# 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
[26] proposed an architecture of a typical cyber-physical water system utilized for water pollutant detection. The proposed system consists of sensing devices (with water quality sensors) arranged in a sensor network, a hydraulic model, and energy-harvesting platform, as shown in Figure 7[26]. The water quality sensors provide real-time measurement of the water pollutant from the system. Also, a water quality hydraulic model is blended with the sensor real-time measurements to predict water pollutant occurrence in the water environment. The predicted pollutant and the real-time measurement from the sensors are fed to the decision-making block. This block allows the optimal decision for immediate action to be taken on water contamination before it spreads to the entire system. In most cases, necessary actions include the closing of valves at the particular node where the pipe conveying the contaminated water is attached. The valves installed at the entrance of a DMA of the water distribution network could also be controlled to stop the spread of contaminant. This is an advantage of partitioning a water system in DMAs for the monitoring and reduction of potential impact of contaminants [102; 103]. The system is also equipped with an energy harvesting methodology to improve the power consumptions of the sensors. Similarly, [PERSON] et al. [104] proposed a CPS framework for in-pipe water monitoring. The proposed system, regarded as PipeSense, is made up of six tiers interconnected as indicated in Figure 8[104]. In Figure 8, the sensing tier comprises of a flow, pressure, pH, and water quality sensor to gather data for water flow, pipe water pressure, pH, and contaminant level. The measured sensor data are transferred to the processing tier for data analysis and processing. In this tier (the processing tier),  
Figure 8: A CPS architecture for in-pipe water monitoring. Figure 7: A typical CPS for water quality monitoring. extensive data extraction is performed in real-time, which is then fed to the modelling tier. The data from the processing tier could also be stored for future reference. In the modelling tier, a water quality hydraulic model is used to estimate the water contaminant level. The output of this tier is sent to the data fusion tier, where the information from the hydraulic model and the processed data are fused. The human tier makes the optimal decision based on the information from the previous tiers. The decision makers at this tier could be sent to an actuator to control the spread of contaminant within the system. A section of the network where the effect of the contaminant is detected could be isolated with the help of the actuators. In most of the studies presented for water quality monitoring, there is higher uncertainty in using the developed system to give information concerning the source of the contaminant. More importantly, the associated cost of implementation is a major concern as the system deploys a huge number of sensors for real-time measurements.