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

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-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).

Citation

Manjakkal, L., Mitra, S., Petillot, Y. R., Shutler, J., Scott, E. M., Willander, M., & Dahiya, R. (2021). Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring. IEEE Internet of Things, 8(18), 13805-13824. https://doi.org/10.1109/jiot.2021.3081772

• Journal Article Type: Article
• Acceptance Date: May 15, 2021
• Online Publication Date: May 19, 2021
• Publication Date: Sep 15, 2021
• Deposit Date: Jul 18, 2022
• Publicly Available Date: Jul 19, 2022
• Journal: IEEE Internet of Things Journal
• Print ISSN: 2327-4662
• Electronic ISSN: 2327-4662
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 18
• Pages: 13805-13824
• DOI: https://doi.org/10.1109/jiot.2021.3081772
• Keywords: Connected sensors, intelligent data analysis, Internet of Things (IoT), robotics sensor deployment, water quality monitoring (WQM)
• Public URL: http://researchrepository.napier.ac.uk/Output/2890063