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Walrasian Equilibrium-Based Incentive Scheme for Mobile Crowdsourcing Fingerprint Localization.

Tao Yu1, Linqing Gui2, Tianxin Yu3

  • 1Institute of Network Science and Cyberspace, Tsinghua University, Beijing 100084, China. yutao@cutech.edu.cn.

Sensors (Basel, Switzerland)
|June 19, 2019
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Summary

This study proposes a novel incentive mechanism for mobile crowdsourcing to collect Wi-Fi fingerprints for localization. The system motivates users, balances data supply and demand, and maximizes benefits for all participants.

Keywords:
crowdsourcingequilibriumfingerprintinglocalization

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Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Wireless Networking

Background:

  • Mobile crowdsourcing is crucial for collecting Wi-Fi fingerprints for localization.
  • Database construction is time-consuming, requiring user motivation for participation.

Purpose of the Study:

  • To propose a Walrasian equilibrium-based incentive mechanism for mobile crowdsourcing.
  • To motivate mobile users for fingerprint data collection and enhance system efficiency.

Main Methods:

  • Constructing a social welfare maximization problem.
  • Employing dual decomposition to optimize benefits for crowdsourcer, users, and the system.
  • Designing a distributed iterative algorithm to reach Walrasian equilibrium.

Main Results:

  • The proposed incentive mechanism eliminates crowdsourcer monopoly and balances data supply and demand.
  • The distributed iterative algorithm demonstrates convergence and optimality.
  • The incentive scheme exhibits self-reconstruction, ensuring system robustness and scalability.

Conclusions:

  • The developed incentive mechanism effectively motivates mobile users for crowdsourcing fingerprint data.
  • The system achieves a balanced market and maximizes overall participant benefits.
  • The proposed approach offers a robust and scalable solution for fingerprint-based localization systems.