arXiv：cs.LG（机器学习，全量分类）· Christophe C\'erin, Mamadou Sow, Fr\'ed\'eric Andr\`es·· 1 天前AI 评分31
面向智能建筑的云-雾-边缘系统：在 Arduino 等微控制器上实现 AI 在线学习
Towards a Cloud Fog Edge System for Smart Building
AI 导读
一项研究提出以建筑自身作为数据中心的云-雾-边缘架构，基于 KOptim 与 FIWARE 组件构建类 Kubernetes 的轻量编排框架，用于在建筑内就地部署 AI 服务。工作还评估了两种新的在线学习算法，并在 Arduino 生态等低功耗微控制器上实现 AI 算法，使传感器可就地学习，兼顾数据主权、隐私与能效。
正文
Abstract:In this article, we present our vision and recent advancements toward creating a decentralized system capable of learning from real-time data within buildings to support sustainable and privacy-preserving smart environments. Our approach promotes the concept of the building itself as the data center, aligning with the principles of edge computing to safeguard confidentiality and reduce reliance on external cloud infrastructure. This is particularly valuable in humanitarian contexts, where data sovereignty, energy efficiency, and infrastructure constraints are critical. We detail a lightweight, "Kubernetes-like" orchestration framework for deploying AI services within such environments and demonstrate our progress in implementing AI algorithms on low-power, cost-effective microcontrollers such as those in the Arduino ecosystem. By enabling in-situ learning directly on sensors or microcontrollers, our work aims to bring intelligent services to resource-limited settings, fostering autonomy, resilience, and sustainable development in vulnerable or underserved communities. The contributions in this article are related, firstly, to our project "Online Machine Learning Algorithms for Embedded Systems" and the evaluation of two new online algorithms. Secondly, we envision a cloud-fog-edge architecture based on the KOptim and FIWARE components, and we propose a methodology for coupling them. Experimental results of the online algorithms are also presented, showcasing real-world traces.
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01647 [cs.DC] |
| (or arXiv:2610.01647v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01647 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Christophe Cerin [view email]
[v1]
Thu, 1 Oct 2026 13:13:21 UTC (905 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org