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An Incentive Mechanism in Mobile Crowdsourcing Based on Multi-Attribute Reverse Auctions.

Ying Hu1, Yingjie Wang2, Yingshu Li3,4

  • 1School of Computer and Control Engineering, Yantai University, Yantai 264005, China. gaoyang@ytu.edu.cn.

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Summary

This study introduces a novel incentive mechanism for mobile crowdsourcing, combining reverse and multi-attribute auctions. It ensures efficient worker selection and fair payment, enhancing system utility and trust.

Keywords:
crowdsourcingdynamic thresholdmalicious competitionmulti-attribute reverse auctiononline incentive mechanism

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Mobile crowdsourcing systems face challenges with malicious competition and selecting high-quality workers.
  • Existing incentive mechanisms may not adequately address worker quality and payment fairness.

Purpose of the Study:

  • To propose an online incentive mechanism for mobile crowdsourcing that combines reverse and multi-attribute auctions.
  • To enhance the utility, efficiency, and trustworthiness of mobile crowdsourcing systems.

Main Methods:

  • Developed a crowd worker selection algorithm using a multi-attribute reverse auction with a dynamic threshold.
  • Implemented a payment determination algorithm considering worker reputation and sensing data quality.
  • Proved the mechanism's properties: computational efficiency, individual rationality, budget-balance, truthfulness, and honesty.

Main Results:

  • The proposed mechanism effectively selects high-quality crowd workers.
  • Payment is determined based on reputation and data quality, ensuring fairness.
  • Simulations verified the mechanism's efficiency and adaptability.

Conclusions:

  • The novel incentive mechanism significantly improves mobile crowdsourcing system efficiency.
  • The approach enhances adaptability and increases the trust degree in crowdsourcing platforms.