Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accurac...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Haq Nawaz, Ahsen Tahir, Nauman Ahmed, Ubaid U. Fayyaz, Tayyeb Mahmood, Abdul Jaleel, Mandar Gogate, Kia Dashtipour, Usman Masud, Qammer Abbasi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	indoor positioning system (IPS) time difference of arrival (TDOA) ultra-low power telemetry link Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/a37f58ecb8e849d9834fa7468d977098
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a37f58ecb8e849d9834fa7468d977098
record_format	dspace
spelling	oai:doaj.org-article:a37f58ecb8e849d9834fa7468d9770982021-11-25T17:29:21ZUltra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models10.3390/e231114011099-4300https://doaj.org/article/a37f58ecb8e849d9834fa7468d9770982021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1401https://doaj.org/toc/1099-4300Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.Haq NawazAhsen TahirNauman AhmedUbaid U. FayyazTayyeb MahmoodAbdul JaleelMandar GogateKia DashtipourUsman MasudQammer AbbasiMDPI AGarticleindoor positioning system (IPS)time difference of arrival (TDOA)ultra-low powertelemetry linkScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1401, p 1401 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	indoor positioning system (IPS) time difference of arrival (TDOA) ultra-low power telemetry link Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	indoor positioning system (IPS) time difference of arrival (TDOA) ultra-low power telemetry link Science Q Astrophysics QB460-466 Physics QC1-999 Haq Nawaz Ahsen Tahir Nauman Ahmed Ubaid U. Fayyaz Tayyeb Mahmood Abdul Jaleel Mandar Gogate Kia Dashtipour Usman Masud Qammer Abbasi Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models
description	Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.
format	article
author	Haq Nawaz Ahsen Tahir Nauman Ahmed Ubaid U. Fayyaz Tayyeb Mahmood Abdul Jaleel Mandar Gogate Kia Dashtipour Usman Masud Qammer Abbasi
author_facet	Haq Nawaz Ahsen Tahir Nauman Ahmed Ubaid U. Fayyaz Tayyeb Mahmood Abdul Jaleel Mandar Gogate Kia Dashtipour Usman Masud Qammer Abbasi
author_sort	Haq Nawaz
title	Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models
title_short	Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models
title_full	Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models
title_fullStr	Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models
title_full_unstemmed	Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models
title_sort	ultra-low-power, high-accuracy 434 mhz indoor positioning system for smart homes leveraging machine learning models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a37f58ecb8e849d9834fa7468d977098
work_keys_str_mv	AT haqnawaz ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT ahsentahir ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT naumanahmed ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT ubaidufayyaz ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT tayyebmahmood ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT abduljaleel ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT mandargogate ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT kiadashtipour ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT usmanmasud ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels AT qammerabbasi ultralowpowerhighaccuracy434mhzindoorpositioningsystemforsmarthomesleveragingmachinelearningmodels
_version_	1718412303382085632

Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Ejemplares similares