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.