LoRaLoc : machine learning-based fingerprinting for outdoor geolocation using LoRa

Carrino, Francesco (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Janka, Ales (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Abou Khaled, Omar (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Mugellini, Elena (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland)

LoRa technology allows long-range transmissions with low power consumption and it can also be used indoor. For these reasons, the introduction of a precise timestamping of LoRa frames provides the possibility to use this technology for accurate localization in many scenarios. However, this is still very challenging to achieve in non-line-of-sight environments such as urban landscapes. In this paper, we present a “fingerprinting” method to perform outdoor geolocation based on machine learning (Random Forest and Neural Networks) applied to a reference map. The map combines Time Difference Of Arrival (TDOA) measurements generated by a LoRa network and GPS location as ground truth. We tested our approach on simulated data achieving promising results with a Root Mean Squared Error below 9 meters by using a Long Short-Term Memory (LSTM) network.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
HumanTech - Technology for Human Wellbeing Institute
iCoSys - Institut des systèmes complexes
Publisher:
Bern, Switzerland, 14 June 2019
Date:
2019-06
Bern, Switzerland
14 June 2019
Pagination:
5 p.
Published in:
Proceedings of 6th Swiss Conference on Data Science – SDS|2019, 14 June 2019, Bern, Switzerland
Numeration (vol. no.):
pp. 82-86
ISBN:
978-1-7281-3105-4
External resources:
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-01-14, last modified 2020-01-14

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