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Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring

In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are oft...

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Autores principales: Strigaro, Daniele, Cannata, Massimiliano, Lepori, Fabio, Capelli, Camilla, Lami, Andrea, Manca, Dario, Seno, Silvio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459782/
https://www.ncbi.nlm.nih.gov/pubmed/36081143
http://dx.doi.org/10.3390/s22176684
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author Strigaro, Daniele
Cannata, Massimiliano
Lepori, Fabio
Capelli, Camilla
Lami, Andrea
Manca, Dario
Seno, Silvio
author_facet Strigaro, Daniele
Cannata, Massimiliano
Lepori, Fabio
Capelli, Camilla
Lami, Andrea
Manca, Dario
Seno, Silvio
author_sort Strigaro, Daniele
collection PubMed
description In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.
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spelling pubmed-94597822022-09-10 Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring Strigaro, Daniele Cannata, Massimiliano Lepori, Fabio Capelli, Camilla Lami, Andrea Manca, Dario Seno, Silvio Sensors (Basel) Article In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices. MDPI 2022-09-04 /pmc/articles/PMC9459782/ /pubmed/36081143 http://dx.doi.org/10.3390/s22176684 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Strigaro, Daniele
Cannata, Massimiliano
Lepori, Fabio
Capelli, Camilla
Lami, Andrea
Manca, Dario
Seno, Silvio
Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
title Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
title_full Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
title_fullStr Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
title_full_unstemmed Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
title_short Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring
title_sort open and cost-effective digital ecosystem for lake water quality monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459782/
https://www.ncbi.nlm.nih.gov/pubmed/36081143
http://dx.doi.org/10.3390/s22176684
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