dc.contributor.author |
Lutakamale, Albert Selebea
|
|
dc.contributor.author |
Myburgh, Herman C.
|
|
dc.contributor.author |
De Freitas, Allan
|
|
dc.date.accessioned |
2024-08-19T13:05:12Z |
|
dc.date.available |
2024-08-19T13:05:12Z |
|
dc.date.issued |
2024-06-01 |
|
dc.description |
DATA AVAILABILITY STATEMENT: The dataset used is a publicly available dataset. |
en_US |
dc.description.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. |
en_US |
dc.description.department |
Electrical, Electronic and Computer Engineering |
en_US |
dc.description.sdg |
SDG-09: Industry, innovation and infrastructure |
en_US |
dc.description.uri |
https://www.elsevier.com/locate/adhoc |
en_US |
dc.identifier.citation |
Lutakamale, A.S., Myburgh, H.C., De Freitas, A., 'RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks',
Ad Hoc Networks, Vol. 159, art.103486, pp. 1-12, doi: 10.1016/j.adhoc.2024.103486. |
en_US |
dc.identifier.issn |
1570-8705 (print) |
|
dc.identifier.other |
10.1016/j.adhoc.2024.103486 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/97723 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.rights |
© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license. |
en_US |
dc.subject |
Fingerprint localization |
en_US |
dc.subject |
LoRaWAN |
en_US |
dc.subject |
Wireless communications |
en_US |
dc.subject |
Convolutional neural network (CNN) |
en_US |
dc.subject |
Internet of Things (IoT) |
en_US |
dc.subject |
Low power wide area networking (LPWAN) |
en_US |
dc.subject |
SDG-09: Industry, innovation and infrastructure |
en_US |
dc.title |
RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks |
en_US |
dc.type |
Article |
en_US |