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LoRaWAN Path Loss Measurements in an Urban Scenario including Environmental Effects

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mauricio González-Palacio & Diana Tobón-Vallejo & Lina M. Sepúlveda-Cano & Santiago Rúa & Giovanni Pau & Long Bao Le, 2022. "LoRaWAN Path Loss Measurements in an Urban Scenario including Environmental Effects," Data, MDPI, vol. 8(1), pages 1-22, December.
  • Handle: RePEc:gam:jdataj:v:8:y:2022:i:1:p:4-:d:1011802
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    References listed on IDEAS

    as
    1. Michiel Aernouts & Rafael Berkvens & Koen Van Vlaenderen & Maarten Weyn, 2018. "Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas," Data, MDPI, vol. 3(2), pages 1-15, April.
    2. Dong-Hoon Kim & Eun-Kyu Lee & Jibum Kim, 2019. "Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed," Sustainability, MDPI, vol. 11(7), pages 1-32, March.
    3. Pavel Masek & Martin Stusek & Ekaterina Svertoka & Jan Pospisil & Radim Burget & Elena Simona Lohan & Ion Marghescu & Jiri Hosek & Aleksandr Ometov, 2021. "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor," Data, MDPI, vol. 6(6), pages 1-20, June.
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