实测办公环境信号衰减受温湿度等影响，最大波动达10.58dB
A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors
- 在6个终端+1个网关部署下，测量温湿度等环境参数对信号影响
- 环境变化导致信号衰减最高变化10.58dB，显著影响传播稳定性
- 提出融合环境参数的改进模型，误差降低至8.04dB，适合物联网优化
本文在德国锡根大学办公楼内构建了包含六个终端设备（EDs）和一个室内网关（GW）的LoRaWAN网络，系统采集了温度、相对湿度、二氧化碳浓度、气压及颗粒物水平（PM₂.₅）等环境因素下的信号强度数据，包括接收信号强度指示（RSSI）与信噪比（SNR）。实证分析表明，反射、散射、干扰、人员活动及家具变动等瞬态现象可引起信号衰减高达10.58 dB，凸显室内传播的动态特性。基于此数据，我们测试并评估了一种融合结构遮挡（多墙体）与环境参数的改进对数距离路径损耗与阴影模型（LDPLSM-MW-EP），相较仅考虑多墙体的基线模型（LDPLSM-MW），其均方根误差（RMSE）由10.58 dB降至8.04 dB，决定系数（R²）从0.6917提升至0.8222。该模型通过捕捉环境动态效应，为优化功耗、延长设备电池寿命、增强室内物联网网络可靠性提供了有力支持。本数据集为未来室内无线通信研究奠定坚实基础。
原文摘要 · Abstract (English)
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.
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