Edge Computing in IoT: A 6G Perspective

TL;DR

This paper discusses the role of edge computing in enabling future 6G networks, focusing on deployment considerations, services, and open research challenges to meet increasing demands.

Contribution

It provides a comprehensive overview of edge deployment factors, state-of-the-art services, experimental evaluations, and identifies open research problems for 6G networks.

Findings

Edge deployment factors significantly impact service performance.

Experimental results demonstrate effectiveness of edge-based video transcoding.

Deep learning inference at the edge shows promising latency improvements.

Abstract

Edge computing is one of the key driving forces to enable Beyond 5G (B5G) and 6G networks. Due to the unprecedented increase in traffic volumes and computation demands of future networks, multi-access (or mobile) edge computing (MEC) is considered as a promising solution to provide cloud-computing capabilities within the radio access network (RAN) closer to the end users. There has been a significant amount of research on MEC and its potential applications; however, very little has been said about the key factors of MEC deployment to meet the diverse demands of future applications. In this article, we present key considerations for edge deployments in B5G/6G networks including edge architecture, server location and capacity, user density, security etc. We further provide state-of-the-art edge-centric services in future B5G/6G networks. The paper also present experimental evaluation of edge-based services deployment including video transcoding and deep learning inference. Lastly, we present some interesting insights and open research problems in edge computing for 6G networks.