LoRaWAN Path Loss Measurements in an Urban Scenario including Environmental Effects

Mauricio González-Palacio (), Diana Tobón-Vallejo, Lina M. Sepúlveda-Cano, Santiago Rúa, Giovanni Pau and Long Bao Le
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Mauricio González-Palacio: Telecommunications Department, Universidad de Medellín, Carrera 87 #30-65, Medellín 050026, Colombia
Diana Tobón-Vallejo: Telecommunications Department, Universidad de Medellín, Carrera 87 #30-65, Medellín 050026, Colombia
Lina M. Sepúlveda-Cano: Accountancy Department, Universidad EAFIT, Carrera 49 # 7 Sur-50, Medellín 050022, Colombia
Santiago Rúa: Electronics Department, Universidad Nacional Abierta y a Distancia, Medellín 050012, Colombia
Giovanni Pau: Informatics Department, Università Kore di Enna, 94100 Enna, Italy
Long Bao Le: Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada

Data, 2022, vol. 8, issue 1, 1-22

Abstract: LoRaWAN is a widespread protocol by which Internet of things end nodes (ENs) can exchange information over long distances via their gateways. To deploy the ENs, it is mandatory to perform a link budget analysis, which allows for determining adequate radio parameters like path loss (PL). Thus, designers use PL models developed based on theoretical approaches or empirical data. Some previous measurement campaigns have been performed to characterize this phenomenon, primarily based on distance and frequency. However, previous works have shown that weather variations also impact PL, so using the conventional approaches and available datasets without capturing important environmental effects can lead to inaccurate predictions. Therefore, this paper delivers a data descriptor that includes a set of LoRaWAN measurements performed in Medellín, Colombia, including PL, distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and energy, among other things. This dataset can be used by designers who need to fit highly accurate PL models. As an example of the dataset usage, we provide some model fittings including log-distance, and multiple linear regression models with environmental effects. This analysis shows that including such variables improves path loss predictions with an RMSE of 1.84 dB and an R 2 of 0.917.

Keywords: LoRaWAN; path loss; environmental variables (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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