European Union has been active in recent years in promoting the enhancement of railway transports through research and development programs. The related challenges are even more critical for freight trains, because there is neither power supply guaranteed for every wagon nor data connection from and towards the locomotive. In this context the present paper proposes a plug-and-play solution for continuous monitoring of both wagon units and trains in order to improve freight trains safety and enable a more efficient overall management. This paper reports on the design and development of an integrated and heterogeneous network that involves on board communication/data exchange through an 868 MHz WSN (Wireless Sensor Network) component, data communications across a mobile network through M2M (Machine-to-Machine) SIMs, data collection on the Cloud for processing and detection of anomalies. A lab prototype has been built and tested, and is ready for large scale testing in real scenarios.

A cloud-based heterogeneous wireless platform for monitoring and management of freight trains

CHIOCCHIO, SANDRO;PERSIA, ARIANNA;SANTUCCI, FORTUNATO;
2016-01-01

Abstract

European Union has been active in recent years in promoting the enhancement of railway transports through research and development programs. The related challenges are even more critical for freight trains, because there is neither power supply guaranteed for every wagon nor data connection from and towards the locomotive. In this context the present paper proposes a plug-and-play solution for continuous monitoring of both wagon units and trains in order to improve freight trains safety and enable a more efficient overall management. This paper reports on the design and development of an integrated and heterogeneous network that involves on board communication/data exchange through an 868 MHz WSN (Wireless Sensor Network) component, data communications across a mobile network through M2M (Machine-to-Machine) SIMs, data collection on the Cloud for processing and detection of anomalies. A lab prototype has been built and tested, and is ready for large scale testing in real scenarios.
2016
9781467388184
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/112180
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