LoRaWAN Path Loss Measurements in an Indoor Office Setting including Environmental Factors/Conditions

This dataset was collected during a LoRaWAN measurement campaign in a multi-room indoor office environment at the University of Siegen, Germany. It contains over 2.66 million time-stamped records from 6 LoRaWAN nodes transmitting once per minute to a single gateway. Each record includes environmental parameters: temperature, relative humidity, barometric pressure, particulate matter (PM2.5), and carbon dioxide (CO₂); as well as device metadata such as RSSI, SNR, spreading factor (SF), etc. The dataset also includes the effective signal power (ESP) and the noise (NP) for LoRaWAN propagation analysis purposes. The dataset is designed to support research on indoor wireless propagation, distance estimation (localization), and environment-aware modeling, among other IoT use cases and applications in line with the sixth generation (6G) flagship demands. A detailed methodology and initial analysis are presented in our accompanying IEEE Access paper published as: Nahshon Mokua Obiri and Kristof Van Laerhoven, "A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors," in IEEE Access, vol. 13, pp. 83148-83170, 2025, doi: 10.1109/ACCESS.2025.3569164. 🔗 Additional Notes The original dataset pulled directly from InfluxDB is provided as 1.unsorted_combined_measurements_data.csv. The preprocessed version: filtered, sorted, and with variable renaming, aligned to the structure described in our journal paper, is included as 2.aggregated_measurements_data.csv. The fully cleaned dataset, where anomalies have been removed and SF11/SF12 data isolated, is available as 3.cleaned_dataset_per_device.csv. Version note: The IEEE Access paper reports results obtained from an initial six-month subset of this measurement campaign. Version 2 records now include an extended one-year dataset (1 October 2023 to 30 September 2024). Intermediate processing steps and transformations are fully reproducible and documented in the accompanying GitHub repository, where each stage of the pipeline is implemented and version-controlled. Users can generate intermediate versions as needed by following the processing scripts or reviewing the commit history.