将智能作为可共享的独立实体，实现边缘智能的动态管理与协同。
Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI
- 提出面向智能的分布式架构，使智能可被发现、观察、共享和管理。
- 构建三层框架，涵盖智能库存、语义表示、聚类机制等核心技术。
- 适合研究边缘智能协同与跨设备智能复用的学者与开发者。
我们正从信息时代迈向智能时代。未来十年内，数据将不再是最宝贵的资源，而是源于数据的智能以及网络边缘所掌握的信息。当前边缘智能研究主要集中在两个方向：利用AI进行边缘资源管理，以及在边缘设备上部署轻量级AI模型。然而，现有边缘计算研究缺乏以智能为中心的框架，未能将生成的智能视为一个可独立描述、发现、观测、共享、复用并动态聚类的首等实体。为弥补这一空白，本文提出集群边缘智能（Clustered Edge Intelligence, CEI），一种以智能为核心的前瞻性方法。其目标是使智能成为可在分布式边缘-云环境中独立表示、发现、观察、交换和管理的可共享、可复用的首等实体。本文提出了三层次的CEI架构，并探讨了若干使能技术与研究维度，包括智能库存、语义知识表示、通信、可发现性、可观测性、生命周期自动化、聚类机制、市场机制、互操作性及标准化。
原文摘要 · Abstract (English)
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.
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