Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI

2026-07-23

AI Summary

This work addresses the absence of a systematic framework in existing edge intelligence research that treats intelligence as a first-class entity, thereby hindering its independent management, sharing, and reuse. To bridge this gap, the paper proposes a Clustered Edge Intelligence (CEI) architecture that, for the first time, conceptualizes intelligence as a first-class citizen across the edge-to-cloud continuum—capable of being described, discovered, observed, exchanged, and dynamically clustered. The CEI framework integrates three architectural layers encompassing intelligent asset inventory, semantic knowledge representation, discoverability, observability, automated lifecycle management, clustering mechanisms, and interoperability standards. This holistic approach establishes a comprehensive CEI architecture and delineates key enabling dimensions, laying a theoretical foundation and offering a technical roadmap for future edge intelligence systems.

Abstract

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.

Problem

• Edge Intelligence
• Intelligence-centric Framework
• First-class Entity
• Heterogeneous Edge Devices
• Intelligence Clustering

Innovation

• Clustered Edge Intelligence
• intelligence-centric framework
• first-class intelligence entity
• semantic knowledge representation
• edge-cloud continuum