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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9459782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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