Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids

Zhou, Zhenyu; Jia, Zehan; Liao, Haijun; Lu, Wenbing; Mumtaz, Shahid; Guizani, Mohsen; Tariq, Muhammad

doi:10.1109/tii.2021.3137349

Cited by 97 publications

(33 citation statements)

References 27 publications

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“…• DT Modeling/Updating [32], [123], which mainly aims to improve DT modeling/update performance, such as the model accuracy, latency, and cost. • Edge Association [61], [96], [124], which includes the association between end devices/DTs and edge servers.…”

Section: A Typical Services Of Ditenmentioning

confidence: 99%

“…• Task Offloading [61], [96], [124], which aims to decide whether to offload a task and the proportion, thereby improving the task processing efficiency. • Resource Allocation [96], [123], [124], which includes the allocation of resources in terms of communication, computation, caching, and so on. • Security Mechanism [32], [61], [123], [125], [126], which aims to guarantee the security while satisfying the performance requirements.…”

Section: A Typical Services Of Ditenmentioning

confidence: 99%

“…• Resource Allocation [96], [123], [124], which includes the allocation of resources in terms of communication, computation, caching, and so on. • Security Mechanism [32], [61], [123], [125], [126], which aims to guarantee the security while satisfying the performance requirements.…”

Section: A Typical Services Of Ditenmentioning

confidence: 99%

“…In [123], the authors focus on DT construction and DTassisted resource allocation. To balance the delay and accuracy performance, the iteration latency and DT's loss function are jointly optimized.…”

Section: A Typical Services Of Ditenmentioning

confidence: 99%

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Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Tang

Chen

Rodrigues

et al. 2022

IEEE Open J. Commun. Soc.

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The next generation (6G) wireless systems aim to cater to the Internet of Everything (IoE) and revolutionize customer services and applications to a fully intelligent and autonomous system. To achieve this, the digital twin edge network (DITEN) is proposed to combine mobile/multi-access edge computing (MEC) and digital twin (DT), thereby improving the network performance such as throughput and security, and reducing the cost of communication, computation, and caching. In DITENs, the network status can be continuously monitored, and based on the obtained network states, the networking schemes, such as routing and resource management, can be studied in the established DITENs from a centralized perspective. In this survey, we present a comprehensive overview of DITEN for 6G. First, we present the fundamental aspects of DITEN, including concept, framework, and potential. Second, a comprehensive design of DITEN is devised, including the DT modeling/updating, DT deployment, key issues, and enabling technologies. Then, the typical applications of DITEN towards 6G are provided, including the Internet of Things (IoT), vehicular network, spaceair-groud integrated network (SAGIN), healthcare, wireless systems, and other applications, along with the design of DITEN in each application, such as DT modeling, DT association, incentive mechanisms, and so on. Finally, challenges and open issues are discussed.

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Section: A Typical Services Of Ditenmentioning

confidence: 99%

Section: A Typical Services Of Ditenmentioning

confidence: 99%

Section: A Typical Services Of Ditenmentioning

confidence: 99%

Section: A Typical Services Of Ditenmentioning

confidence: 99%

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Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Tang

Chen

Rodrigues

et al. 2022

IEEE Open J. Commun. Soc.

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“…The combination of multi-mode communication network and edge computing provides a flexible data transmission and processing mechanism for the development of green IoT services in smart building [13,14]. In order to realise efficient task offloading with low latency and low energy consumption, the joint optimisation of multimode channel selection and gateway selection should be taken into account [15,16]. However, latency-sensitive task offloading for green IoT in smart building still faces some technical challenges, which are elaborated below.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional quota matching‐based latency‐sensitive task offloading for multi‐mode green IoT in smart buildings

et al. 2022

Self Cite

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The green internet of things with heterogeneous communication technologies can provide data transmission and computing services for low‐carbon operation of smart buildings. However, latency‐sensitive task offloading in smart buildings for multi‐mode green internet of things still faces several challenges such as coupling between multi‐mode channel and multiple gateway selection, diversified quality of service requirement guarantee, and contradiction of long‐term performance guarantee and short‐term optimisation objectives. To address these challenges, a three‐dimensional quota matching‐based latency‐sensitive task offloading algorithm is proposed to minimise the weighted difference between energy consumption and throughput under the long‐term queuing delay constraints. Specifically, the minimisation problem is decoupled by Lyapunov optimisation. The three‐dimensional quota matching among devices, gateways, and channels is employed to solve the conflicts between gateway selection and channel selection. Finally, the three‐dimensional quota matching is converted to a two‐side quota matching to further reduce complexity and solved iteratively. Numerical results demonstrate that compared with H3CG and MMCS, the proposed algorithm improves the weighted difference between energy consumption and throughput by 21.85% and 27.91%, respectively, and reduces the sensor‐side average queuing delay by 30.82% and 16.83%, and gateway‐side average queuing delay by 16.57% and 26.71%, respectively.

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Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization

Zhou,

Yu,

Luo

et al. 2024

Adv Control Appl

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This research is dedicated to the optimization of power system terminal image processing based on RL and MEC. With the continuous development of power system, the demand for image processing of terminal equipment is increasing day by day. However, traditional image processing methods have the problems of high computing complexity and real‐time and energy consumption. To solve this problem, this study introduces the idea of RL and MEC to improve the efficiency and performance of image processing of power system terminals. By modeling and optimizing the image processing task of the power system terminal equipment, the intelligent adjustment of the processing parameters is realized to adapt to the needs of different scenarios. MEC technology is introduced to move image processing tasks from the central server to the edge device, reducing data transmission delay and network burden, thus improving real‐time performance and reducing energy consumption. The experimental results show that the proposed optimization method based on RL and MEC has a significant performance improvement compared with the traditional method in the power system terminal image processing. The framework our proposed has achieved significant improvement in task completion latency, achieving higher system energy efficiency compared to traditional methods.

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Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids

Cited by 97 publications

References 27 publications

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Survey on Digital Twin Edge Networks (DITEN) Toward 6G

Three‐dimensional quota matching‐based latency‐sensitive task offloading for multi‐mode green IoT in smart buildings

Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization

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