TY - JOUR
T1 - Connected Sensors, Innovative Sensor Deployment and Intelligent Data Analysis for Online Water Quality Monitoring
AU - Manjakkal, L.
AU - Mitra, S.
AU - Petillo, Y.
AU - Shutler, J.
AU - Scott, M.
AU - Willander, M.
AU - Dahiya, R.
N1 - Funding Information:
Manuscript received December 31, 2020; revised April 12, 2021; accepted May 15, 2021. Date of publication May 19, 2021; date of current version September 6, 2021. This work was supported in part by the Engineering and Physical Sciences (EPSRC) Engineering Fellowship for Growth under Grant EP/R029644/1 and ORCA hub Grant EP/R026173/1, and in part by the European Commission through AQUASENSE under Project H2020-MSCA-ITN-2018-813680. (Corresponding author: Ravinder Dahiya.) Libu Manjakkal and Ravinder Dahiya are with the Bendable Electronics and Sensing Technologies Group, University of Glasgow, Glasgow G12 8QQ, U.K. (e-mail: ravinder.dahiya@glasgow.ac.uk).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/9/15
Y1 - 2021/9/15
N2 - 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).
AB - 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).
KW - Connected Sensors
KW - Intelligent Data Analysis.
KW - Intelligent sensors
KW - Internet of Things
KW - Monitoring
KW - Pollution measurement
KW - Robot sensing systems
KW - Robotics
KW - Sensor Deployment
KW - Sensors
KW - Water pollution
KW - Water quality
KW - Water quality monitoring
UR - http://www.scopus.com/inward/record.url?scp=85107228873&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2021.3081772
DO - 10.1109/JIOT.2021.3081772
M3 - Article
AN - SCOPUS:85107228873
SN - 2327-4662
VL - 8
SP - 13805
EP - 13824
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 18
ER -
