Journal
INTERNET OF THINGS
Volume 12, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.iot.2020.100300
Keywords
Low power wide area (LPWA); LoRaWAN; Indoor localization; Deep learning (DL); Machine learning (ML); WiFi; Receiver signal strength (RSS)
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Funding
- Natural Sciences and Engineering Research Council of Canada [RGPIN-201503674, RDCPJ 533444-18]
- Canada Foundation for Innovation
- CMC Microsystems grant [20559]
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In this paper, we propose a framework for the indoor localization in non-line-of-sight (NLoS) conditions using partial knowledge of the channel state information (CSI) obtained from low power wide area (LPWA) radios. The framework is based on NLoS CSI classification using machine learning (ML) and deep learning (DL) models that leverage measurements using end-to-end LoRaWAN network. The measurement set-up provides access to not only the sensor data but also to the physical layer metrics such as the receiver signal strength (RSS), spreading factor (SF), and the frequency hoping signature to name a few. Since LoRa is based on narrow band spread spectrum modulation techniques derived from chirp spread spectrum technology, the CSI are partial in nature. We demonstrate that the partial CSI with frequency hopping signature can be efficiently exploited to predict indoor location with accuracy of more than 98% using a multilayer neural network (MNN). (C) 2020 Elsevier B.V. All rights reserved.
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