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Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing.

Jia Xu1, Shangshu Yang1, Weifeng Lu1

  • 1Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|February 7, 2020
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Summary

This study introduces an incentive mechanism for edge-assisted mobile crowdsensing, encouraging user participation in truth discovery. The proposed system enhances data accuracy and system scalability efficiently.

Keywords:
edge computingincentive mechanismmobile crowdsensingtruth discovery

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Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Distributed Systems

Background:

  • Mobile crowdsensing systems increasingly rely on deep cloud infrastructure, facing challenges with scale, latency, and complexity.
  • Edge computing offers a paradigm to enhance mobile crowdsensing by reducing latency and computational load, improving expandability and security.

Purpose of the Study:

  • To propose an integrated incentive mechanism for edge-assisted mobile crowdsensing.
  • To stimulate strategic users to contribute more effectively to truth discovery.
  • To address the limitations of deep cloud-dependent systems.

Main Methods:

  • Designed a two-stage incentive mechanism: truth discovery and budget-feasible reverse auction.
  • Truth discovery stage estimates task truthfulness in both deep cloud and edge cloud environments.
  • Budget-feasible reverse auction stage employs a greedy algorithm to maximize quality under budget constraints.

Main Results:

  • The proposed mechanism is computationally efficient, individually rational, truthful, budget-feasible, and constant approximate.
  • Demonstrated significant superiority in estimation precision compared to existing methods.
  • Showcased enhanced system expandability and scalability.

Conclusions:

  • The developed incentive mechanism effectively stimulates user contribution for truth discovery in edge-assisted mobile crowdsensing.
  • The system achieves high estimation precision and improves overall expandability.
  • Edge computing integration provides a viable solution for complex and large-scale mobile crowdsensing challenges.