Science and Technology, Chinese Academy of Sciences
Hunan Provincial Innovation Foundation For Postgraduate
the open project funding of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences
Hunan Provincial Natural Science Foundation of China
U.S. NSF ECCS

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SIGMOD/PODS '19

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SIGMOD/PODS '19: International Conference on Management of Data

June 30 - July 5, 2019

Amsterdam, Netherlands

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SIGMOD '19 Paper Acceptance Rate 88 of 430 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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https://doi.org/10.1109/TNET.2024.3480008
Wang ESong ZWu MLiu WYang BYang YWu J(2025)A New Data Completion Perspective on Sparse CrowdSensing: Spatiotemporal Evolutionary Inference ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2024.348098324:3(1357-1371)Online publication date: Mar-2025
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Li ZHu YLi MYang WLi K(2024)cuFastTucker: A Novel Sparse FastTucker Decomposition For HHLST on Multi-GPUsACM Transactions on Parallel Computing10.1145/366145011:2(1-31)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3661450
Yang GWen PLiu YKong LLiu Y(2024)Spatial-Temporal Data Inference With Graph Attention Neural Networks in Sparse Mobile CrowdsensingIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.341839311:5(4617-4626)Online publication date: Sep-2024
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