In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.

Smart Healthy Intelligent Room: Headcount through Air Quality Monitoring

Cicceri, Giovanni
Primo
Writing – Original Draft Preparation
;
Scaffidi, Carlo
Writing – Original Draft Preparation
;
Benomar, Zakaria
Writing – Original Draft Preparation
;
Distefano, Salvatore
Penultimo
Writing – Review & Editing
;
Puliafito, Antonio
Ultimo
Supervision
;
Tricomi, Giuseppe
Writing – Original Draft Preparation
;
Merlino, Giovanni
Writing – Review & Editing
2020

Abstract

In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3180667
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