6G White Paper on Edge Intelligence

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Peltonen , E , Bennis , M , Capobianco , M , Debbah , M , Ding , A , Gil-Castiñeira , F , Jurmu , M , Karvonen , T , Kelanti , M , Kliks , A , Leppänen , T , Lovén , L , Mikkonen , T , Samarakoon , S , Rao , A , Seppänen , K , Sroka , P , Tarkoma , S & Yang , T 2020 , 6G White Paper on Edge Intelligence . 6G Research Visions , no. 8 , University of Oulu , Oulu . < https://www.6gchannel.com/portfolio-posts/6g-white-paper-edge-intelligence/ >

Title: 6G White Paper on Edge Intelligence
Author: Peltonen, Ella; Bennis, Mehdi; Capobianco, Michele; Debbah, Merouane; Ding, Aaron; Gil-Castiñeira, Felipe; Jurmu, Marko; Karvonen, Teemu; Kelanti, Markus; Kliks, Adrian; Leppänen, Teemu; Lovén, Lauri; Mikkonen, Tommi; Samarakoon, Sumudu; Rao, Ashwin; Seppänen, Kari; Sroka, Paweł; Tarkoma, Sasu; Yang, Tingting
Contributor organization: Department of Computer Science
Empirical Software Engineering research group
Helsinki Institute for Information Technology
Content-Centric Structures and Networking research group / Sasu Tarkoma
Publisher: University of Oulu
Date: 2020-06-30
Language: eng
Number of pages: 34
Belongs to series: 6G Research Visions
ISBN: 978-952-62-2677-4
ISSN: 2669-9621
URI: http://hdl.handle.net/10138/317723
Abstract: 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.
Subject: cs.DC
213 Electronic, automation and communications engineering, electronics
Usage restriction: openAccess
Self-archived version: acceptedVersion

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