# Digital Twin Conceptual Model within the Context of Internet of Things
## 2 Literature Review  
The induction of digital twins began with the aviation industry before escalating in the manufacturing domain. A number of manufacturing-, power grid-, and consumer electronics-focused corporations such as Tesla, Siemens, and General Electric are adopting the digital twin concept for their consumer products and their products' life cycle management during manufacturing and operation [2]. In [3], the authors developed a methodology to capture real-time sensor data from cyber physical systems technologies communicating current status of factory objects to the object's real-time digital twin model. Based on this model, the users can monitor the current status of a factory's objects. In [4; 5; 6; 7], the authors have provided a comprehensive review and survey on different characteristics and applications of a digital twin and its increasing demand in Industry 4.0 manufacturing technologies. The potential need for a holistic framework and architecture that links the physical object with the virtual (digital) instance of the object is presented. In [8], the authors proposed the initial stages to mimic a digital twin model for smart farming, which is only feasible when multiple systems and processes involved in smart farming are orchestrated together. Similarly, a use-case scenario for implementing digital twin in laser welding manufacturing technology was presented in [9]. Limited work has been done focusing on a complete modular architecture of a digital twin in the context of Industry 4.0 that leverages the data from the physical layer followed by lightweight protocols for communication, edge processing, modeling, and 3D visualization [10]. With the rapid evolution of the Internet of Things, the concept of \"smart objects\" is also emerging as a valuable asset to IoT applications as it can leverage the large amount of data and services provided by the mesh network of IoT devices. A'smart object' is empowered with the ability to sense the physical world events and human activities and interact with the users based on the user input to make control decisions [11]. The surrounding environment is integrated with smart objects, sensors, and a network of intelligent systems. These smart objects are capable of making optimal decisions and render customized IoT services in anticipation of the requirements of the nearby visitors or inhabitants [12]. In addition to a digital twin communicating with a physical object in real time, these digital twins can also be designed similarly to a smart object in order to be more adaptive and aware in close interaction with the surrounding environment and human activities with the help of ambient intelligence. It is worth mentioning that most of the existing work and models do not efficiently incorporate the application, features, and design framework for an intelligent digital twin model, which is important for realization of an autonomous cyber physical system. With the help of current IoT technologies such as 5G cellular communication, data analytics, artificial intelligence, machine learning, and cloud computing, a digital twin can be revolutionized to understand how the products and machinery are performing in real time and to make fast and accurate predictions for the future as well as detect potential failures. The proposed work is different from the existing work because it proposes an end-to-end conceptual model for a digital twin. On the other hand, most of the existing work on digital twins has focused only on the simulation aspect of the digital twin for emulating the mechanical process and the physical component of a machinery in the virtual layer of the architecture or the cloud. The proposed conceptual model incorporates different communication technologies and machine learning algorithms as well as security for simulating a digital twin. These layers are essential for seamless integration of data in real time from the physical layer up to the application layer encapsulated with a security framework for predictive maintenance.