Lutakamale, Albert SelebeaMyburgh, Hermanus CarelDe Freitas, Allan2024-08-192024-08-192024-06-01Lutakamale, 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.2024.103486.1570-8705 (print)10.1016/j.adhoc.2024.103486http://hdl.handle.net/2263/97723DATA AVAILABILITY STATEMENT: The dataset used is a publicly available dataset.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© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license.Fingerprint localizationLoRaWANWireless communicationsConvolutional neural network (CNN)Internet of Things (IoT)Low power wide area networking (LPWAN)SDG-09: Industry, innovation and infrastructureArticle