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Secure Indoor Localization Based on Extracting Trusted Fingerprint.

Juan Luo1, Xixi Yin2, Yanliu Zheng3

  • 1School of Information Science and Engineering, Hunan University, Changsha 410012, China. juanluo@hnu.edu.cn.

Sensors (Basel, Switzerland)
|February 7, 2018
PubMed
Summary

This study introduces a secure fingerprinting localization (SFL) method for WiFi indoor positioning. The novel approach enhances accuracy and robustness against environmental changes and access point issues.

Keywords:
WiFifingerprintindoor localizationsecuretrusted factor

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • WiFi-based indoor localization is crucial for many applications.
  • Fingerprinting techniques are popular due to simplicity and hardware compatibility.
  • Existing methods struggle with abnormal Received Signal Strength Indication (RSSI) from environmental changes or access point (AP) issues.

Purpose of the Study:

  • To propose a Secure Fingerprinting Localization (SFL) method.
  • To enhance robustness against variable environments, impaired APs, and new APs.
  • To improve indoor localization accuracy.

Main Methods:

  • Developed an offline phase with a voting mechanism and fingerprint database update method.
  • Utilized mutual cooperation between reference anchor nodes for database updates.
  • Analyzed RSSI standard deviation, implemented AP voting, and calculated AP trust factors.
  • Introduced an online phase to identify new/broken APs and extract secure fingerprints based on trust factors.

Main Results:

  • The proposed SFL method demonstrates robustness against abnormal RSSI.
  • The strategy effectively improves localization accuracy compared to existing algorithms.
  • Experimental results validate the effectiveness of the SFL method.

Conclusions:

  • The SFL method offers a robust solution for WiFi indoor localization.
  • The approach successfully addresses challenges posed by environmental variability and AP malfunctions.
  • This work significantly advances the reliability and accuracy of fingerprinting-based localization systems.