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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/11/1401 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:31:42Z |
| format | Article |
| id | doaj.art-a37f58ecb8e849d9834fa7468d977098 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T05:31:42Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-a37f58ecb8e849d9834fa7468d9770982023-11-22T23:14:26ZengMDPI AGEntropy1099-43002021-10-012311140110.3390/e23111401Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning ModelsHaq Nawaz0Ahsen Tahir1Nauman Ahmed2Ubaid U. Fayyaz3Tayyeb Mahmood4Abdul Jaleel5Mandar Gogate6Kia Dashtipour7Usman Masud8Qammer Abbasi9Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Electrical Engineering, University of Engineering and Technology, Lahore 54890, PakistanDepartment of Computer Science, Rachna College of Engineering and Technology (RCET), University of Engineering and Technology, Lahore 54890, PakistanSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKDepartment of Electronics Engineering, University of Engineering and Technology, Taxila 47080, PakistanJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKGlobal 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.https://www.mdpi.com/1099-4300/23/11/1401indoor positioning system (IPS)time difference of arrival (TDOA)ultra-low powertelemetry link |
| spellingShingle | 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 Entropy indoor positioning system (IPS) time difference of arrival (TDOA) ultra-low power telemetry link |
| title | 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_short | 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 |
| topic | indoor positioning system (IPS) time difference of arrival (TDOA) ultra-low power telemetry link |
| url | https://www.mdpi.com/1099-4300/23/11/1401 |
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