Pedestrian and vehicular tracking based on Wi-Fi sniffing: a real-world case study

This paper presents an innovative vehicle monitoring system based on Wi-Fi sniffing devices and real-time data processing using machine learning techniques. Our solution involves the construction of a neural network-based multiclass classifier that can classify the incoming Wi-Fi signal from many sources based on the received signal strength. The solution was carried out by training the neural network to predict different output classes corresponding to different vehicular (0–30 Km/h, 30-60 Km/h, 60-90 Km/h, 90-120 Km/h) and several pedestrian speed ranges among 0-15 Km/h.