用FIWARE架构实现城市交通中的微型机器学习全生命周期管理。
Enhanced FIWARE-Based Architecture for Cyberphysical Systems With Tiny Machine Learning and Machine Learning Operations: A Case Study on Urban Mobility Systems
- 基于FIWARE构建支持tinyML的边缘计算架构，实现全流程管理。
- 案例验证了该架构在智能交通系统中可有效部署与运行。
- 适合研究边缘AI与物联网系统的开发者参考使用。
人工智能与物联网的发展正在加速社会的数字化转型。由于实时性要求高、需去中心化以及通过无线网络连接，移动计算面临特殊挑战。针对这些问题，边缘计算与微型机器学习（tinyML）研究探索在低性能设备上执行AI模型。然而，目前缺乏能够管理智能信息物理系统全生命周期的通用架构。本文扩展了基于FIWARE软件组件的先前架构，实现了机器学习运维流程，支持在信息物理系统中对tinyML进行全生命周期管理。我们还提供了一个实际案例，展示如何通过完整的智能交通系统示例来实现FIWARE架构。结果表明，FIWARE生态系统为开发tinyML和边缘计算在信息物理系统中的应用提供了切实可行的参考方案。
原文摘要 · Abstract (English)
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.
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