# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 1 Introduction
However, in the proposed models, these tests are rarely addressed, and in the cases where it is handled, normality is not always met ([18; 19]). Due to the limitations mentioned above, some previous datasets have tackled the problem of modeling the radio frequency features in LoRaWAN networks to improve PL predictions. For instance, in [20], the authors collected 665 samples in the city of Beirut, Lebanon, logging timestamp, distance, frequency (868 MHz), RSSI, signal-to-noise ratio (SNR), GW coordinates, and spreading factor (SF), with a fixed bandwidth (BW) of 125 kHz and a fixed payload of 37 bytes. An LDPLM was enhanced by adding a new feature based on the EN antenna height. After fitting the corresponding model, they found a PL standard deviation of 7.2 dB. Another approach can be found in [21], wherein the authors collected some operational aspects of LoRaWAN in Brno, Czech Republic. Regarding PL modelling, the authors collected data for two months, logging timestamp, RSSI, SNR, timestamp, GW coordinates, EN coordinates, time on air (ToA), frequency (868 MHz), SF, payload size, and frame count. They found that the RSSI fluctuated up to 50 dB, concluding that the conventional propagation models may lead to significant inaccuracies in PL prediction. Furthermore, in [22], the authors deployed nine GWs in central London, UK, and collected timestamp, frequency (868 MHz), RSSI, SNR, SF, and payload size. However, this dataset does not include the distance between the GW and the ENs, so its use is mainly oriented to optimizing network parameters, and PL modelling is impossible. Moreover, in [23], the authors provide a dataset for localization/tracking purposes by using fingerprinting techniques by which the base station position is not needed. This dataset provides the RSSI of 68 base stations, timestamp, SF, and EN coordinates, for three months, in the city center of Antwerp, Belgium. As an application, this approach shows a fingerprint location using clustering techniques, particularly KNN [24], achieving a mean error of 398.4 m. In that way, this dataset is unsuitable for PL modelling purposes. In addition, because our dataset can be mainly used for path loss modeling, we retrieved the most recent approaches for LoRaWAN to further exhibit our dataset's contribution. For instance, [PERSON] et al. [25] proposed a LoRaWAN path loss model to characterize the attenuation in oil palm crops by using an LDPLM mainly based on the distance between the ENs and the GW and the number of canopies and trunks throughout the communication path. [PERSON] et al. [26] fitted a semiempirical machine-learning-based path loss model for LoRaWAN links combining the Friis model with a stepwise multiple linear regression that depends on the frequency, bandwidth, antennas' heights, spreading factor, and distance. [PERSON] et al. [27] performed a measurement campaign by using LoRaWAN in a suburban environment and fitted close-in and floating intercept LDPLMs that depend on the distance between the ENs and the GW; then, they compared their performance versus the Okumura-Hata model. [PERSON] et al. collected path loss measurements in a mountain environment, fitted an LDPLM by using the distance as a predictor variable, and used it in tracking and rescue applications. [PERSON] et al. [28] evaluated coverage and path loss in urban, forest, and coastal environments and fitted a two-slope LDPLM to assess the protocol's reliability in each scenario. Finally, [PERSON] et al. [20] proposed different LDPLMs for indoor, campus, and city environments. The contributions regarding datasets and path loss models are summarized in Table 1.  
As presented in Table 1, the measurement campaigns and path loss models for LoRaWAN are mainly based on distance, frequency, and antennas' heights. However, previous studies have shown that PL variability is also accentuated by the change of some environmental-related variables like temperature [30], relative humidity [31], barometric