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Related Experiment Videos

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users' Trust Dispositions' Consideration.

Eva Zupančič1, Borut Žalik2

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia. eva.zupancic@um.si.

Sensors (Basel, Switzerland)
|March 20, 2019
PubMed
Summary

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This study introduces a new trust framework for mobile crowdsensing, enhancing data reliability by considering user subjectivity. The proposed method significantly outperforms existing approaches in evaluating data trustworthiness.

Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Data Science

Background:

  • Mobile crowdsensing leverages smartphone ubiquity for large-scale data collection, surpassing fixed sensor networks.
  • Participant reliance in mobile crowdsensing systems introduces risks of malicious or erroneous data, necessitating trust mechanisms.
  • Ensuring system sustainability requires robust trust and reputation frameworks to manage data quality.

Purpose of the Study:

  • To define a conceptual trust framework for mobile crowdsensing that incorporates human factors and subjective data.
  • To develop a novel method for evaluating data trustworthiness, accounting for user opinions and subjective contributions.
  • To address the challenge of data quality in crowdsensing systems reliant on human participation.

Main Methods:

Keywords:
data trustworthinesshuman involvementmobile crowdsensingopinionsopportunistic sensingparticipatory sensingreputation systemssubjectivitytrust attitudetrust framework

Related Experiment Videos

  • Proposed a novel method for evaluating data trustworthiness based on comparing users' trust attitudes.
  • Utilized non-parametric statistical methods to analyze subjective and objective data contributions.
  • Conducted extensive simulations to evaluate the proposed method's performance against existing approaches.

Main Results:

  • The proposed method demonstrated superior performance in trustworthiness evaluation compared to Huang's method (28.6% improvement).
  • Outperformed systems without data trustworthiness calculation by an average of 33.6%.
  • Effectively handles subjective data alongside raw sensor data in trust assessment.

Conclusions:

  • The developed trust framework and evaluation method enhance the reliability of mobile crowdsensing systems.
  • Incorporating user trust attitudes and subjective data is crucial for robust crowdsensing data quality.
  • The proposed approach offers a significant advancement in managing and validating crowdsourced data.