Being able to localize smart devices in Low Power Wide Area Networks (LPWANs) is of primary importance in many Internet of Things applications, including Smart Cities. When GPS positioning is not available, a common strategy is to employ fingerprinting localization, which leverages Received Signal Strength (RSS) radio maps constructed offline during a calibration phase. Often, radio maps can then be interpolated to increase the spatial resolution thus improving localization accuracy. We consider different LPWAN technologies coexisting in the same area, and we explore the possibility of augmenting the localization performance by transferring assistance data for RSS map calibration from one technology to the other. We leverage RSS samples from two real-life LPWANs, namely Wireless M-Bus and LoRaWAN, and we propose several methods for localizing devices through knowledge transfer, comparing them to classical techniques based on simple interpolation within the same technology. Results show that transfer-based approaches are able to improve the localization accuracy up to 12% compared to simple interpolation based on single technology and 16% compared to the case where no interpolation strategy is applied.

Machine Learning Based Localization of LoRaWAN Devices via Inter-Technology Knowledge Transfer

Pimpinella, Andrea;Redondi, Alessandro Enrico Cesare;Nicoli, Monica;Cesana, Matteo
2020-01-01

Abstract

Being able to localize smart devices in Low Power Wide Area Networks (LPWANs) is of primary importance in many Internet of Things applications, including Smart Cities. When GPS positioning is not available, a common strategy is to employ fingerprinting localization, which leverages Received Signal Strength (RSS) radio maps constructed offline during a calibration phase. Often, radio maps can then be interpolated to increase the spatial resolution thus improving localization accuracy. We consider different LPWAN technologies coexisting in the same area, and we explore the possibility of augmenting the localization performance by transferring assistance data for RSS map calibration from one technology to the other. We leverage RSS samples from two real-life LPWANs, namely Wireless M-Bus and LoRaWAN, and we propose several methods for localizing devices through knowledge transfer, comparing them to classical techniques based on simple interpolation within the same technology. Results show that transfer-based approaches are able to improve the localization accuracy up to 12% compared to simple interpolation based on single technology and 16% compared to the case where no interpolation strategy is applied.
2020
2020 IEEE International Conference on Communications Workshops (ICC Workshops)
978-1-7281-7440-2
File in questo prodotto:
File Dimensione Formato  
CV_2020_ICC_ML_localizzazione.pdf

Accesso riservato

Descrizione: Publisher's version full paper
: Publisher’s version
Dimensione 406.05 kB
Formato Adobe PDF
406.05 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1145017
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 3
social impact