# Digital Twin Conceptual Model within the Context of Internet of Things
## 8 Use Case  
In this paper, we will take a case study to demonstrate an application of digital twin model in smart transportation. A high-level framework of sensing, communication, and control between the physical and virtual space of a vehicle is represented in Figure 2. As shown in Figure 2, we have referred to different physical sensors such as camera, radar, gyroscopic sensor, GPS, tire pressure sensor, speed sensor, and sound sensor for the automobiles to capture real-time parametric values from the vehicles and communicate them to the virtual space layer in real time. The virtual space layer will communicate efficient control decisions to actuate the ABS, vehicle's speed, steering control, and motion planning. To further elaborate on the proposed end-to-end conceptual model of a digital twin in the context of cars, assume that we have four different types of cars of two models, namely, Toyota Avalon and Camry. The system collects a set of physical parameters from the cars while they are on the move: ABS, transmission, mileage, and tire status from the on-board car sensors. The vehicle's 2D/3D models, bill of materials (BOM), and historical data are stored in the virtual twin in the virtual space layer, as shown in Figure 3.  
In the data analytics and visualization layer of the end-to-end conceptual, model as presented in Figure 3, complex big data analytics and ML processing algorithms are conducted to perform automated decision making based on real-time, measured sensor data from the cars and historical data. The data are stored on an off-the-shelf, high-end cluster designed using commodity hardware such as Hadoop Distributed File Storage system (HDFS) consisting of multiple data nodes distributed geographically. Figure 2: A high-level framework for sensing, communication, and control between physical and virtual space for a vehicle. One such example is of a MapReduce processing algorithm, as shown in Figure 4. This algorithm comprises four distinct stages, i.e., splitting, mapping, shuffling, and reducing. The functions associated with each stage are discussed as follows. 1. Splitting: This involves the logical division of data across cluster nodes in accordance with the parameters/sensor readings as measured by different automobiles. For example, data node 1 of the cluster stores the ABS status for all the cars, the data node 2 stores the transmission parameter for all, the data node 3 is responsible for storing the tire status, and data node 4 stores the mileage covered with respect to each car. 2. Mapping: In this phase, the data split across each cluster are sent to a mapping function to return an aggregated value for each type of car model with respect to a specific parameter. For example, the mapping function in data node 1 returns an aggregated output for ABS for each car's model (Avalon and Camry). Similarly, mapping function from other data nodes returns the sum of transmission, tire status, and mileage values for each model of the cars. 3. Shuffling: In this phase, multiple outputs from the mapping stage are consolidated and relevant records are clubbed together, according to their respective car model. For example, different parameters of all Camrys and Avalons will be clubbed together on different data nodes of the cluster. 4. Reduce: In this phase, the outputs from the shuffling stage are combined together into a single output depending upon the status query from the stakeholder. In other words, this stage  
Figure 3: Proposed end-to-end digital twin conceptual model for vehicles. summarizes the complete dataset of the cars and presents it to the stakeholder based on their respective requirement. The analytics and processing data from the DAVL layer will be leveraged with the 2D/3D simulation modeling to plan and predict breakdowns and to highlight failure patterns [23]. After the processing and analytics phase, these results will be communicated to different levels of stakeholders for data monetization and visualization in terms of dashboard graphs, charts, tables, and reports.