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A Study on LoRa Signal Propagation Models in Urban Environments for Large-scale Networks DeploymentPETRARIU, A. I. , MUTESCU, P.-M. , COCA, E. , LAVRIC, A.
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LoRa signal coverage, Internet of Things, urban areas, chirp modulation, radiofrequency interference
lora(11), internet(8), propagation(6), technology(5), environment(5), communication(5), systems(4), power(4), networks(4), evaluation(4)
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About this article
Date of Publication: 2021-11-30
Volume 21, Issue 4, Year 2021, On page(s): 61 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.04007
Web of Science Accession Number: 000725107100007
SCOPUS ID: 85122257608
The development of Low-Power Wide-Area Networks is challenging in urban areas due to the terrain elevation changes, clutter losses or dense vegetation regions that attenuate the radio signals. To provide accurate coverage estimation, signal propagation models that integrate losses caused by reflections or attenuations should be used. In this study, we analyze two radio propagation models used for different urban environment configurations, Longley-Rice and ITU-R, to perform a coverage estimation of a LoRa communication network for large-scale deployments. According to our analysis results, validated by measurements, the Longley-Rice and ITU-R radio propagation models are suitable for an urban environment as they use vegetation path losses and can be adapted according to LoRa modulation requirements. Those propagation models are adjusted for real urban field measurements achieved from a point-to-point communication. The obtained results focus also on coverage optimization of a locally deployed LoRa network, considering the best gateway location for the optimum coverage. Thus, a low-cost deployment of the entire network is ensured by reducing the number of installed gateways.
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 Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design, Lavric, Alexandru, Petrariu, Adrian I., Mutescu, Partemie-Marian, Coca, Eugen, Popa, Valentin, Sensors, ISSN 1424-8220, Issue 2, Volume 22, 2022.
Digital Object Identifier: 10.3390/s22020503 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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