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Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing.

Jing Zhang1, Qian Ding1, Biao Li1

  • 1School of Computer Science and Mathematics, Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China.

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Summary
This summary is machine-generated.

This study introduces a bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol (BKNN-CAP) using edge computing. It enhances efficiency and matching success rates for spatial crowdsourcing tasks.

Keywords:
Bidirectional k-nearest neighborEdge computingRoad network distanceSpatial crowdsourcingTask allocation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Spatial crowdsourcing involves allocating workers to tasks based on location.
  • Existing methods often use centralized frameworks, leading to inefficiencies.
  • Current allocation schemes are typically one-way, limiting query capabilities.

Purpose of the Study:

  • To propose a bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing (BKNN-CAP).
  • To address the limitations of centralized frameworks and one-way allocation in spatial crowdsourcing.
  • To improve the efficiency and accuracy of spatial crowdsourcing task allocation.

Main Methods:

  • Established a spatial crowdsourcing task allocation framework based on edge computing (SCTAFEC) for parallel processing.
  • Developed positive k-nearest neighbor (PKNN) and reverse k-nearest neighbor (RKNN) spatio-temporal query algorithms for bidirectional matching.
  • Incorporated a road network distance calculation method to enhance spatial query accuracy.

Main Results:

  • The proposed BKNN-CAP protocol demonstrates reduced time costs compared to existing methods.
  • Achieved a higher matching success rate in spatial crowdsourcing allocation.
  • Edge computing enables parallel processing of spatio-temporal queries, improving overall efficiency.

Conclusions:

  • The BKNN-CAP protocol offers an efficient and effective solution for spatial crowdsourcing allocation.
  • Bidirectional querying and edge computing significantly enhance task matching and reduce processing time.
  • The proposed methods improve upon traditional spatial crowdsourcing allocation strategies.