Digital-Twin-Assisted Task Offloading Based on Edge Collaboration in the Digital Twin Edge Network

Liu, Tong; Tang, Lun; Wang, Weili; Chen, Qianbin; Zeng, Xiaoping

doi:10.1109/jiot.2021.3086961

Cited by 125 publications

(56 citation statements)

References 39 publications

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“…On the other hand, when the tasks is not cached, it is normally processed with the task offloading computing model. We note that the results returned from the AP to UEs are typical small (e.g., controlled messages) and the AP transmits the messages with more power than the UEs so that we only consider the uplink transmission latency in this paper [2], [3]. As a result, the latency model with edge caching is expressed as…”

Section: Latency and Energy Model With Edge Cachingmentioning

confidence: 99%

“…Recently, edge computing assisted DT has attracted attention from the research community [1]- [3]. In particular, a DT edge network has been presented in [1] to deal with the offloading latency minimisation problem.…”

Section: Introductionmentioning

confidence: 99%

“…This paper has taken into account various variables including transmit power, user association, offloading portions, and the estimated processing of IIoT devices to minimise the end-to-end latency with an iterative optimisation algorithm. Another DT-asisted MEC based on edge collaboration has been addressed in [3], which deals with the edge selection and task offloading variables by applying the decision tree algorithm and the DRL-based solution. More recently, the combination of URLLC and the edge computing in the DT paradigm has been introduced in [4], [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

Huynh

Khosravirad

Masaracchia

et al. 2022

IEEE Wireless Commun. Lett.

127

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In this paper, we propose a novel digital twin scheme supported metaverse by jointly considering the integrated model of communications, computing, and storage through the employment of mobile edge computing (MEC) and ultra-reliable and low latency communications (URLLC). The MEC-based URLLC digital twin architecture is proposed to provide powerful computing infrastructure by exploring task offloading, and task caching techniques in nearby edge servers to reduce the latency. In addition, the proposed digital twin scheme can guarantee stringent requirements of reliability and low latency, which are highly applicable for the future networked systems of metaverse. For this first time in the literature, our paper addresses the optimal problem of the latency/reliablity in digital twins-enabled metaverse by optimising various communication and computation variables, namely, offloading portions, edge caching policies, bandwidth allocation, transmit power, computation resources of user devices and edge servers. The proposed scheme can improve the quality-of-experience of the digital twin in terms of latency and reliability with respect to metaverse applications.

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Section: Latency and Energy Model With Edge Cachingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

Huynh

Khosravirad

Masaracchia

et al. 2022

IEEE Wireless Commun. Lett.

127

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show abstract

“…The network physical entities and the wireless communication environment in the physical layer have their twin mapping in the virtual layer, such as twin end devices, twin edge/cloud servers, and twin communication environment. Data such as the real-time states of the physical entities are collected in the physical layer then be transmitted to the twins in the virtual layer via the physical-to-twin interfaces [61].…”

Section: B Frameworkmentioning

confidence: 99%

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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“…In [8], Sun et al utilized the DTs of MEC servers to assist offloading decision by estimating the states of their physical counterparts and providing the training data for decision agent in deep reinforcement learning (DRL) framework. By taking the edge collaboration into account, Liu et al in [19] designed a DT-assisted task offloading scheme, depending on the selection of cooperative and reliable MEC servers via the data acquisition about channel state information as well as the blockchain application in DTs. With the aid of using DTs to capture the running states and behaviors of end devices, Lu et al in [7] developed a blockchain-enabled federated learning (FL) scheme to enhance learning security and data privacy protection for end devices.…”

Section: A Digital Twin In Mec Systemsmentioning

confidence: 99%

Digital Twin-Assisted Edge Computation Offloading in Industrial Internet of Things With NOMA

Zhang¹,

Wang²,

Xue³

et al. 2022

Preprint

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<p>Integrating digital twins (DTs) and multi-access edge computing (MEC) is a promising technology that realizes edge intelligence in 6G, which has been recognized as the key enabler for Industrial Internet of Things (IIoT). In this paper, we explore a DT-assisted MEC system for the IIoT scenario where a DT server is created as a digital counterpart of the MEC server, via estimating the computation state of the MEC server within the DT modelling cycle. To achieve energy and spectrally efficient offloading, we consider that IIoT devices communicate with industrial gateways (IGWs) through a non-orthogonal multiple access (NOMA) protocol. Each IIoT device has an industrial computation task that can be executed locally or fully offloaded to IGW. We aim to minimize the total task completion delay of all IIoT devices by jointly optimizing the IGW's subchannel assignment as well as the computation capacity allocation, edge association, and transmit power control of IIoT device. The resulting problem is shown to be a mixed integer non-convex optimization problem, which is challenging to solve. We decompose the original problem into four sub-problems, and then propose an efficient iterative algorithm to solve this problem by leveraging the block coordinate descent method. Therefore, the subchannel assignment, computation capacity allocation, edge association, and power control are alternately optimized in an iterative way, and then a locally optimal solution of the original problem is obtained until convergence. Simulation results demonstrate the benefits of the proposed scheme over the other benchmarks in reducing the total task completion delay and increasing the percentage of offloading IIoT devices with low complexity.</p>

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Digital-Twin-Assisted Task Offloading Based on Edge Collaboration in the Digital Twin Edge Network

Cited by 125 publications

References 39 publications

Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

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

Digital Twin-Assisted Edge Computation Offloading in Industrial Internet of Things With NOMA

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