Real-time environmental data logging represents a fundamental component of Internet of Things (IoT) systems, yet reliance on platforms such as Arduino, ESP32, and Raspberry Pi introduces financial and technical challenges in resource-constrained educational settings. This study presents a Python-based simulation framework that eliminates the need for physical hardware by emulating sensor data generation, UART-style communication, structured CSV logging, and real-time visualization. The framework produces temperature and humidity readings using controlled randomness (random values within defined limits) and dynamically plots trends with matplotlib, thereby replicating realistic sensor variability. It further models serial communication behavior to strengthen learners’ understanding of embedded data pipelines. Evaluation results demonstrate that the system preserves data integrity, accurately reproduces streaming behavior, and maintains stability during extended runs. Student feedback from a classroom survey suggested improvements in learners’ comprehension of data acquisition and communication concepts, with ninety-two percent reporting successful understanding of simulation workflows. The framework is lightweight, modular, and open-source, enabling deployment on low-spec computers or cloud platforms such as Google Colab. By lowering the entry barrier to IoT education, the proposed solution supports experiential learning, accelerates prototyping, and fosters reproducible experimentation. This contribution enhances the body of virtual IoT educational tools and provides a scalable foundation for future extensions, including multi-sensor support, networked simulations, and cloud integration.
Huthaifa Ayad Sabri (Mon,) studied this question.