This chapter looks into the technical features of state-of-the-art wireless sensors networks for environmental monitoring. Technology advances in low-power and wireless devices have made the deployment of those networks more and more affordable. In addition, wireless sensor networks have become more flexible and adaptable to a wide range of situations. Hence, a framework for their correct implementation will be provided. Then, one specific application about real-time environmental monitoring in support of a model-based predictive control system installed in a metro station will be described. In these applications, filtering, resampling, and post-processing functions must be developed, in order to convert raw data into a dataset arranged in the right format, so that it can inform the algorithms of the control system about the current state of the domain under control. Finally, the whole architecture of the model-based predictive control and its final performances will be reported.

Wireless Real-Time Monitoring System for the Implementation of Intelligent Control in Subways / Carbonari, Alessandro; Vaccarini, Massimo; Mikko, Valta; Maddalena, Nurchis. - STAMPA. - (2016), pp. 141-170. [10.5772/62679]

Wireless Real-Time Monitoring System for the Implementation of Intelligent Control in Subways

CARBONARI, Alessandro
;
Massimo Vaccarini;
2016-01-01

Abstract

This chapter looks into the technical features of state-of-the-art wireless sensors networks for environmental monitoring. Technology advances in low-power and wireless devices have made the deployment of those networks more and more affordable. In addition, wireless sensor networks have become more flexible and adaptable to a wide range of situations. Hence, a framework for their correct implementation will be provided. Then, one specific application about real-time environmental monitoring in support of a model-based predictive control system installed in a metro station will be described. In these applications, filtering, resampling, and post-processing functions must be developed, in order to convert raw data into a dataset arranged in the right format, so that it can inform the algorithms of the control system about the current state of the domain under control. Finally, the whole architecture of the model-based predictive control and its final performances will be reported.
2016
Real-Time Systems
978-953-51-2398-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/236367
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