Manjakkal, L. , Mitra, S., Petillot, Y., Shutler, J., Scott, M. , Willander, M. and Dahiya, R. (2021) Connected sensors, innovative sensor deployment and intelligent data analysis for online water quality monitoring. IEEE Internet of Things Journal, 8(18), pp. 13805-13824. (doi: 10.1109/JIOT.2021.3081772)
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Abstract
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 and 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, spatio-temporal 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. Further, intelligent algorithms (e. g. artificial intelligence (AI)) could be employed for standardised data analysis and forecasting. This paper 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 global scale to address global challenges related to food (e.g., aqua/agriculture), drinking water, and health (e.g., water borne diseases).
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Mitra, Dr Srinjoy and Dahiya, Professor Ravinder and Scott, Professor Marian and Manjakkal, Dr Libu |
| Authors: | Manjakkal, L., Mitra, S., Petillot, Y., Shutler, J., Scott, M., Willander, M., and Dahiya, R. |
| College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | IEEE Internet of Things Journal |
| Publisher: | IEEE |
| ISSN: | 2327-4662 |
| ISSN (Online): | 2327-4662 |
| Published Online: | 19 May 2021 |
| Copyright Holders: | Copyright © 2021 The Authors |
| First Published: | First published in IEEE Internet of Things Journal 8(18): 13805-13824 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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