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Incentives for Delay-Constrained Data Query and Feedback in Mobile Opportunistic Crowdsensing.

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This study introduces data collection schemes for mobile crowdsensing, encouraging cooperation among selfish users through a credit-based reward system and an auction mechanism for efficient resource allocation.

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Game Theory

Background:

  • Mobile opportunistic crowdsensing involves data collection from mobile users.
  • Selfish users may not cooperate, hindering data collection efficiency.
  • Incentive mechanisms are crucial for motivating participation in crowdsensing.

Purpose of the Study:

  • To propose effective data collection schemes for mobile opportunistic crowdsensing.
  • To stimulate cooperation between selfish users through a novel incentive mechanism.
  • To enhance data collection efficiency and credit circulation within the network.

Main Methods:

  • Formulating the user bargain process as a two-person cooperative game.
  • Applying the Nash theorem to find the solution for the cooperative game.
  • Designing an online auction process for credit circulation and resource trading.
  • Utilizing real-world traces for extensive simulation-based evaluation.

Main Results:

  • The proposed schemes effectively stimulate cooperation among selfish users.
  • The credit-based reward mechanism incentivizes data providers and query issuers.
  • The online auction improves credit circulation and facilitates trading of replies and feedbacks.
  • Simulations demonstrate the effectiveness of the proposed data collection schemes.

Conclusions:

  • The developed data collection schemes enhance cooperation in mobile crowdsensing.
  • The game-theoretic approach and auction mechanism provide an effective incentive structure.
  • The findings contribute to more efficient and robust mobile crowdsensing systems.