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A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building.

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Summary

This study introduces an intelligent indoor positioning system (I-IPS) using machine learning (ML) algorithms to efficiently predict user location via Wi-Fi fingerprinting. The system enhances context-aware services (CAS) with superior performance in large indoor environments.

Keywords:
fingerprintingindoor localizationmachine learningreceived signal strength indicator

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Ubiquitous Computing

Background:

  • Unobtrusive indoor location systems require minimal hardware and administrator access.
  • Wi-Fi fingerprinting compares real-time RSSI with a database but struggles with large areas.
  • Conventional fingerprinting methods face computational challenges in extensive indoor environments.

Purpose of the Study:

  • To develop a fast and efficient machine learning-based indoor positioning system (I-IPS).
  • To reduce the computational complexity of Wi-Fi fingerprinting in large-scale indoor scenarios.
  • To integrate the I-IPS with a multi-agent framework for enhanced context-aware services (CAS).

Main Methods:

  • Implementation of popular machine learning (ML) algorithms for RSSI data classification.
  • Development of an intelligent indoor positioning system (I-IPS) utilizing Wi-Fi fingerprinting.
  • Integration of the I-IPS with a multi-agent framework to support context-aware services (CAS).

Main Results:

  • Machine learning algorithms effectively classify real-time RSSI values for location prediction.
  • The proposed I-IPS demonstrates superior performance in indoor positioning tasks.
  • Statistical measurements validate the accuracy and efficiency of the developed system.

Conclusions:

  • The intelligent indoor positioning system (I-IPS) offers an efficient solution for unobtrusive indoor localization.
  • Machine learning significantly improves the performance of Wi-Fi fingerprinting in large indoor spaces.
  • The integration with multi-agent systems enhances context-aware services (CAS) through accurate positioning.