**4. Metrics for SWS**

The technical structure of SWS has a pyramid structure with core information on the top to ensure system efficiency and security [87]. Figure 11 illustrates the features of such a technical structure. In this general structure of SWS, the configuration of components and connections can be interpreted as a network of cyber information (e.g., leak detection, discharge control, and noise recognition), data compiling (e.g., real-time modeling, real-time controlling, and real-time sampling), and physical instruments (e.g., sensors and loggers) domain. In Figure 11, nodes represent system components while the links stand for the functional relationship between nodes. For instance, the bottom nodes are connected with the intermedia nodes, which optionally means that the data from the sensor is transmitted to SCADA via links. To better assess the SWS's efficiency and security within these domains, it is necessary to develop the metrics [45].

**Figure 11.** Illustration of a smart water system technical structure.

Before moving to the metrics discussion, the relationship between property and metrics should be clarified. While metrics are refined from properties, and both metrics and properties might be connected by functions, the application of SWS ultimately aims to assess the performance of SWS. Therefore, properties can be seen as the inherent components of SWS whilst metrics are the manual

product. Additionally, properties might determine the assessment indexes on a given SWS, while metrics are those elements to achieve the terminal performance. For example, real-time modeling is a crucial property of SWS, which makes measuring the efficiency of SWS one indicator for smartness.

Furthermore, the performance of data processing in the context of resourcefulness is related to informational security. However, the effects of property layers on metrics are not certain without specific analysis of a given system. In this section, the paper proposes two new conceptual metrics (Smartness and Cyber wellness) for assessing two essential properties of SWS, efficiency and security, and discusses how to define these two metrics and how they can be objectively built to deal with threats of SWS.

A brief investigation of 27 reviewed academic studies was conducted to analyze the SWS metrics shown in Figure 12, showing the number of studies (report and paper) for smartness scope and cyber wellness scope. Smartness and cyber wellness are seldom discussed directly in previous articles and reports. Most of them mention relationships with metrics or present the features of these two metrics. Thus, we consider that these papers and reports listed should be included in the scope of the metric. In the cyber wellness scope, cyber wellness only comes from the electrical and telecommunication fields [30], which makes it necessary to translate the cyber wellness into water systems. Although smartness has been described in the previous environmental studies, the vision is a little broader as water systems are only a small part of the environment [87]. More efforts would be required to narrow down the scope of smartness if it is applied in the water system sector.

**Figure 12.** The number of studies for different metrics scope.
