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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

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A Cooperative Machine Learning Approach for Pedestrian Navigation in Indoor IoT.

Marzieh Jalal Abadi^1,2, Luca Luceri³, Mahbub Hassan⁴

¹School of Electrical Engineering, Sharif University of Technology, Tehran PO Box 11365-11155, Iran. m.jalalabadi@sharif.edu.

Sensors (Basel, Switzerland)

|October 27, 2019

Summary

This study introduces a cooperative system for accurate indoor localization using pedestrian dead reckoning (PDR). It combines machine learning and consensus algorithms to overcome magnetometer sensor errors, improving mobile user positioning without external infrastructure.

Keywords:

IoT context aware application indoor localization machine learning smart environments

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Area of Science:

Mobile Computing
Sensor Networks
Machine Learning

Background:

Accurate localization of mobile users is crucial for various applications.
Pedestrian dead reckoning (PDR) offers infrastructure-free indoor localization using device sensors.
Heading estimation in PDR is challenging indoors due to magnetometer perturbations.

Purpose of the Study:

To develop a novel cooperative system for enhanced PDR-based localization.
To improve heading accuracy by addressing magnetometer measurement perturbations.
To leverage machine learning and consensus algorithms for cooperative data fusion.

Main Methods:

A machine learning approach detects and filters perturbed magnetometer measurements.
A consensus algorithm aggregates users walking in the same direction.
Distributed data fusion combines measurements from aggregated users for accurate heading estimation.

Main Results:

The proposed system significantly reduces heading errors in indoor environments.
Cooperative data fusion enhances localization accuracy compared to individual PDR.
The system demonstrates robustness against sensor failures through pre-filtering.

Conclusions:

The integration of machine learning and consensus algorithms offers a novel solution for cooperative PDR.
This infrastructure-free, distributed approach improves indoor localization accuracy and reliability.
The method is the first to combine ML with consensus for cooperative PDR, outperforming existing techniques.