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Related Experiment Video

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems.

Ziwei Liu1,2,3, Xiaoguang Niu4,5, Xu Lin6

  • 1State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China. lzw@eqhb.gov.cn.

Sensors (Basel, Switzerland)
|May 26, 2016
PubMed
Summary

This study optimizes mobile crowdsourcing by selecting active users to ensure data integrity and fairness. Efficient algorithms are proposed to manage participant selection, conserving resources without sacrificing sensing quality.

Keywords:
data integritydata predictionmobile crowd sensingparticipant selectiontask-centric

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Data Science

Background:

  • Mobile crowdsourcing systems involve numerous participants collecting data.
  • Neighboring participants' data often shows strong spatial correlations.
  • Optimizing participant selection is crucial for data integrity and resource conservation.

Purpose of the Study:

  • To address participant selection in mobile crowdsourcing.
  • To ensure sensing data integrity above a threshold.
  • To achieve fairness and resource conservation, even with inaccurate data.

Main Methods:

  • Developed a task-centric approach exploiting data correlation for participant selection.
  • Formulated participant selection as a constrained optimization problem.
  • Proposed efficient polynomial-time algorithms for optimal subset selection and set partitioning.

Main Results:

  • An efficient algorithm was developed to select optimal participants for data integrity.
  • An improved algorithm addressed fairness and resource conservation with potentially inaccurate data.
  • Validation using the Intel-Berkeley lab sensing dataset demonstrated satisfactory performance.

Conclusions:

  • The proposed methods effectively manage participant selection in mobile crowdsourcing.
  • Data correlation exploitation enhances sensing data integrity.
  • Algorithms ensure resource conservation and fairness while maintaining data quality.