Toward Scalable and Low-Cost Wi-Fi Sensing: Preventing Animal-Vehicle Collisions on Rural Roads

Rural roads are vital for the transportation of persons and goods, but are vulnerable to animal-vehicle collisions, which cause substantial environmental, economic and public health impacts every year. WiFi sensing has recently emerged as a cost-effective approach to detect hazardous situations on roads, overcoming some limitations of traditional monitoring systems. However, scaling WiFi sensing to cover long road sections introduces challenges, including interference between WiFi nodes and with other protocols operating in the same Industrial, Scientific, and Medical (ISM) bands, such as IEEE 802.15.4. In this work, we present a scalable, low-cost architecture for WiFi-based monitoring of rural roads. We develop and optimize machine learning models for real-time detection and classification of pedestrians, vehicles, and animals using low-cost IoT devices, achieving over 95% accuracy and a miss rate of only 0.7%. We evaluate the models under varied conditions, including different sensor distances, light rain, and previously unseen rural locations, demonstrating robustness across environmental variations. Additionally, we conduct network simulations to assess the coexistence of WiFi sensing with IEEE 802.15.4 communications and perform LoRaWAN experiments in rural environments to validate long-range data transmission. We also release an open dataset containing all evaluated scenarios to support reproducibility and further research. Our results confirm the feasibility of the proposed system in terms of sensing accuracy, network performance, and deployment cost.