Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring
The sensor technology for water quality monitoring (WQM) has improved during recent years. The cost-effective sensorised tools that can autonomously measure the essential physical-chemical-biological (PCB) variables are now readily available and are being deployed on buoys, boats, and ships. Yet, th...
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Linköpings universitet, Fysik, elektroteknik och matematik
2021
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Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180887 https://doi.org/10.1109/JIOT.2021.3081772 |
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ftlinkoepinguniv:oai:DiVA.org:liu-180887 2023-05-15T17:53:55+02:00 Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring Manjakkal, Libu Mitra, Srinjoy Petillot, Yvan R. Shutler, Jamie Scott, E. Marian Willander, Magnus Dahiya, Ravinder 2021 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180887 https://doi.org/10.1109/JIOT.2021.3081772 eng eng Linköpings universitet, Fysik, elektroteknik och matematik Linköpings universitet, Tekniska fakulteten Univ Glasgow, Scotland Univ Edinburgh, Scotland Univ Heriot Watt, Scotland Univ Exeter, England IEEE Internet of Things Journal, 2327-4662, 2021, 8:18, s. 13805-13824 orcid:0000-0001-6235-7038 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180887 doi:10.1109/JIOT.2021.3081772 ISI:000703315500002 info:eu-repo/semantics/openAccess Sensors Monitoring Robot sensing systems Water pollution Intelligent sensors Water quality Pollution measurement Connected sensors intelligent data analysis Internet of Things (IoT) robotics sensor deployment water quality monitoring (WQM) Communication Systems Kommunikationssystem Article in journal info:eu-repo/semantics/article text 2021 ftlinkoepinguniv https://doi.org/10.1109/JIOT.2021.3081772 2022-05-01T08:24:35Z The sensor technology for water quality monitoring (WQM) has improved during recent years. The cost-effective sensorised tools that can autonomously measure the essential physical-chemical-biological (PCB) variables are now readily available and are being deployed on buoys, boats, and ships. Yet, there is a disconnect between the data quality, data gathering, and data analysis due to the lack of standardized approaches for data collection and processing, spatiotemporal variation of key parameters in water bodies and new contaminants. Such gaps can be bridged with a network of multiparametric sensor systems deployed in water bodies using autonomous vehicles, such as marine robots and aerial vehicles to broaden the data coverage in space and time. Furthermore, intelligent algorithms [e.g., artificial intelligence (AI)] could be employed for standardized data analysis and forecasting. This article presents a comprehensive review of the sensors, deployment, and analysis technologies for WQM. A network of networked water bodies could enhance the global data intercomparability and enable WQM at a global scale to address global challenges related to food (e.g., aqua/agriculture), drinking water, and health (e.g., water-borne diseases). Funding Agencies|Engineering and Physical Sciences (EPSRC)UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/R029644/1]; ORCA hub [EP/R026173/1]; European Commission through AQUASENSE [H2020-MSCA-ITN-2018-813680] Article in Journal/Newspaper Orca LIU - Linköping University: Publications (DiVA) IEEE Internet of Things Journal 8 18 13805 13824 |
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Open Polar |
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LIU - Linköping University: Publications (DiVA) |
op_collection_id |
ftlinkoepinguniv |
language |
English |
topic |
Sensors Monitoring Robot sensing systems Water pollution Intelligent sensors Water quality Pollution measurement Connected sensors intelligent data analysis Internet of Things (IoT) robotics sensor deployment water quality monitoring (WQM) Communication Systems Kommunikationssystem |
spellingShingle |
Sensors Monitoring Robot sensing systems Water pollution Intelligent sensors Water quality Pollution measurement Connected sensors intelligent data analysis Internet of Things (IoT) robotics sensor deployment water quality monitoring (WQM) Communication Systems Kommunikationssystem Manjakkal, Libu Mitra, Srinjoy Petillot, Yvan R. Shutler, Jamie Scott, E. Marian Willander, Magnus Dahiya, Ravinder Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring |
topic_facet |
Sensors Monitoring Robot sensing systems Water pollution Intelligent sensors Water quality Pollution measurement Connected sensors intelligent data analysis Internet of Things (IoT) robotics sensor deployment water quality monitoring (WQM) Communication Systems Kommunikationssystem |
description |
The sensor technology for water quality monitoring (WQM) has improved during recent years. The cost-effective sensorised tools that can autonomously measure the essential physical-chemical-biological (PCB) variables are now readily available and are being deployed on buoys, boats, and ships. Yet, there is a disconnect between the data quality, data gathering, and data analysis due to the lack of standardized approaches for data collection and processing, spatiotemporal variation of key parameters in water bodies and new contaminants. Such gaps can be bridged with a network of multiparametric sensor systems deployed in water bodies using autonomous vehicles, such as marine robots and aerial vehicles to broaden the data coverage in space and time. Furthermore, intelligent algorithms [e.g., artificial intelligence (AI)] could be employed for standardized data analysis and forecasting. This article presents a comprehensive review of the sensors, deployment, and analysis technologies for WQM. A network of networked water bodies could enhance the global data intercomparability and enable WQM at a global scale to address global challenges related to food (e.g., aqua/agriculture), drinking water, and health (e.g., water-borne diseases). Funding Agencies|Engineering and Physical Sciences (EPSRC)UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/R029644/1]; ORCA hub [EP/R026173/1]; European Commission through AQUASENSE [H2020-MSCA-ITN-2018-813680] |
format |
Article in Journal/Newspaper |
author |
Manjakkal, Libu Mitra, Srinjoy Petillot, Yvan R. Shutler, Jamie Scott, E. Marian Willander, Magnus Dahiya, Ravinder |
author_facet |
Manjakkal, Libu Mitra, Srinjoy Petillot, Yvan R. Shutler, Jamie Scott, E. Marian Willander, Magnus Dahiya, Ravinder |
author_sort |
Manjakkal, Libu |
title |
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring |
title_short |
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring |
title_full |
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring |
title_fullStr |
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring |
title_full_unstemmed |
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring |
title_sort |
connected sensors, innovative sensor deployment, and intelligent data analysis for online water quality monitoring |
publisher |
Linköpings universitet, Fysik, elektroteknik och matematik |
publishDate |
2021 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180887 https://doi.org/10.1109/JIOT.2021.3081772 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
IEEE Internet of Things Journal, 2327-4662, 2021, 8:18, s. 13805-13824 orcid:0000-0001-6235-7038 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-180887 doi:10.1109/JIOT.2021.3081772 ISI:000703315500002 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1109/JIOT.2021.3081772 |
container_title |
IEEE Internet of Things Journal |
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8 |
container_issue |
18 |
container_start_page |
13805 |
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13824 |
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