*3.2. Property Layer*

The property of SWS can be understood as the ability to respond to threat, withstand attack, and adapt more readily to failure risks, like connectivity, real-time, security, resourcefulness, and robustness. These properties can be considered indicators of how smart the SWS is, and have to be quantified either qualitatively or quantitatively through metrics. The metrics applied in the SWS assessment, such as smartness, how efficient the SWS is towards real-time, and cyber wellness, how defensible the SWS is against cyberattack, would be discussed in the next section. In this work, we proposed that SWS based on components layer should have 4 properties: (1) Automation; (2) Resourcefulness; (3) Real-time; (4) Connectivity. We designed the interaction between each other within the property loop in Figure 7. For instance, automation is the foundation to achieve real-time and also real-time is facilitated by connectivity. Resourcefulness is ensured by the automation and connectivity among various IoT. One common sense is that these four properties might not be applied to each case of SWS, and also, some researchers may consider SWS with other extra features, but these four demonstrate what most SWS look like.

**Figure 7.** Property layer designing.

Automation is the fundamental property, which means that SWS can perform the physical and cyber components' operation automatically once the relevant parameters are set manually. This is a near-automatic process in the SWS since the application of ACT and ICT enables the SWS to execute one-way or two-way orders without too many operators' involvement. For the one-way control case (e.g., by setting the sampling interval in the Arduino sketch), the smart sensor can modify the data collecting frequency. As to the two-way control example, the real-time metering data fusion can help provide feedback for the control center, where the data analytic tools, in turn, give instructions to the opening status of smart meters, smart valves, or intelligent pumps. Further, automation can be implemented not only for components providing functionality but also for operation mechanisms. For example, when water problems happen in the operation process or the element itself, the smart components notify the system center and then take action to avoid a crash. Additionally, with automatic self-verification, the water utility can know when the sensor needs maintenance or re-installation [64].

Connectivity is also a fundamental property, which means the degree of interconnectedness or duplication [56]. In a cyber-physical system like SWS, connectivity can be implemented by deploying multiple sensors and software for monitoring the same physical processes [65]. SWS should be qualified to connect with different software and hardware to make the system collect data, analyze data, and share information publicly. For example, the SWS can be connected with the hydraulic model, GIS platform, billing system, and Database Model.

Real-time is the core property of SWS, which can also be called system efficiency. The real-time performance of SWS is characterized as online steps like online data monitoring, online data assimilation, online modeling, online plotting, and online results output, despite offline performance included in SWS. Real-time is the property that SWS obtains to achieve the required smart features [66]. However, current research mainly focuses on real-time modeling. The real-time modeling of SWS is structured as 6 steps: (1) Communicate SCADA datasets; (2) Updating the network model boundary conditions and operational statuses; (3) Pausing execution; (4) Generating the corresponding networks analysis; (5) Waiting for the new SCADA measurements to reload the network model; (6) Return the network simulation. Mentioned in the connectivity property, real-time modeling can be performed only by connecting with data sources systems like SCADA with modeling tools like EPANET-RTX [60]. This newly produced data from real-time hydraulic modeling can forecast results and calibrate the model by comparing measurements and predicted values.

Resourcefulness is the final property, which means the SWS not only owns massive data storage but also aims to timely exchange data for further analysis. This property of data exchange can also be interpreted as interoperability, which refers to the capability of units of an SWS to exchange and use information and services with one another and interfaced external units [67]. SWS provides massive information to an automation and security system, compared with the traditional instruments [58]. Typically, there are three types of source data open to the processor: spatial data, attribute data, and multimedia data, which determine the database model designing. However, these data can only be shared with the public and business after being analyzed by experts. Those processed data would be input into the hydraulic model to produce forecasting data, and those visualized data would be interpreted as valuable information such as early warning system, assessment of pipe leakage/breaks, or identification of cyberattacks or for decision support [68]. Moreover, the processed information or data would be transmitted back to the SCADA system and stored as instrument status and diagnostic information.
