The rise of AI and the Internet of Things is accelerating the digital transformation of society. Mobility computing presents specific barriers due to its real-time requirements, decentralization, and connectivity through wireless networks. New research on edge computing and tiny machine learning (tinyML) explores the execution of AI models on low-performance devices to address these issues. However, there are not many studies proposing agnostic architectures that manage the entire lifecycle of intelligent cyberphysical systems. This article extends a previous architecture based on FIWARE software components to implement the machine learning operations flow, enabling the management of the entire tinyML lifecycle in cyberphysical systems. We also provide a use case to showcase how to implement the FIWARE architecture through a complete example of a smart traffic system. We conclude that the FIWARE ecosystem constitutes a real reference option for developing tinyML and edge computing in cyberphysical systems.

翻译：人工智能与物联网的兴起正在加速社会的数字化转型。移动计算因其实时性要求、去中心化特性及无线网络连接方式而面临特定挑战。边缘计算与微型机器学习（tinyML）的新研究探索在低性能设备上执行人工智能模型以应对这些问题。然而，目前鲜有研究提出能够管理智能网络物理系统全生命周期的通用架构。本文在基于FIWARE软件组件的既有架构基础上进行扩展，实现了机器学习运维流程，从而支持网络物理系统中微型机器学习全生命周期的管理。我们还通过智慧交通系统的完整案例，展示了如何实现该FIWARE架构。研究结论表明，FIWARE生态系统为在网络物理系统中开发微型机器学习与边缘计算提供了切实可行的参考方案。