M. S. Elbamby et al., "Wireless Edge Computing With Latency and Reliability Guarantees," in Proceedings of the IEEE, vol. 107, no. 8, pp. 1717-1737, Aug. 2019. doi: 10.1109/JPROC.2019.2917084
Wireless edge computing with latency and reliability guarantees
|Author:||Elbamby, Mohammed S.1; Perfecto, Cristina2; Liu, Chen-Feng1;|
1Centre for Wireless Communications, University of Oulu, 90014 Oulu, Finland
2Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
3VTT Technical Research Centre of Finland, 90571 Oulu, Finland
4Department of Computer Science and Engineering, Kyung Hee University, Seoul 17104, South Korea
|Online Access:||PDF Full Text (PDF, 7.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202002195819
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-02-19
Edge computing is an emerging concept based on distributed computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth-generation (5G) wireless systems and beyond. While the current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This paper will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultrareliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so on. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.
Proceedings of the IEEE
|Pages:||1717 - 1737|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by CWC, Academy of Finland, through the CARMA Project, under Grant 294128, in part by the 6Genesis Flagship under Grant 318927, in part by the Kvantum Institute Strategic Project (SAFARI), in part by the Spanish MINECO through the Project 5RANVIR under Grant TEC2016-80090-C2-2-R, and in part by the (VTT) Academy of Finland thorough the MISSION Project under Grant 319759. The work of C. Perfecto was supported in part by the European Commission through the H2020 5G-PPP Project ESSENCE under Grant Agreement 761592.
|Academy of Finland Grant Number:||
294128 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
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