Smart Healthy Intelligent Room: Headcount through Air Quality Monitoring
| Autor: | Giuseppe Tricomi, Giovanni Merlino, Antonio Puliafito, Carlo Scaffidi, Salvatore Distefano, Zakaria Benomar, Giovanni Cicceri |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
IoT
Exploit Computer science business.industry Indoor Air Quality Real-time computing Cloud computing 02 engineering and technology Indoor Air Quality IoT Machine Learning Cloud Edge Computing Environmental data Machine Learning Air quality monitoring Indoor air quality 020204 information systems Environmental monitoring 0202 electrical engineering electronic engineering information engineering Edge Computing 020201 artificial intelligence & image processing Enhanced Data Rates for GSM Evolution business Cloud Edge computing |
| Zdroj: | SMARTCOMP |
| DOI: | 10.1109/smartcomp50058.2020.00071 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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