{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T12:05:54Z","timestamp":1775477154621,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Medell\u00edn","award":["1039"],"award-info":[{"award-number":["1039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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 Medell\u00edn, 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 R2 of 0.917.<\/jats:p>","DOI":"10.3390\/data8010004","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:02:15Z","timestamp":1671764535000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["LoRaWAN Path Loss Measurements in an Urban Scenario including Environmental Effects"],"prefix":"10.3390","volume":"8","author":[{"given":"Mauricio","family":"Gonz\u00e1lez-Palacio","sequence":"first","affiliation":[{"name":"Telecommunications Department, Universidad de Medell\u00edn, Carrera 87 #30-65, Medell\u00edn 050026, Colombia"}]},{"given":"Diana","family":"Tob\u00f3n-Vallejo","sequence":"additional","affiliation":[{"name":"Telecommunications Department, Universidad de Medell\u00edn, Carrera 87 #30-65, Medell\u00edn 050026, Colombia"}]},{"given":"Lina M.","family":"Sep\u00falveda-Cano","sequence":"additional","affiliation":[{"name":"Accountancy Department, Universidad EAFIT, Carrera 49 # 7 Sur-50, Medell\u00edn 050022, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0067-8316","authenticated-orcid":false,"given":"Santiago","family":"R\u00faa","sequence":"additional","affiliation":[{"name":"Electronics Department, Universidad Nacional Abierta y a Distancia, Medell\u00edn 050012, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5798-398X","authenticated-orcid":false,"given":"Giovanni","family":"Pau","sequence":"additional","affiliation":[{"name":"Informatics Department, Universit\u00e0 Kore di Enna, 94100 Enna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3577-6530","authenticated-orcid":false,"given":"Long Bao","family":"Le","sequence":"additional","affiliation":[{"name":"Institut National de la Recherche Scientifique, University of Quebec, Montr\u00e9al, QC H5A 1K6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8029","DOI":"10.1109\/JSEN.2020.2981209","article-title":"Measurement of users\u2019 well-being through domotic sensors and machine learning algorithms","volume":"20","author":"Casaccia","year":"2020","journal-title":"IEEE Sens. 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