# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Description** & **Variables** & **Model Type** \\ \hline [PERSON] et al. [23] & Measurement campaign to evaluate fingerprinting algorithms for LoRaWAN and Sigfox & RSSI, timestamp, location & Not reported \\ \hline [PERSON] et al. [25] & Path loss model to characterize the attenuation in oil palm crops & Distance, number of trees and canopies & LDPLM \\ \hline [PERSON] et al. [26] & Path loss model to improve the reliability of transmission in a water quality monitoring application & Distance, frequency, bandwidth, antennas’ heights, and spreading factor & ML-based \\ \hline [PERSON] et al. [27] & Path loss model for suburban environments to analyze the impacts of path loss in coverage, SNR, and received packets & Distance & LDPLM \\ \hline [PERSON] et al. [22] & LoRaWAN campaign collected from nine GWs over four months in an urban environment & RSSI, SNR, and SF & Not reported \\ \hline [PERSON] et al. [29] & Path loss model to improve the localization in search and rescue application, particularly for mountain environments & Distance & LDPLM \\ \hline [PERSON] et al. [28] & Path loss model for different environments, such as urban, forests, and coasts & Distance & LDPLM \\ \hline [PERSON] et al. [20] & Path loss model for indoors, campus, city, and suburban environments & Distance & LDPLM \\ \hline [PERSON] et al. [21] & LoRaWAN dataset of operational aspects in campus and urban environments in Czech Republic & Distance, frequency, RSSI, SNR, SF, GW location, EN location, PL & LDPLM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: LoRaWAN path loss models. pressure ([32]), and particulate matter [33]. However, these effects have not been measured in the available LoRaWAN datasets. In that way, this paper provides a comprehensive LoRaWAN measurement campaign carried out in an urban environment, in Medellin, Colombia, for four months. Our measurement setup includes one GW, and four fixed ENs from 2 km to 8 km. The dataset has up to 930.000 observations, including geometric conditions (distance and antennas' heights), link budget features (transmitter powers, antennas' gains, cables and connectors attenuations, carrier frequency, SF, and frame length), propagation variables (RSSI, SNR, ToA, effective signal power (ESP), noise power (P\({}_{n}\)), and consumed energy) and environmental variables (temperature, relative humidity, barometric pressure, and particulate matter). The main contribution of building this dataset is the inclusion of the environmental variables because designers can fit more accurate path loss and shadowing models depending on weather variations. The rest of this paper is organized as follows. Section 2 briefly introduces the main features of the LoRaWAN protocol. Section 3 specifies the logged fields in the dataset. Section 4 shows the experimental setup from the ENs' construction to the database logging. Section 5 shows a possible application of PL modelling using the dataset, including a lognormal combined path loss and shadowing (CPLS) model and an environment-based CPLS model that improves the prediction errors and increases the correlation factor. Finally, Section 6 shows the conclusions.