Computing Offloading for RIS-Aided Internet of Everything: A Cybertwin Version

Xiaohui Gu, Guoan Zhang, Wei Duan, Shuping Dang, Miaowen Wen, Pin-Han Ho

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

Cybertwin technology introduces a novel paradigm employing digital twins to model complex physical systems within a cyber environment, thus enhancing communication, collaboration, and decision-making capabilities. By harnessing advanced technologies, such as reconfigurable intelligent surfaces (RISs) and multi-access edge computing (MEC), seamless interaction between physical and virtual entities is facilitated. In this paper, we propose a cybertwin-driven edge computing framework that leverages RIS technology, complemented by an efficient computing offloading strategy to support largescale Internet of Everything (IoE) applications. Specifically, the proposed strategy focuses on a multi-cell system where numerous randomly distributed end users have the option to offload delay-sensitive and computing-intensive tasks to edge computing nodes. The offloading channels are enhanced by RISs through passive beamforming, while cybertwin technology directs resource cooperation among multi-cells and allocates computing and communication resources. Our main objective is to optimize the system’s utility with respect to task completion latency and energy consumption reduction. To achieve this goal, we conduct the joint optimization of task offloading and resource allocation. Furthermore, we develop a joint task offloading and resource allocation (JTORA) algorithm to derive optimal solutions for passive beamforming design, computing offloading decisions, communication resource scheduling, and computing capacity allocation. The simulation results demonstrate the superiority of the proposed algorithm over benchmark schemes in terms of edge computing efficiency. Furthermore, the system utility can be further enhanced by increasing the number of embedded RIS elements.
Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
Volume11
Issue number11
Early online date27 Feb 2024
DOIs
Publication statusE-pub ahead of print - 27 Feb 2024

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Publisher Copyright:
IEEE

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