## 1 Introduction  
The technological trend of the Internet of Things (IoT) has led to a massive increase in the number of smart devices that are connected to cyberspace [1; 2; 3]. In order for these smart devices to make a meaningful impact, it is important that they have the capability of capturing information related to their intended use; some common information that would be captured from typical smart devices may include traffic flows, temperature, humidity, and energy consumption. Depending on what a smart device is designed for, it may capture the key characteristics of things so that the efficiency and intelligence of cyber-physical systems (CPS), including energy, transportation, manufacturing, agriculture, healthcare, and other critical infrastructure systems, can be realized [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. Due to the large number of data points that are introduced in these CPS, it is important to adopt advanced networking, data analysis (e.g., deep learning), and cloud/edge computingtechnologies in smart systems [15; 16; 17; 18; 19; 20; 21]. By doing this, the relevant information can be collected, transmitted, analyzed, and shared in a timely manner so that usable information can provide the intelligence to enhance the monitoring and control abilities of physical systems [22]. As an energy-based CPS, the smart grid can be identified as a complex system, where numerous uncertainties arise in both cyber and physical components [23; 24; 25; 26]. The uncertainties can be caused by an outage in one part of the grid; in other words, the smart grid is susceptible and is easily affected by random accidents such as power load imbalance, outages, and even disruption from surrounding environments. We can understate both the importance and frangibility of the smart grid, which is facing unprecedented challenges. Depending on the development of big data and IoT, adapting big data analysis tools (ML/DL, data mining, statistics, etc.) can predict some potential risks, in order to mitigate accidents in the smart grid [27]. Furthermore, leveraging big data can establish a virtual smart grid environment to simulate real accidents, to investigate and develop mitigation plans. Another example is related to wireless sensors networks, which can be used in different environmental monitoring systems. For example, smart farming is an emerging application that connects IoT devices so that agricultural production, soil quality, and temperature humidity can be managed [12; 28; 29]. Due to the harsh weather and environments, there are numerous uncertainties that could affect the reliability of connectivity and physical devices in smart farming. The uncertainties of the smart farming system can cause failures and lead to the quantity and quality reduction of agricultural products. Thus, it is necessary to design and implement the smart farming system as a typical CPS in the agriculture domain so that important data can be collected and efficient operations of smart farms can be supported. To this end, leveraging the collected data to create a cyber replica of the farming system for testing and evaluation is a feasible approach to deal with a variety of uncertainties in the system. Likewise, in smart manufacturing systems, leveraging big data analysis can help system administrators and engineers discover vulnerabilities in the system. In addition, based on big data analysis, system administrators and engineers can update systems to optimize supply chain and production performance [7; 30; 31]. Nonetheless, directly manipulating or updating the real system has some critical risks. Thus, creating a cyber replica of real physical systems to emulate real cases in those physical systems is an efficient strategy, which is named Digital Twin (DT). Generally speaking, DT is a digital clone of a real physical system. The vision of DT refers to data-driven software and hardware set that describes a real physical system with all functions and use cases. It also includes all status and information in life-cycle phases [32].