迈向智能建筑的云-雾-边缘系统
Towards a Cloud Fog Edge System for Smart Building
- University Sorbonne Paris Nord（巴黎北索邦大学）
- CNRS（法国国家科学研究中心）
- INRIA（法国国家信息与自动化研究所）
- National institute of Informatics（国立信息学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出将建筑物作为数据中心的去中心化系统，利用边缘计算和轻量级Kubernetes-like框架，在低功耗微控制器上部署AI算法，实现可持续、保护隐私的智能环境，并评估了两种新在线算法。
AI中文摘要：
在本文中，我们阐述了我们的愿景和最新进展，旨在创建一个能够从建筑物内实时数据中学习的去中心化系统，以支持可持续且保护隐私的智能环境。我们的方法提倡将建筑物本身作为数据中心的概念，符合边缘计算的原则，以保障机密性并减少对外部云基础设施的依赖。这在人道主义背景下尤其有价值，因为数据主权、能源效率和基础设施限制至关重要。我们详细介绍了一个轻量级的、类似Kubernetes的编排框架，用于在此类环境中部署AI服务，并展示了我们在Arduino生态系统等低功耗、成本效益高的微控制器上实现AI算法的进展。通过直接在传感器或微控制器上进行原位学习，我们的工作旨在将智能服务带到资源受限的环境中，促进脆弱或服务不足社区的自主性、韧性和可持续发展。本文的贡献首先涉及我们的项目“用于嵌入式系统的在线机器学习算法”以及两个新在线算法的评估。其次，我们设想了一种基于KOptim和FIWARE组件的云-雾-边缘架构，并提出了一种耦合它们的方法。最后，还展示了在线算法的实验结果，并展示了真实世界的轨迹。
英文摘要：
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.
