This paper addresses the issue of monitoring and tracking people and vehicles within smart cities. The actors in this work jointly cooperate in sensing, sensible data processing, anonymized data delivery, and data processing, with the final goal of providing real-time mapping of vehicular and pedestrian concentration conditions. The classification of conditions can bring out critical situations that can be communicated in realtime to citizens. Tests were conducted in the city of Cagliari, Italy.
A Machine Learning-based Approach for Vehicular Tracking in Low Power Wide Area Networks / Bertolusso, M; Spanu, M; Pettorru, G; Anedda, M; Fadda, M; Girau, R; Farina, M; Giusto, D. - ELETTRONICO. - (2022), pp. 1-6. (Intervento presentato al convegno 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) tenutosi a Bilbao, Spain nel 15-17 June 2022) [10.1109/BMSB55706.2022.9828755].
A Machine Learning-based Approach for Vehicular Tracking in Low Power Wide Area Networks
Girau, R;
2022
Abstract
This paper addresses the issue of monitoring and tracking people and vehicles within smart cities. The actors in this work jointly cooperate in sensing, sensible data processing, anonymized data delivery, and data processing, with the final goal of providing real-time mapping of vehicular and pedestrian concentration conditions. The classification of conditions can bring out critical situations that can be communicated in realtime to citizens. Tests were conducted in the city of Cagliari, Italy.| File | Dimensione | Formato | |
|---|---|---|---|
|
A Machine Learning-based Approach for Vehicular Tracking in Low Power Wide Area Networks.pdf
accesso aperto
Tipo:
Postprint
Licenza:
Licenza per accesso libero gratuito
Dimensione
6.42 MB
Formato
Adobe PDF
|
6.42 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
