# Enhanced FIWARE-Based Architecture for Cyberphysical Systems With Tiny Machine Learning and Machine Learning Operations: A Case Study on Urban Mobility Systems

arXiv: https://arxiv.org/abs/2411.13583 · HTML: https://mlsanity.com/paper/2411.13583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [AI based Service Management for 6G Green Communications](https://mlsanity.com/paper/2101.01588/) (2021)
- [Applying digital twins for the management of information in turnaround event operations in commercial airports](https://mlsanity.com/paper/2408.14291/) (2024)
- [Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things](https://mlsanity.com/paper/2408.14528/) (2024)

## Abstract (arXiv)

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.