Exploring Environmental Information From Smartphone Signals: A Light Indoor Stationary Experimental Study for Rainfall Detection

The significance of rainfall detection is generally acknowledged, and the linked opportunistic approach to giving it new momentum has also been extensively demonstrated. This study conducted a rainfall monitoring experiment using two stationary smartphones in a light indoor setting, receiving downlink signals from a long-term evolution (LTE) base station for two months. The analysis reveals that the received signals are not stable during dry periods, but the reference signal receiving power (RSRP) and reference signal strength indicator (RSSI) parameters can produce a certain degree of degradation during rainfall. To address the concern about frequent switching of connection to the base station resulting in different fluctuation levels, the standardized standard deviations of the four signaling parameters for different time windows were extracted as features to build a dry-rainy classification model. Furthermore, a rain rate class identification model is also established based on the extraction of the specific rain-induced attenuation and standardized standard deviation. The experiment mentioned has shown promising results in rainfall monitoring based on widely available opportunistic signal sources from wireless terminals.