Optimizing IoT architectures by using model driven approach and evolution strategy

The Internet of Things (IoT) is rapidly transforming the modern world by connecting billions of devices that generate a continuous flow of data. Its rapid expansion has broadened applications from home automation to nearly every industry. Selecting an appropriate IoT architecture is increasingly challenging due to the complexity of components, interactions, and diverse application requirements. Ensuring that an IoT architecture meets performance, reliability, and resource constraints before deployment is essential for avoiding inefficiencies and bottlenecks. Emulating IoT architectures during the design phase allows architects to test different IoT architectures and assess system performance under various conditions. This helps ensure optimal resource utilization and responsiveness. Several simulation approaches, particularly those based on Model-Driven Development (MDD), have been proposed to model and analyse IoT environments. These methodologies provide high-level abstractions of complex IoT architectures, enabling systematic experimentation and evaluation. This work introduces a novel methodology and tools for simulating and refining IoT architectures using a evolutionary approach. They allow architects to assess and improve performance based on configurable parameters such as latency and CPU usage. Through iterative IoT architecture modifications and testing, the proposed MDD-based approach optimizes system design, ensuring that the final architecture is fine-tuned to deliver the best possible performance for a given application. The proposal is validated by a case study related with a smart parking.