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.
Pedestrian and vehicular tracking based on Wi-Fi sniffing: a real-world case study / Bertolusso, M.; Pettorru, G.; Spanu, M.; Fadda, M.; Sole, M.; Farina, M.; Anedda, M.; Giusto, D. D.. - (2022), pp. -6. (Intervento presentato al convegno 61st FITCE International Congress Future Telecommunications: Infrastructure and Sustainability, FITCE 2022 tenutosi a ita nel 2022) [10.23919/FITCE56290.2022.9934777].
Pedestrian and vehicular tracking based on Wi-Fi sniffing: a real-world case study
Fadda M.;Anedda M.;
2022-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
