Year: 2025
Type: article
Abstract: Accurate and real-time detection of precipitation phases is essential for hydrological modeling, water resource management, and climate impact assessments. However, conventional methods struggle to distinguish precipitation phase near freezing and are unsuitable for distributed deployment in remote, complex terrain due to cost, size, and power constraints. In this work, we present an integrated acoustic sensing system that combines machine learning on the edge with acoustic sensing and Mel-Frequency Cepstral Coefficients (MFCC)-based feature extraction, all implemented on our designed edge device. The device is low-cost, with LAN and WAN capabilities for near real-time reporting, and can integrate multiple sensors for snow hydrology studies, in addition to precipitation phase detection. We present the design, fabrication, and validation of the Advanced Unified Rainfall and Atmospheric Monitoring (AURA) system, integrating sensors for temperature, humidity, ultrasonic snow depth, solar radiation, and wind velocity. AURA features an embedded machine learning algorithm to enable accurate classification of precipitation phases. Short-time Fourier Transform (STFT) and MFCCs are computed on each sensor node from recorded precipitation acoustics employing two novel Mel filter bank computation methods: a lookup table and a linear model. Using support vector machine (SVM) and random forest (RF) classifiers, the RF model achieves testing accuracy of 97.35% on simulated precipitation acoustics and 85.67% in consolidated class (CC) environmental recordings. The SVM classifier achieves 98.07% accuracy on simulated acoustics and 85.99% on CC environmental recordings. The low-power AURA network employs a LoRa star topology with Time-Division Multiple Access (TDMA) and an Iridium-enabled gateway for reliable data transmission from remote, offline locations. Field testing and ground-truth validation confirm system reliability.
Source: IEEE Internet of Things Journal
Authors: Soheyl Faghir Hagh, Jordan Bourdeau, Julia Sober, Rachael Chertok, Christopher Jepsen, Parmida Amngostar, Lucas Levine, Casey Forey, Tian Xia, Christian Skalka
Institution: University of Vermont
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,645
OpenAlex ID: W4414404613