Ultra-low-power, high accuracy 434 MHz indoor positioning system for smart homes leveraging machine learning models

Nawaz, H., Tahir, A., Ahmed, N., Fayyaz, U. U., Mahmood, T., Jaleel, A., Gogate, M., Dashtipour, K., Masud, U. and Abbasi, Q. (2021) Ultra-low-power, high accuracy 434 MHz indoor positioning system for smart homes leveraging machine learning models. Entropy, 23(11), 1401. (doi: 10.3390/e23111401)

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Abstract

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.

Item Type:Articles
Additional Information:This article belongs to the Special Issue Adaptive Signal Processing and Machine Learning Using Entropy and Information Theory.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Tahir, Mr Ahsen and Abbasi, Dr Qammer
Authors: Nawaz, H., Tahir, A., Ahmed, N., Fayyaz, U. U., Mahmood, T., Jaleel, A., Gogate, M., Dashtipour, K., Masud, U., and Abbasi, Q.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Journal Name:Entropy
Publisher:MDPI
ISSN:1099-4300
Published Online:25 October 2021
Copyright Holders:Copyright © 2021 by the authors
First Published:First published in Entropy 23(11): 1401
Publisher Policy:Reproduced under a Creative Commons licence

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