# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 4 Digital Technologies in the Water System: Key Drivers of Water 4.0
### Cloud Computing  
Cloud computing is one of the new computing paradigms which permit the provisioning of a reliable and quality of service-guaranteed dynamic computing environment for end users [101]. Cloud-based computing technologies can make a significant contribution to Water 4.0 realization. Cloud computing has powerful storage, processing and service ability [102]. When combined with the IoT's capability of information collection, utilities can have access to scalable, on-demand services that are provided through web-based technologies. In Water 4.0, cloud computing is expected to share resources such as water piping network data to achieve coherence and economies of scale. As discussed in the previous sections, water supply systems generate a huge amount of big data from its components such as the information from water distribution networks, water treatment stations, and pumping stations. A huge volume of this data could be sent to a cloud computing center for processing, computation and storage, which eases monitoring operations of water supply systems. Most of the AI training and inferences are performed in the cloud and as a result of the cloud network's scalability and flexibility, many organizations have chosen to rely on cloud computing, storage, and networking architecture.  
Table 3 gives the summary of the features and potential application domains of the key drivers of Water 4.0 in the water sector. The CPS has been widely used for water quality monitoring where sensor technologies were utilized to acquire a huge amount of data to capture changes in water quality parameters along a pipe network. This is usually achieved through sensing, communication (through wireless system), and control (using actuator networks). Similar to the CPS, the IoT has been widely used for leak detection and monitoring, water quality detection, and pipe health monitoring, among others, although the CPS offers better control capabilities [16]. Currently, research studies focus on the use of AI in the water domain. It requires gathering necessary data related to the utility facility, analyzing the data and making optimal decisions using a decision support system. In this, both supervised and unsupervised machine learning algorithms are employed. Among others, anomaly detection and water demand forecasting are the most famous applications of AI in the water sector. Nevertheless, its utilization for pipe health monitoring as well as water quality detection in water treatment stations cannot be overlooked. Among these digital technologies, cloud computing is seldom used in the water sector. It encompasses data processing, storage, and decision support and has found applications in the management of water resources and water quality detection. Because the CPS and IoT require deployment of numerous sensing and/or actuating devices relevant to the specific application, the success rate of such systems depends on the robustness of the sensing and communication devices used. For instance, in a water distribution network application where the sensors are exposed to severe temperatures and harsh environments, the long-term durability of the sensing device is a challenge.  
Deploying smart sensors to gather water quality data from a water pipe network installed in a harsh underground environment is a big challenge. This is because flora aggregate [135] may be found around the sensors, which depletes the sensing power and the operational performance of the sensing device. This may affect the quality of the acquired data needed for other digital technologies (AI, BD analytics, and cloud computing). Therefore, the success rate of AI, BD analytics and cloud computing will depend on the quality of the data and complexity of the algorithms used for data analysis. In addition, complex water network analysis could be performed using hydraulic models to gather sufficient water quality or leak data needed for AI and BD applications. Research studies in this domain can be found in [136; 137].