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 (0-30Km/h,30-60Km/h, 60-90Km/h, 90-120Km/h) and several pedestrian speed ranges among 0-15Km/h.
A passive Wi-Fi based monitoring system for urban flows detection / Bertolusso, M.; Pettorru, G.; Spanu, M.; Fadda, M.; Sole, M.; Anedda, M.; Giusto, D. D.. - (2022), pp. 66-70. (Intervento presentato al convegno 2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2022 tenutosi a idn nel 2022) [10.1109/IAICT55358.2022.9887478].
A passive Wi-Fi based monitoring system for urban flows detection
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 (0-30Km/h,30-60Km/h, 60-90Km/h, 90-120Km/h) and several pedestrian speed ranges among 0-15Km/h.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
