Abstract:
The ability to offer long-range, high scalability, sustainability, and low-power wireless communication, are
the key factors driving the rapid adoption of the LoRaWAN technology in large-scale Internet of Things
applications. This situation has created high demand to incorporate location estimation capabilities into largescale IoT applications to meaningfully interpret physical measurements collected from IoT devices. As a result,
research aimed at investigating node localization in LoRaWAN networks is on the rise. The poor localization
performance of classical range-based localization approaches in LoRaWAN networks is due to the long-range
nature of LoRaWAN and the rich scattering nature of outdoor environments, which affects signal transmission.
Because of the ability of fingerprint-based localization methods to effectively learn useful positional information
even from noisy RSSI data, this work proposes a fingerprinting-based branched convolutional neural network
(CNN) localization method enhanced with squeeze and excitation (SE) blocks to localize a node in LoRaWAN
using RSSI data. Results from the experiments conducted to evaluate the performance of the proposed method
using a publicly available LoRaWAN dataset prove its effectiveness and robustness in localizing a node with
satisfactory results even with a 30% reduction in both the principal component analysis (PCA) variances on the
training data and the size of the original sample. A localization accuracy of 284.57 m mean error on the test
area was achieved using the Powed data representation, which represents an 8.39% increase in localization
accuracy compared to the currently best-performing fingerprint method in the literature, evaluated using the
same LoRaWAN dataset.
