Articles | Volume 11, issue 1
Drink. Water Eng. Sci., 11, 9–17, 2018
https://doi.org/10.5194/dwes-11-9-2018
Drink. Water Eng. Sci., 11, 9–17, 2018
https://doi.org/10.5194/dwes-11-9-2018

Research article 05 Feb 2018

Research article | 05 Feb 2018

Towards a cyber-physical era: soft computing framework based multi-sensor array for water quality monitoring

Jyotirmoy Bhardwaj et al.

Related authors

Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a Python framework approach
Punit Khatri, Karunesh Kumar Gupta, and Raj Kumar Gupta
Drink. Water Eng. Sci., 12, 31–37, https://doi.org/10.5194/dwes-12-31-2019,https://doi.org/10.5194/dwes-12-31-2019, 2019
Short summary

Related subject area

Tools: Sensoring and monitoring
Design methodology to determine the water quality monitoring strategy of a surface water treatment plant in the Netherlands
Petra Ross, Kim van Schagen, and Luuk Rietveld
Drink. Water Eng. Sci., 13, 1–13, https://doi.org/10.5194/dwes-13-1-2020,https://doi.org/10.5194/dwes-13-1-2020, 2020
Short summary
Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a Python framework approach
Punit Khatri, Karunesh Kumar Gupta, and Raj Kumar Gupta
Drink. Water Eng. Sci., 12, 31–37, https://doi.org/10.5194/dwes-12-31-2019,https://doi.org/10.5194/dwes-12-31-2019, 2019
Short summary
Real-time hydraulic interval state estimation for water transport networks: a case study
Stelios G. Vrachimis, Demetrios G. Eliades, and Marios M. Polycarpou
Drink. Water Eng. Sci., 11, 19–24, https://doi.org/10.5194/dwes-11-19-2018,https://doi.org/10.5194/dwes-11-19-2018, 2018
Short summary
Online total organic carbon (TOC) monitoring for water and wastewater treatment plants processes and operations optimization
Céline Assmann, Amanda Scott, and Dondra Biller
Drink. Water Eng. Sci., 10, 61–68, https://doi.org/10.5194/dwes-10-61-2017,https://doi.org/10.5194/dwes-10-61-2017, 2017
Short summary
Application of machine learning for real-time evaluation of salinity (or TDS) in drinking water using photonic sensors
Sandip Kumar Roy and Preeta Sharan
Drink. Water Eng. Sci., 9, 37–45, https://doi.org/10.5194/dwes-9-37-2016,https://doi.org/10.5194/dwes-9-37-2016, 2016
Short summary

Cited articles

Adhikari, U., Morris, T., and Pan, S.: WAMS cyber-physical test bed for power system, cybersecurity study, and data mining, IEEE T. Smart Grid, 8, 2744–2753, https://doi.org/10.1109/TSG.2016.2537210, 2016. 
Ali, S., Qaisar, S. B., Saeed, H., Khan, M. F., Naeem, M., and Anpalagan, A.: Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring, Sensors, 15, 7172–7205, 2015. 
Arduino: Arduino MEGA 2560 Rev 3, available at: https://store.arduino.cc/usa/arduino-mega-2560-rev3, last access: 23 September 2017. 
Ari, N. and Mamatnazarova, N.: Symbolic python, in: IEEE 11th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, Nigeria, 29 September–1 October 2014, 1–8, https://doi.org/10.1109/ICECCO.2014.6997580, 2014. 
Atlas Scientific: Atlas Scientific Environmental Robotics, available at: https://www.atlas-scientific.com/, last access: 23 September 2017. 
Download
Short summary
Reliable and effective continuous water quality monitoring has always been challenging. To detect water quality, deployment of multiple sensor nodes in a water distribution network generates complex and convoluted data sets. This paper demonstrates the implementation of a cyber-physical system along with soft-computing approaches (Python and fuzzy). The designed system monitors water quality in real time, simplifies the complexity of sensor data and assists water engineers in decision making.