集群化边缘智能：超越边缘计算与人工智能的简单融合
Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI
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中文总结 AI 辅助
研究从信息时代向智能时代迈进中边缘智能的问题，提出集群化边缘智能方法，构建三层CEI架构，研究相关使能技术和维度，让智能成为可共享重用的一流实体，填补现有边缘计算研究空白。
中文摘要 AI 辅助
我们正从信息时代迈向智能时代，未来数据不再是关键，从数据中衍生的智能才是。现有边缘智能研究主要集中在利用人工智能进行边缘资源管理和在边缘设备上部署轻量级人工智能模型。但现有边缘计算研究缺乏以智能为中心的框架。为填补这些空白，我们引入了集群化边缘智能这一以智能为中心的方法。其目标是使智能成为可共享、可重用的一流实体，能在分布式边缘 - 云连续体中独立表示、发现、观察、交换和管理。我们提出了三层CEI架构，并研究了使能技术和研究维度。
英文摘要
We are moving from an information age to the age of intelligence. A decade, or possibly less than that, data will not be the gold anymore rather the derived intelligence out of the data and the information we posses from the edge of the network. Existing Edge Intelligence research focuses mainly on two directions: using AI for edge resource management and deploying lightweight AI models on edge devices. However, existing edge computing research lacks an intelligence-centric framework in which derived intelligence is treated as a first-class, independently manageable entity that can be described, discovered, observed, shared, reused, and dynamically clustered across heterogeneous edge devices and applications. To address these research gaps, we introduced Clustered Edge Intelligence, a visionary intelligence-centric approach. The aim of CEI is to make intelligence a shareable and reusable first-class entity that can be independently represented, discovered, observed, exchanged, and managed across the distributed edge-cloud continuum. We present a three layer CEI architecture and examine enabling technologies and research dimensions, including intelligence inventories, semantic knowledge representation, communication, discoverability, observability, lifecycle automation, clustering mechanisms, marketplaces, interoperability, and standardization.
发表机构
- IISER Berhampur（贝汉布尔印度科学教育与研究学院）
- TU Wien（维也纳工业大学）
- University of Helsinki（赫尔辛基大学）
- University of Prishtina（普里什蒂纳大学）
- University of Oulu（奥卢大学）
- University of Hyderabad（海得拉巴大学）
- Stockholm University（斯德哥尔摩大学）
机构由 AI 辅助整理，请以论文原文为准。
