research-article

LocateMe: Magnetic-fields-based indoor localization using smartphones

Authors:

Kalyan Pathapati Subbu,

Brandon Gozick,

Ram DantuAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 4, Issue 4

Article No.: 73, Pages 1 - 27

https://doi.org/10.1145/2508037.2508054

Published: 08 October 2013 Publication History

Abstract

Fine-grained localization is extremely important to accurately locate a user indoors. Although innovative solutions have already been proposed, there is no solution that is universally accepted, easily implemented, user centric, and, most importantly, works in the absence of GSM coverage or WiFi availability. The advent of sensor rich smartphones has paved a way to develop a solution that can cater to these requirements.

By employing a smartphone's built-in magnetic field sensor, magnetic signatures were collected inside buildings. These signatures displayed a uniqueness in their patterns due to the presence of different kinds of pillars, doors, elevators, etc., that consist of ferromagnetic materials like steel or iron. We theoretically analyze the cause of this uniqueness and then present an indoor localization solution by classifying signatures based on their patterns. However, to account for user walking speed variations so as to provide an application usable to a variety of users, we follow a dynamic time-warping-based approach that is known to work on similar signals irrespective of their variations in the time axis.

Our approach resulted in localization distances of approximately 2m--6m with accuracies between 80--100% implying that it is sufficient to walk short distances across hallways to be located by the smartphone. The implementation of the application on different smartphones yielded response times of less than five secs, thereby validating the feasibility of our approach and making it a viable solution.

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Cited By

Lu YWei DLi WJi XYuan H(2024)A Map-Aided Fast Initialization Method for the Magnetic Positioning of VehiclesElectronics10.3390/electronics1307131513:7(1315)Online publication date: 31-Mar-2024
https://doi.org/10.3390/electronics13071315
Wang QWang LFu MWang JSun LHuang RLi XJiang ZLuo H(2024)Multiscale Transformer and Attention Mechanism for Magnetic Spatiotemporal Sequence LocalizationIEEE Internet of Things Journal10.1109/JIOT.2024.336579311:11(19454-19469)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JIOT.2024.3365793
Wu ZWang WZhao CYue YZhang JShen HWang D(2024)MM4MM: Map Matching Framework for Multi-Session Mapping in Ambiguous and Perceptually-Degraded Environments2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10611566(4399-4405)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10611566
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Index Terms

LocateMe: Magnetic-fields-based indoor localization using smartphones
1. Applied computing
  1. Physical sciences and engineering
    1. Physics
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 4, Issue 4

Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers

September 2013

452 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2508037

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Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 October 2013

Accepted: 01 June 2012

Revised: 01 May 2012

Received: 01 March 2012

Published in TIST Volume 4, Issue 4

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https://doi.org/10.3390/electronics13071315
Wang QWang LFu MWang JSun LHuang RLi XJiang ZLuo H(2024)Multiscale Transformer and Attention Mechanism for Magnetic Spatiotemporal Sequence LocalizationIEEE Internet of Things Journal10.1109/JIOT.2024.336579311:11(19454-19469)Online publication date: 1-Jun-2024
https://doi.org/10.1109/JIOT.2024.3365793
Wu ZWang WZhao CYue YZhang JShen HWang D(2024)MM4MM: Map Matching Framework for Multi-Session Mapping in Ambiguous and Perceptually-Degraded Environments2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10611566(4399-4405)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10611566
Sun MYu KBi J(2024)Magnetic Positioning Based on Evolutionary AlgorithmsPositioning and Navigation Using Machine Learning Methods10.1007/978-981-97-6199-9_7(155-183)Online publication date: 18-Sep-2024
https://doi.org/10.1007/978-981-97-6199-9_7
Sarcevic PCsik DOdry A(2023)Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer FingerprintsSensors10.3390/s2304185523:4(1855)Online publication date: 7-Feb-2023
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Yan SSu YLuo XSun AJi YGhazali K(2023)Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead ReckoningRemote Sensing10.3390/rs1517416515:17(4165)Online publication date: 24-Aug-2023
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