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Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing.

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This study introduces a novel task allocation model for mobile crowdsourcing systems by integrating social network friend relationships. The proposed algorithm enhances system utility and worker privacy using GeoHash coding.

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Area of Science:

  • Computer Science
  • Mobile Computing
  • Social Network Analysis

Background:

  • Mobile crowdsourcing is a growing research area due to the proliferation of mobile devices.
  • Existing task allocation methods in mobile crowdsourcing often overlook the potential of social network integration.
  • Maximizing the utility of mobile crowdsourcing systems remains a key challenge.

Purpose of the Study:

  • To propose a novel task allocation model for mobile crowdsourcing systems that incorporates social network attributes.
  • To leverage human homogeneity and friend relationships within social networks to improve task allocation.
  • To enhance the overall utility and efficiency of mobile crowdsourcing systems.

Main Methods:

  • Developed a task allocation algorithm specifically designed for mobile crowdsourcing, utilizing social network friend relationships.
  • Employed the GeoHash coding mechanism to quantify the strength of worker relationships while preserving location privacy.
  • Evaluated the model's performance using both synthetic datasets and a real-world Yelp dataset.

Main Results:

  • The proposed task allocation model demonstrated effectiveness and applicability in enhancing mobile crowdsourcing systems.
  • Comparison experiments validated the superior performance of the new allocation mechanism.
  • The integration of social network attributes led to improved system utilities.

Conclusions:

  • Social network relationships can be effectively integrated into mobile crowdsourcing for optimized task allocation.
  • The proposed GeoHash-based algorithm successfully balances task allocation efficiency with worker location privacy.
  • This research provides a valuable framework for future mobile crowdsourcing system designs.