research-article

Feature selection difference matching method for indoor positioning

Authors:

T. T. Lu,

Y. J. Liu,

J. J. Ciou,

C. H. LuAuthors Info & Claims

ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing

Article No.: 116, Pages 1 - 5

https://doi.org/10.1145/3018896.3025165

Published: 22 March 2017 Publication History

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Abstract

Location-based services positioning uses the relative position between devices and base stations or satellites to calculate positions. The global positioning system (GPS) is currently one of the most widely used positioning tools. However, the distance calculation and positioning of GPSs are negatively influenced by the closed structures of indoor environments and tall buildings. Although GPS is fully developed for outdoor environments and provides numerous Internet of Things services, it cannot be applied to indoor positioning involving various obstructions. Bluetooth low energy sensors have been applied to use the accepted received signal strength indication (RSSI) values to conduct indoor positioning. Consequently, sensor placement affect the quality of indoor positioning.

In this study, a feature selection difference matching method was proposed. This method not only achieves rapid and accurate positioning. This method also has an advantage in reducing about 49% - 71% of the average excessive amount of data required by various mobile devices to establish prediction models and compare matching results.

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Cited By

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Quezada-Gaibor DTorres-Sospedra JNurmi JKoucheryavy YHuerta J(2021)Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic ReviewSensors10.3390/s2201011022:1(110)Online publication date: 24-Dec-2021
https://doi.org/10.3390/s22010110
Yean SLee BOh H(2020)Feature Engineering for Grid-based Multi-Floor Indoor Localisation using Machine Learning2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA50958.2020.9263706(142-148)Online publication date: 19-Oct-2020
https://doi.org/10.1109/IDSTA50958.2020.9263706

Index Terms

Feature selection difference matching method for indoor positioning
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Sensor applications and deployments

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Published In

ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing

March 2017

1349 pages

ISBN:9781450347747

DOI:10.1145/3018896

General Chair:
Hani Hamdan
University of Paris-Saclay, Paris, France
,
Program Chairs:
Homero Toral-Cruz
University of Quintana Roo, Mexico
,
Sedat Akleylek
Ondokuz Mayis University, Turkey
,
Hamid Mcheick
Université du Québec, Canada
,
Publications Chair:
Djallel Eddine Boubiche
University of Batna, Algeria

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2017

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ICC '17

ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing

March 22 - 23, 2017

Cambridge, United Kingdom

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ICC '17 Paper Acceptance Rate 213 of 590 submissions, 36%;

Overall Acceptance Rate 213 of 590 submissions, 36%

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Cited By

View all

Quezada-Gaibor DTorres-Sospedra JNurmi JKoucheryavy YHuerta J(2021)Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic ReviewSensors10.3390/s2201011022:1(110)Online publication date: 24-Dec-2021
https://doi.org/10.3390/s22010110
Yean SLee BOh H(2020)Feature Engineering for Grid-based Multi-Floor Indoor Localisation using Machine Learning2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA50958.2020.9263706(142-148)Online publication date: 19-Oct-2020
https://doi.org/10.1109/IDSTA50958.2020.9263706

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