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6G White Paper on Edge Intelligence

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arxiv 2004.14850 v1 pith:NNADT55T submitted 2020-04-30 cs.DC cs.AIcs.NI

classification cs.DCcs.AIcs.NI
keywords edgeintelligentwhitecomputingdevelopmentintelligenceinternetalong
verification ladder T0 review T1 audit T2 compute T3 formal
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In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments and experiences, urban computing and autonomous traffic settings. We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers. In this white paper, we focus in the domains of edge computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, along with security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges and identify the key research questions for the development of the Intelligent Edge services. As a main outcome of this white paper, we envision a transition from Internet of Things to Intelligent Internet of Intelligent Things and provide a roadmap for development of 6G Intelligent Edge.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 126 citations worldwide. Full citation record

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

    cs.AI 2026-07 conditional novelty 4.0 of 10

    Clustered Edge Intelligence reframes edge AI as managing and clustering derived intelligence as independent entities rather than clustering the devices that produce it.

  2. Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions

    cs.NI 2025-08 conditional novelty 4.0 of 10

    A survey that organizes agentic AI for 6G edge networks into four pillars, compactness, efficiency, knowledge and reasoning, and migration, and illustrates them with prior case studies.

  3. SDVDiag: A Modular Platform for the Diagnosis of Connected Vehicle Functions

    cs.SE 2025-07 conditional novelty 4.0 of 10

    A modular platform for automated fault diagnosis in connected vehicles, evaluated in a 5G testbed, but with only qualitative evidence for its reliability.

  4. AI/ML for 5G and Beyond Cybersecurity

    cs.CR 2025-05 conditional novelty 1.0 of 10

    A literature review and position statement on AI/ML security challenges and defenses in 5G and beyond networks, based on sources through 2022.

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