Optimizing Vehicle IoT Systems: SUMO-Digital Twin Performance Analysis

The Internet of Things (IoT) has catalyzed numerous transformations within the automotive sector by enabling seamless connectivity between vehicles and centralized data platforms. With this integration, diverse data, including vehicle performance metrics and real-time traffic patterns, can be collected and analyzed. However, there are some challenges associated with performance testing on these platforms, as they present high costs, potential security risks, and limitations in the ability to cover a wide range of scenarios. In this way, the study uses the concept of digital twins, that is, virtual representations of objects, systems, or processes in the real world to solve the challenges. Therefore, it used the Simulation of Urban MObility (SUMO) framework to evaluate the efficiency and reliability of a vehicle data architecture. Through a detailed experiment comprising three experimental rounds, totaling 91,296 vehicles running simultaneously on four machines, the study investigates system resource utilization, throughput, latency, and data integrity. Preliminary results highlight the importance of robust architectures for successful vehicle simulations, providing insights into the advancement of automotive technologies and urban mobility systems.