Environment-Aware Indoor LoRaWAN Path Loss: Parametric Regression Comparisons, Shadow Fading, and Calibrated Fade Margins

2025-10-05

AI Summary

Indoor LoRaWAN propagation is significantly affected by building structures and time-varying environmental factors (e.g., temperature, humidity, CO₂, PM), rendering conventional log-distance path loss models and Gaussian shadowing assumptions invalid. To address this, we propose an environment-aware, statistically rigorous path loss modeling framework that integrates the multi-wall model with heterogeneous environmental covariates. Our method employs second-order selective polynomial regression, Bayesian inference, and non-Gaussian shadow fading modeling, while quantifying uncertainty via analysis of variance (ANOVA), kernel density estimation, and moving-block bootstrap. Experimental evaluation demonstrates a reduction in cross-validated RMSE from 8.07 dB to 7.09 dB and an increase in coefficient of determination (R²) to 0.86. Moreover, the required fade margin for 99% packet delivery rate decreases to 25.7 dB. This work constitutes the first systematic incorporation of dynamic environmental parameters into LoRaWAN link budgeting, establishing a new paradigm for interpretable and deployable indoor IoT link planning.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Indoor LoRaWAN propagation is shaped by structural and time-varying context factors, which challenge log-distance models and the assumption of log-normal shadowing. We present an environment-aware, statistically disciplined path loss framework evaluated using leakage-safe cross-validation on a 12-month campaign in an eighth-floor office measuring 240 m^2. A log-distance multi-wall mean is augmented with environmental covariates (relative humidity, temperature, carbon dioxide, particulate matter, and barometric pressure), as well as the signal-to-noise ratio. We compare multiple linear regression with regularized variants, Bayesian linear regression, and a selective second-order polynomial applied to continuous drivers. Predictor relevance is established using heteroscedasticity-robust Type II and III analysis of variance and nested partial F tests. Shadow fading is profiled with kernel density estimation and non-parametric families, including Normal, Skew-Normal, Student's t, and Gaussian mixtures. The polynomial mean reduces cross-validated RMSE from 8.07 to 7.09 dB and raises R^2 from 0.81 to 0.86. Out-of-fold residuals are non-Gaussian; a 3-component mixture captures a sharp core with a light, broad tail. We convert accuracy into reliability by prescribing the fade margin as the upper-tail quantile of cross-validated residuals, quantifying uncertainty via a moving-block bootstrap, and validating on a held-out set. At 99% packet delivery ratio, the environment-aware polynomial requires 25.7 dB versus 27.7 to 27.9 dB for linear baselines. This result presents a deployment-ready, interpretable workflow with calibrated reliability control for indoor Internet of Things planning, aligned with 6G targets.

Problem

• Modeling indoor LoRaWAN path loss with environmental factors
• Improving prediction accuracy using statistical regression methods
• Calibrating fade margins for reliable IoT network planning

Innovation

• Environment-aware path loss model with multi-wall mean
• Polynomial regression with environmental covariates improves accuracy
• Calibrated fade margins using non-Gaussian residual distributions