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Related Experiment Video

Updated: Mar 3, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Social Welfare Control in Mobile Crowdsensing Using Zero-Determinant Strategy.

Qin Hu1, Shengling Wang2, Rongfang Bie3

  • 1College of Information Science and Technology, Beijing Normal University, Beijing 100875, China. huqin@mail.bnu.edu.cn.

Sensors (Basel, Switzerland)
|May 4, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to improve mobile crowdsensing data quality by controlling social welfare in game theory models. The proposed mechanisms ensure maximized and stable social welfare, enhancing system performance.

Keywords:
crowdsensinggame theorysocial welfarezero-determinant strategy

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Area of Science:

  • Computer Science
  • Game Theory
  • Mobile Computing

Background:

  • Mobile crowdsensing (MCS) leverages ubiquitous mobile device sensors for data collection.
  • Worker greed leads to low-quality sensing data, degrading MCS system performance.
  • Existing solutions often require complex additional components to address data quality issues.

Purpose of the Study:

  • To systematically formulate the MCS data quality problem as an iterated game between a requestor and a worker.
  • To propose novel social welfare control mechanisms using zero-determinant strategies.
  • To optimize social welfare in MCS systems regardless of worker strategies.

Main Methods:

  • Modeling the MCS interaction as two types of iterated games with distinct strategy spaces.
  • Applying zero-determinant (ZD) strategies to control social welfare within the game models.
  • Developing requestor-controlled mechanisms with specific ZD strategy parameter settings.

Main Results:

  • The proposed mechanisms enable the requestor to control and optimize social welfare.
  • Maximized social welfare can be achieved and maintained stably, irrespective of worker strategies.
  • Simulation results validate the effectiveness of the proposed social welfare control mechanisms.

Conclusions:

  • The developed zero-determinant strategy-based mechanisms effectively address low-quality data in mobile crowdsensing.
  • Requestor-controlled social welfare optimization enhances overall MCS system performance and reliability.
  • This game-theoretic approach offers a robust solution for improving data integrity in crowdsourced systems.