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A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring.

Fei Li1, Min Liu2, Yue Zhang3

  • 1Department of Computer Science, Zhejiang University City College, Hangzhou 310015, China. lif@zucc.edu.cn.

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Summary

This study introduces a two-level indoor localization algorithm to enhance accuracy and response time for disaster management. The method uses clustering and optimized regression for faster, more precise positioning in dangerous areas.

Keywords:
affinity propagation clustering (APC)disaster managementdisaster reliefindoor fingerprint localizationparticle swarm optimization (PSO)support vector regression (SVR)

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Area of Science:

  • Computer Science
  • Engineering
  • Geospatial Science

Background:

  • Localization technologies are crucial for disaster management and emergency response.
  • Fingerprint-based indoor localization offers high accuracy but often suffers from slow response times, especially in large areas.
  • Existing systems face challenges balancing accuracy and speed, particularly with increasing monitoring areas and reference nodes.

Purpose of the Study:

  • To propose a novel two-level positioning algorithm to simultaneously improve accuracy and response time for indoor localization.
  • To address the conflict between high accuracy and rapid response in fingerprint-based localization systems.
  • To enhance the suitability of localization methods for dangerous area monitoring.

Main Methods:

  • An affinity propagation clustering (APC) algorithm based on Shepard similarity is used in the off-line stage to divide the fingerprint database into sub-databases.
  • An online coarse positioning algorithm identifies the most similar sub-database by matching cluster centers with the tested node's fingerprint.
  • A support vector regression (SVR) algorithm, optimized by particle swarm optimization (PSO), is employed for fine positioning within the selected sub-database.

Main Results:

  • The proposed two-level algorithm effectively narrows the search space, significantly reducing response time.
  • Fine positioning using PSO-optimized SVR within sub-databases enhances localization accuracy.
  • Experimental results and implementations demonstrate superior performance compared to other methods.

Conclusions:

  • The developed two-level localization method offers a more suitable solution for dangerous area monitoring.
  • The algorithm achieves a better balance between localization accuracy and response time.
  • It also demonstrates advantages in terms of algorithm complexity and storage requirements.