Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibi...

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Autores principales:	Teodora Kocevska, Tomaž Javornik, Aleš Švigelj, Andrej Hrovat
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	indoor propagation channel state information ray tracing decision tree electromagnetic properties radio environment signature Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/0ff2f8e79f824789a745c58612947269
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spelling	oai:doaj.org-article:0ff2f8e79f824789a745c586129472692021-11-25T17:25:15ZFramework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials10.3390/electronics102228432079-9292https://doaj.org/article/0ff2f8e79f824789a745c586129472692021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2843https://doaj.org/toc/2079-9292Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments.Teodora KocevskaTomaž JavornikAleš ŠvigeljAndrej HrovatMDPI AGarticleindoor propagationchannel state informationray tracingdecision treeelectromagnetic propertiesradio environment signatureElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2843, p 2843 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	indoor propagation channel state information ray tracing decision tree electromagnetic properties radio environment signature Electronics TK7800-8360
spellingShingle	indoor propagation channel state information ray tracing decision tree electromagnetic properties radio environment signature Electronics TK7800-8360 Teodora Kocevska Tomaž Javornik Aleš Švigelj Andrej Hrovat Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials
description	Available digital maps of indoor environments are limited to a description of the geometrical environment, despite there being an urgent need for more accurate information, particularly data about the electromagnetic (EM) properties of the materials used for walls. Such data would enable new possibilities in the design and optimization of wireless networks and the development of new radio services. In this paper, we introduce, formalize, and evaluate a framework for machine learning (ML) based wireless sensing of indoor surface materials in the form of EM properties. We apply the radio-environment (RE) signatures of the wireless link, which inherently contains environmental information due to the interaction of the radio waves with the environment. We specify the content of the RE signature suitable for surface-material classification as a set of multipath components given by the received power, delay, phase shift, and angle of arrival. The proposed framework applies an ML approach to construct a classification model using RE signatures labeled with the environmental information. The ML method exploits the data obtained from measurements or simulations. The performance of the framework in different scenarios is evaluated based on standard ML performance metrics, such as classification accuracy and F-score. The results of the elementary case prove that the proposed approach can be applied for the classification of the surface material for a plain environment, and can be further extended for the classification of wall materials in more complex indoor environments.
format	article
author	Teodora Kocevska Tomaž Javornik Aleš Švigelj Andrej Hrovat
author_facet	Teodora Kocevska Tomaž Javornik Aleš Švigelj Andrej Hrovat
author_sort	Teodora Kocevska
title	Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials
title_short	Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials
title_full	Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials
title_fullStr	Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials
title_full_unstemmed	Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials
title_sort	framework for the machine learning based wireless sensing of the electromagnetic properties of indoor materials
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0ff2f8e79f824789a745c58612947269
work_keys_str_mv	AT teodorakocevska frameworkforthemachinelearningbasedwirelesssensingoftheelectromagneticpropertiesofindoormaterials AT tomazjavornik frameworkforthemachinelearningbasedwirelesssensingoftheelectromagneticpropertiesofindoormaterials AT alessvigelj frameworkforthemachinelearningbasedwirelesssensingoftheelectromagneticpropertiesofindoormaterials AT andrejhrovat frameworkforthemachinelearningbasedwirelesssensingoftheelectromagneticpropertiesofindoormaterials
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Framework for the Machine Learning Based Wireless Sensing of the Electromagnetic Properties of Indoor Materials

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