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

Fadda, Mauro;Sole, Mariella;Anedda, Matteo
;
Giusto, Daniele D.
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.
2022
978-1-6654-5126-0
File in questo prodotto:
File Dimensione Formato  
2022118291.pdf

Solo gestori archivio

Tipologia: versione editoriale (VoR)
Dimensione 1.46 MB
Formato Adobe PDF
1.46 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/344113
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact