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Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI).

Abdullah M AlSahly1, Mohammad Mehedi Hassan1, Kashif Saleem2

  • 1Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study explores smartphone sensors and algorithms for indoor localization without extra hardware. The proposed handheld-device-based indoor localization with zero infrastructure (HDIZI) approach offers a cost-effective solution for navigation.

Keywords:
Web of Thingsblueprintfilter algorithmindoor localizationindoor trackingmachine learningmultisensor data fusionsmartphone sensorvirtual IMU

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Robotics

Background:

  • Indoor localization lacks standardized communication and positioning methods.
  • Existing approaches often require additional hardware or infrastructure.
  • A gap exists in understanding the interplay between smartphone sensors, algorithms, and techniques for indoor tracking.

Purpose of the Study:

  • To comprehensively investigate smartphone sensors, algorithms, and techniques for infrastructure-free indoor localization and tracking.
  • To address the research gap by clarifying the connections and impacts of these components.
  • To propose a novel approach for robust indoor navigation using readily available handheld devices.

Main Methods:

  • Systematic review and comparison of smartphone sensors, algorithms, and techniques.
  • Development of the handheld-device-based indoor localization with zero infrastructure (HDIZI) approach.
  • Experimental validation using data from five smartphones across diverse indoor scenarios.

Main Results:

  • The HDIZI approach effectively integrates sensors as input and algorithms as processing engines.
  • Demonstrated a robust localization and tracking model without reliance on external infrastructure.
  • Experimental data analysis considered user stepping behavior and speed across multiple paths.

Conclusions:

  • The proposed HDIZI framework offers a user-friendly and cost-effective solution for indoor navigation.
  • Leverages smartphone capabilities for overcoming inherent sensor limitations through optimal algorithm integration.
  • Expected to outperform previous methods, aiding research in diverse fields like healthcare and Industry 4.0.