Bridging Industry-Standard Boards with Python via API for Enhanced Computational Efficiency

Abstract

This paper presents a novel approach to integrating industry-standard hardware boards such as Arduino with Python using a dedicated API. The system enables boards to send Python code execution requests to a centralized server, where computation takes place, and results are returned to the boards. This setup enhances efficiency, allowing resource-limited devices to perform complex tasks without requiring extensive computational capabilities. The platform supports real-time monitoring, dynamic scalability, and efficient task distribution, fostering applications in IoT, machine learning, and deep learning. Experimental results highlight the significant improvement in execution time when leveraging server-based computation compared to local execution on embedded devices. This work aims to provide a robust framework that empowers users in diverse application areas by overcoming the limitations of local hardware and enabling seamless software integration.