Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation
| Autor: | Paolo D’Auria, Adrian M. Ionescu, Grazia Fattoruso, Saverio De Vito, Antonio Del Giudice, Tiziana Polichetti, F. Formisano, Elena Esposito, M. Salvato, Gerardo D’Elia, Sergio Ferlito, Girolamo Di Francia, Ettore Massera |
|---|---|
| Přispěvatelé: | De Vito, S., Esposito, E., Massera, E., Formisano, F., Fattoruso, G., Ferlito, S., Del Giudice, A., D'Elia, G., Salvato, M., Polichetti, T., D’Auria, P., Ionescu, Adrian M |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Exposome
010504 meteorology & atmospheric sciences Computer science Internet of Things TP1-1185 010501 environmental sciences 01 natural sciences Biochemistry Article Field (computer science) Analytical Chemistry Predictive medicine Air Pollution Cities Electrical and Electronic Engineering Instrumentation Air quality index sensor network 0105 earth and related environmental sciences Chemical technology air quality monitoring calibration Atomic and Molecular Physics and Optics Term (time) IoT AQ nodes machine learning Enabling Systems engineering Mobile device Wireless sensor network |
| Zdroj: | Sensors Volume 21 Issue 15 Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 5219, p 5219 (2021) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21155219 |
| Popis: | A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |