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This study developed a mobile app for accurate indoor positioning using Received Signal Strength Indicator (RSSI) maps. The system achieves over 89% accuracy for locating devices in 10 distinct indoor environments.

Keywords:
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Area of Science:

  • Computer Science
  • Robotics
  • Electrical Engineering

Background:

  • Indoor positioning systems (IPS) are crucial for locating objects and people in environments where GPS is unavailable.
  • Received Signal Strength Indicator (RSSI) based algorithms offer a promising approach for indoor localization without prior knowledge of access point distribution.
  • Existing methods often require complex infrastructure or pre-existing maps, limiting their flexibility.

Purpose of the Study:

  • To design and implement a user-friendly mobile application for indoor positioning.
  • To enable users to create custom Received Signal Strength Indicator (RSSI) maps for training.
  • To evaluate and integrate high-accuracy classifiers for real-time indoor localization.

Main Methods:

  • Development of a mobile application for capturing and building RSSI maps.
  • Off-line training of selected machine learning classifiers using captured RSSI data.
  • Evaluation of 59 classifiers, with the top five integrated into the final application.
  • Testing the system's accuracy in various indoor scenarios with distinct locations.

Main Results:

  • The proposed indoor positioning application demonstrated high classification rates exceeding 89%.
  • The system achieved accurate localization for at least 10 different indoor locations.
  • Locations with a minimum separation of 0.5 meters were reliably distinguished.

Conclusions:

  • The developed mobile application provides an effective solution for user-generated indoor positioning systems.
  • The integration of top-performing classifiers ensures high accuracy and reliability.
  • This approach offers a flexible and accurate method for indoor localization using RSSI data.