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A Joint Constraint Incentive Mechanism Algorithm Utilizing Coverage and Reputation for Mobile Crowdsensing.

Jing Zhang1, Xiaoxiao Yang1, Xin Feng1

  • 1College of Computer Science and Technology, Chang Chun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|August 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for mobile crowdsensing (MCS) that improves data quality by selecting users based on location and reputation. The coverage and user utility are enhanced through a game theory approach and task prioritization.

Keywords:
Stackelberg game theorycoveragehistorical reputationincentive mechanismmobile crowdsensing

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Game Theory

Background:

  • Mobile crowdsensing (MCS) effectiveness relies on optimal user selection for data quality within budget constraints.
  • User coverage and historical reputation are key factors influencing sensing data accuracy and reliability.

Purpose of the Study:

  • To propose a novel incentive mechanism algorithm for MCS that jointly considers user coverage and reputation.
  • To enhance data quality and maximize user and server center utility in MCS environments.

Main Methods:

  • A coverage and reputation joint constraint incentive mechanism algorithm (CRJC-IMA) based on Stackelberg game theory.
  • Two-stage Stackelberg game to determine optimal incentive mechanisms and analyze Nash equilibrium.
  • EM algorithm for data quality evaluation and user reputation updates.
  • Task prioritization by mobile users to maximize total utility.

Main Results:

  • The CRJC-IMA demonstrates higher coverage compared to CTSIA.
  • Both mobile user and server center utility are improved with CRJC-IMA over STD algorithms.
  • Adjusted task prioritization by mobile users further increases their utility.

Conclusions:

  • The proposed CRJC-IMA effectively balances user coverage and reputation for improved MCS data quality.
  • Stackelberg game theory provides a robust framework for optimizing incentive mechanisms in MCS.
  • Dynamic task prioritization by mobile users is a viable strategy for enhancing individual utility.