A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors
Nahshon Mokua Obiri, Kristof Van Laerhoven
TL;DR
This study addresses indoor LoRaWAN propagation by collecting a six-month, large-scale dataset in a university office using six end devices and one gateway, capturing RSSI, SNR, and rich environmental metadata (temperature, humidity, CO$_2$, barometric pressure, PM$_{2.5}$). It introduces two path-loss models—LDPLSM-MW and the enhanced LDPLSM-MW-EP—that incorporate multi-wall attenuation and environmental parameters, with parameter estimation via Levenberg–Marquardt and validation through a robust ML pipeline and 5-fold cross-validation. The enhanced LDPLSM-MW-EP substantially improves predictive accuracy (RMSE from $10.58$ to $8.04$ dB; $R^{2}$ from $0.6914$ to $0.8219$) and provides better residual symmetry, demonstrating the value of environment-aware modeling for indoor LoRaWAN deployments. The dataset and tooling are publicly available, enabling researchers and practitioners to advance site-specific planning, energy optimization, and reliability in indoor IoT networks.
Abstract
This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen, City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO$_2$) concentration, barometric pressure, and particulate matter levels (PM$_{2.5}$). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R$^2$) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. This dataset offers a solid foundation for future research and development in indoor wireless communication.