LeafSense: Leveraging Wi-Fi Signals for Monitoring Seasonal Changes

Wi-Fi sensing has attracted growing interest due to the ubiquity of Wi-Fi signals and their promise as a low-cost sensing modality. Knowing when the leaves in a neighborhood have fallen is needed to schedule municipal leaf collection, which is difficult to plan with the means currently available to cities. In this paper, we introduce LeafSense, a system that detects the seasonal falling of leaves through variations in the received signal strength indicator (RSSI) of beacons broadcast by existing Wi-Fi access points (APs). The idea behind LeafSense is that the environment around a transmitter shapes how its signal propagates, and the presence or absence of leaves changes the received signal accordingly. To verify this, we built a sensing rig from commodity devices that monitors 11 Wi-Fi channels in the 2.4 and 5 GHz bands, and drove it through several neighborhoods over multiple seasons. A multi-step filtering pipeline removes vehicle-induced artifacts. Decision Tree and Random Forest classifiers distinguish the presence and absence of leaves with 81% to 94% accuracy, whereas the same classifiers trained on two weeks with unchanged foliage perform near chance. We further show that the sensing rig can be simplified, as two 5 GHz channels are sufficient, covering more than 90% of the surveyed area with 81% to 97% accuracy, reducing the rig to a single Raspberry Pi with two Wi-Fi adapters. Finally, we show that the results are robust to small variations in the vehicle’s route and that AP density is sufficient for reliable sensing.