# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 4 Selected Studies on CPS Water Supply Network Management
### CPS Water Quality Monitoring Applications  
In the CPS applications to monitor water distribution networks, the type of sensing devices to be deployed for each application depends on the measured parameters. In the water quality and pollutant detection applications, parameters such as pH, temperature, turbidity, dissolved oxygen concentration, and chlorine residual levels, amongst others, are essential. A summary of these parameters and their threshold values is given in Table 3. As may be noticed in Table 3, the World Health Organization [97] provides a pH range of 6.5 to 7.85 as a standard value to prevent excessive acidic or alkaline water consumption. Also, a temperature of 15 \({}^{\circ}\)C is considered safe for a drinking water system. If these parameters are sensed to exceed the limit, an alarm is initiated and the appropriate agencies or utilities at the monitoring center make optimal decisions. In the study conducted by [PERSON] and [PERSON] [12], advanced remote sensing and sensor technologies were utilized to acquire a huge amount of data to capture changes in water quality parameters along a pipe network. The proposed system is also equipped with a cyber-interface, which provides a platform for early warning awareness about water contaminant. While the system is still at its initial stage, the associated cost of implementation is a major concern as the system deploys a huge number of advanced remote sensing technologies for real-time measurements. In [PERSON] et al. [101], a low-cost system for real-time monitoring of water quality parameters was developed. The system also uses a sensor array to acquire data related to turbidity, oxidation reduction potential, temperature, pH, electrical conductivity, and flow rate within the pipe. Also, communication of the sensor readings to a processing center is provided via a ZigBee module. The research effort of [PERSON] et al. [9] considered the application of a CPS in both the drinking water system and the drainage system. Different from other studies [12; 101], the proposed system integrates a model prediction control for optimizing control with current measurements of the installed sensors and to predict the future behavior of the system. The use of a multisensor array for real-time measurement of water quality parameters in a water distribution network was proposed by [PERSON] et al. [100]. The sensing framework acquires data related to pH, dissolve oxygen concentration, oxygen reduction potential, conductivity, and temperature. Also, a soft computing framework (using python and fuzzy logic) is integrated into the system for computational modelling and to facilitate decision making. Similar to the studies presented in [9; 12; 16], the system was able to monitor the water quality parameters in the WDN. However, the accuracy and the operational performance of such a system depends largely on the sensing framework and the robustness of the integrated sensors. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measured Parameters** & **Sensor Type** & **Threshold Value** & **References** \\ \hline Temperature & Temperature sensors & 15 \({}^{\circ}\)C & WHO [97] \\ \hline pH & pH sensors & 6.5–7.85 & WHO [97] \\ \hline Dissolve oxygen concentration & Dissolve oxygen sensors & 5–6 mg/L & [98; 99] \\ \hline Sodium & – & 200 mg/L & WHO [97] \\ \hline Fluoride content & – & 4 mg/L & WHO [97] \\ \hline Residual chlorine & Chlorine residual sensor & 3 mg/L & WHO [97] \\ \hline Turbidity & Turbidity sensor & 0–5 NTU & WHO [97] \\ \hline Oxygen reduction potential & ORP sensor & \(\pm\)2000 mV & [100] \\ \hline Conductivity & Conductivity sensor & 500–1000 μS/cm & WHO [97] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the water quality parameters for drinking water system. In an effort to address some of the challenges in those research studies and make the system robust, [26; 102] incorporated a water quality hydraulic modelling into the system for contaminant prediction and detection. [PERSON] et al.