5G MEC Gateway System Design and Application in Industrial Communication

Jia, Ziwei; Li, Dongdong; Zhang, Weimin; Pang, Lingli

doi:10.1109/wsai49636.2020.9143280

Cited by 8 publications

(1 citation statement)

References 15 publications

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“…The combination of these two promising techniques is frequently used in various fields. For industrial application, Jia et al [40] propose a 5G MEC gateway system capable of supporting URLLC to enable communication in factories. For autonomous vehicles application, vehicle's latency and energy cost functions have been established to investigate URLLC resource scheduling for edge computing [41].…”

Section: Introduction 1| Literature Reviewmentioning

confidence: 99%

Unmanned aerial vehicle‐aided edge networks with ultra‐reliable low‐latency communications: A digital twin approach

Huynh

Do‐Duy

et al. 2022

IET Signal Processing

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A digital twin (DT) framework for Internet‐of‐thing (IoT) networks is proposed where unmanned aerial vehicles (UAVs) acting as flying mobile edge computing (MEC) servers support the task offloading on the fly. The considered DT model is very well suitable for industrial automation with the strict constraints of mission‐critical services' ultra‐reliable low‐latency communication (URLLC) links. To support low‐latency IoT devices, we formulate the end‐to‐end (e2e) latency minimisation problem of digital twin‐aided offloading UAV‐URLLC. Specifically, the minimised latency is obtained by jointly optimising both communication and computation parameters, namely power, offloading factors, and the processing rate of IoT devices and MEC‐UAV servers. Due to the highly non‐convex optimisation problem, we first consider the K‐means clustering algorithm to optimally deploy the on‐demand UAVs. Then, an alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenge. We demonstrate the effectiveness of the proposed DT framework through representative numerical results.

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Section: Introduction 1| Literature Reviewmentioning

confidence: 99%