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

Creative Commons License

Tan Do-Duy T. D., Huynh D. V., Dobre O. A., Canberk B., Duong T. Q.

IEEE WIRELESS COMMUNICATIONS LETTERS, cilt.11, sa.4, ss.806-810, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/lwc.2022.3146207
  • Dergi Adı: IEEE WIRELESS COMMUNICATIONS LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Sayfa Sayıları: ss.806-810
  • Anahtar Kelimeler: Servers, Task analysis, Computational modeling, Digital twin, Resource management, Quality of service, Multi-access edge computing, Mobile edge computing, digital twin, IoT
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.