Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing

In this letter, we study a mobile edge computing (MEC) architecture with the assistance of digital twin (DT) applied for industrial automation where multiple Internet-of-Things (IoT) devices intelligently offload computing tasks to multiple MEC servers to reduce end-to-end latency. To do so, first w...

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Vydané v:IEEE wireless communications letters Ročník 11; číslo 4; s. 806 - 810
Hlavní autori: Do-Duy, Tan, Van Huynh, Dang, Dobre, Octavia A., Canberk, Berk, Duong, Trung Q.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Computation offloading
Computational modeling
Constraint modelling
Digital twin
Digital twins
Edge computing
Internet of Things
IoT
Mobile computing
Mobile edge computing
Multi-access edge computing
Network latency
Optimization
Quality of service
Resource management
Servers
Task analysis
ISSN:2162-2337, 2162-2345
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Popis
Shrnutí:In this letter, we study a mobile edge computing (MEC) architecture with the assistance of digital twin (DT) applied for industrial automation where multiple Internet-of-Things (IoT) devices intelligently offload computing tasks to multiple MEC servers to reduce end-to-end latency. To do so, first we propose and formulate a practical end-to-end latency minimization problem in the DT-assisted MEC model subject to the constraints of quality-of-services and computation resource at the IoT devices and MEC servers in industrial IoT networks. Then, we solve the proposed latency minimization problem by iteratively optimizing the transmit power of IoT devices, user association, intelligent task offloading, and estimated CPU processing rate of the devices. Finally, simulation results are conducted to prove the effectiveness of the proposed method in terms of the latency performance compared with some conventional methods.
Bibliografia:ObjectType-Article-1
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2022.3146207