Smart City Gnosys

Smart city article details

Title	Pedestrian And Vehicular Tracking Based On Wi-Fi Sniffing: A Real-World Case Study
ID_Doc	41527
Authors	Bertolusso M.; Pettorru G.; Spanu M.; Fadda M.; Sole M.; Farina M.; Anedda M.; Giusto D.D.
Year	2022
Published	2022 61st FITCE International Congress Future Telecommunications: Infrastructure and Sustainability, FITCE 2022
DOI	http://dx.doi.org/10.23919/FITCE56290.2022.9934777
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. © 2022 AEIT.
Author Keywords	Internet of Services; Localization and Location-based Services; Machine Learning; Smart City; Smart Logistics; Social IoT

Id	Similarity	Authors	Title	Published
3628	0.951	Bertolusso M.; Pettorru G.; Spanu M.; Fadda M.; Sole M.; Anedda M.; Giusto D.D.	A Passive Wi-Fi Based Monitoring System For Urban Flows Detection	Proceedings of the 2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2022 (2022)
60986	0.869	Bertolusso M.; Spanu M.; Anedda M.; Fadda M.; Giusto D.D.	Vehicular And Pedestrian Traffic Monitoring System In Smart City Scenarios	7th IEEE World Forum on Internet of Things, WF-IoT 2021 (2021)
62003	0.852	Kanschat R.; Gupta S.; Degbelo A.	Wireless-Signal-Based Vehicle Counting And Classification In Different Road Environments	IEEE Open Journal of Intelligent Transportation Systems, 3 (2022)