In recent years, research in IoT-enabled smart building solutions has accelerated significantly, thanks to the coming of age of wireless distributed sensing hardware, protocols and software. Moreover the computational capability of mobile and wireless platforms is now significant and complex tasks can be distributed and executed over remote low-power nodes. Data can be processed locally on-board to reduce network overhead and to increase architectural scalability. We present an heterogeneous sensor network that makes use of low power sensors and cameras to monitor a building's environmental conditions and to correlate them with the number of people inside the monitored environment. Occupancy monitoring is a critical missing link in smart buildings especially for rooms which may have large number of occupants (e.g. classrooms), because it heavily affects control strategies and impacts environmental conditions. Our system estimates the number of people in classrooms by using a novel people counting algorithm which runs in real-time, even on limited resources platforms. Finally, data is collected in a IoT-cloud-based infrastructure, and used to trigger remote actions.

0, 1, 2, many - A classroom occupancy monitoring system for smart public buildings

Brunelli, Davide;
2015

Abstract

In recent years, research in IoT-enabled smart building solutions has accelerated significantly, thanks to the coming of age of wireless distributed sensing hardware, protocols and software. Moreover the computational capability of mobile and wireless platforms is now significant and complex tasks can be distributed and executed over remote low-power nodes. Data can be processed locally on-board to reduce network overhead and to increase architectural scalability. We present an heterogeneous sensor network that makes use of low power sensors and cameras to monitor a building's environmental conditions and to correlate them with the number of people inside the monitored environment. Occupancy monitoring is a critical missing link in smart buildings especially for rooms which may have large number of occupants (e.g. classrooms), because it heavily affects control strategies and impacts environmental conditions. Our system estimates the number of people in classrooms by using a novel people counting algorithm which runs in real-time, even on limited resources platforms. Finally, data is collected in a IoT-cloud-based infrastructure, and used to trigger remote actions.
Conference on Design and Architectures for Signal and Image Processing, DASIP
Piscataway, NJ
IEEE Computer Society
9791092279054
9791092279054
Paci, Francesco; Brunelli, Davide; Benini, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/112260
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