Smart City Gnosys

Smart city article details

Title	Machine Learning Based Localization Of Lorawan Devices Via Inter-Technology Knowledge Transfer
ID_Doc	35934
Authors	Pimpinella A.; Redondi A.E.C.; Nicoli M.; Cesana M.
Year	2020
Published	2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
DOI	http://dx.doi.org/10.1109/ICCWorkshops49005.2020.9145033
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 IEEE.
Author Keywords	Fingerprinting Localization; LoRaWAN; Radio Map Interpolation; Transfer Learning

Similar Articles

Id	Similarity	Authors	Title	Published
15098	0.927	Janssen T.; Berkvens R.; Weyn M.	Comparing Machine Learning Algorithms For Rss-Based Localization In Lpwan	Lecture Notes in Networks and Systems, 96 (2020)
23856	0.905	Keleşoǧlu N.; Halama M.; Strzoda A.	Enhancing Lora-Based Outdoor Localization Accuracy Using Machine Learning	IEEE Access (2025)
24584	0.898	Bonafini F.; Fernandes Carvalho D.; Depari A.; Ferrari P.; Flammini A.; Pasetti M.; Rinaldi S.; Sisinni E.	Evaluating Indoor And Outdoor Localization Services For Lorawan In Smart City Applications	2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2019 - Proceedings (2019)
4638	0.894	Dogan D.; Dalveren Y.; Kara A.; Derawi M.	A Simplified Method Based On Rssi Fingerprinting For Iot Device Localization In Smart Cities	IEEE Access, 12 (2024)
47118	0.884	Rathnayake R.M.M.R.; Maduranga M.W.P.; Dissanayake M.B.	Rssi And Machine Learning-Based Indoor Localization Systems For Smart Cities	7th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2023 (2023)
35965	0.882	Marquez L.E.; Calle M.	Machine Learning For Localization In Lorawan: A Case Study With Data Augmentation	Proceedings - 2022 IEEE Future Networks World Forum, FNWF 2022 (2022)
8927	0.879	Ruan H.; Sun P.; Dong Y.; Tahaei H.; Fang Z.	An Overview Of Lora Localization Technologies	Computers, Materials and Continua, 82, 2 (2025)
8776	0.878	Ahmed S.T.; Annamalai A.; Ahmed A.A.; Chouikha M.; Subedi S.; Polanco M.	An Ml-Based Location Tracking System For Lora Mesh Networks In Gps-Denied Environments	2025 International Conference on Computing, Networking and Communications, ICNC 2025 (2025)
35618	0.875	Ingabire W.; Larijani H.; Gibson R.M.	Lora Rssi Based Outdoor Localization In An Urban Area Using Random Neural Networks	Intelligent Computing - Proceedings of the 2021 Computing Conference (2021)
28114	0.874	Moradbeikie A.; Keshavarz A.; Rostami H.; Paiva S.; Lopes S.I.	Gnss-Free Outdoor Localization Techniques For Resource-Constrained Iot Architectures: A Literature Review	Applied Sciences (Switzerland), 11, 22 (2021)