A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors

TL;DR

This paper provides a detailed dataset of LoRaWAN path loss measurements in an indoor office, analyzing how environmental factors and occupancy influence signal attenuation and proposing an improved path loss model.

Contribution

It introduces a comprehensive indoor LoRaWAN dataset and develops an enhanced path loss model that accounts for environmental and structural factors, improving prediction accuracy.

Findings

Environmental factors can cause signal attenuation variations up to 10.58 dB.

The improved model reduces RMSE from 10.58 dB to 8.04 dB.

Incorporating environmental parameters increases the model's R² from 0.6917 to 0.8222.

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.