Speaker
Description
Rainfall monitoring is essential for hydrological forecasting, agricultural management, and urban flood early‑warning. However, traditional methods based on dedicated instruments face limitations in cost, coverage and maintenance. Recently, opportunistic sensing using communication signals has emerged as a promising alternative. In particular, low‑frequency non‑line‑of‑sight downlink signals from consumer devices offer wide coverage and dense deployment potential, yet reliable rainfall signature extraction remains challenging due to complex propagation environments. To address this issue, this study proposes a two‑stage rainfall monitoring framework based on a Random Forest (RF) model. Using downlink signal parameters collected from ordinary 4G smartphones under a controlled static setup—where terminals remain stationary and are connected to a single base station—the method accomplishes both rainfall detection and rain‑rate estimation. The experimental design emulates fixed IoT terminal scenarios such as smart lampposts. A cascade of RF classifier and regressor is constructed to effectively capture the nonlinear relationships between signal features and rainfall state/intensity. Feature‑importance analysis indicates that sliding‑window statistical moments, especially kurtosis and skewness, play a decisive role in rainfall identification. Experimental results show that the proposed classifier achieves Matthews correlation coefficient of 0.693 and 0.597 for the two devices on the test set, with geometric means above 0.89. The regressor attains Pearson correlation coefficients of 0.66 and 0.64 for rain‑rate estimation, with root‑mean‑square errors of 2.24 mm/h and 1.18 mm/h, respectively. This work demonstrates the feasibility of low‑cost, distributed rainfall monitoring using commercial mobile signals and provides a novel, IoT‑friendly approach for environmental sensing with consumer electronics.