Reducing Offloading Latency for Digital Twin Edge Networks in 6G

Sun, Wen; Zhang, Haibin; Wang, Rong; Zhang, Yan

doi:10.1109/tvt.2020.3018817

Cited by 258 publications

(116 citation statements)

References 27 publications

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“…A detailed survey on mobile data offloading can be found in Rebecchi et al [3] and Zhou et al [12] and references therein. Literature on the problem of mobile data offloading can be classified based on (1) the system model, ( 2) system parameters, (3) performance metrics, and (4) the analytical framework adopted, such as game theory [13][14][15], optimization [16][17][18][19], artificial intelligence/learning-based methods [20,21], and stochastic/queuing theory-based methods [9,[23][24][25]. Table 1 provides the classification of some of the literature in the area of mobile data offloading.…”

Section: Literature Surveymentioning

confidence: 99%

Delayed mobile data offloading scheme for quality of service traffic: Design and analysis

Ajith

Venkatesh

2021

IET Networks

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As the world is entering the era of 5G, we are witnessing exponential growth in the volume of data traffic generated and consumed by mobile devices. Mobile data offloading handles the surge in mobile data traffic by offloading part of the traffic onto the Wi-Fi network. In this context, we design and analyse a quality of service (QoS) enhancement scheme for delayed mobile data offloading. We first consider prioritised queuing of traffic as a scheme for QoS provisioning. Using the concepts of virtual waiting time, renewal process, and level crossing arguments, we derive the average transmission delay of the offloaded and reneged packets of high priority. Next, we mathematically validate the benefit of balking all such packets whose expected virtual waiting time exceeds their deadline. Using a three-dimensional Markov chain model, we derive the required balking probability. Moreover, we merge the technique of balking with prioritisation for improving QoS in delayed offloading. Through an extensive simulation, we validate our analysis and demonstrate how the proposed scheme reduces the transmission delay without sacrificing offloading efficiency. Our investigation has the potential to be adopted in future mobile data offloading standards for improving QoS.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Literature Surveymentioning

confidence: 99%

Delayed mobile data offloading scheme for quality of service traffic: Design and analysis

Ajith

Venkatesh

2021

IET Networks

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“…Mobile edge computing (MEC) has been recently considered as one of the promising solutions for Internet-of-Things (IoT) devices (e.g., sensors, smartphones, etc.) to reduce end-to-end latency by offloading their computing tasks to surrounding macro base stations (MBS) equipped with powerful computing resources [1], [2]. However, when large-scale scenarios with the heterogeneous deployment of IoT devices (UEs) and edge servers in the MEC system are considered, the challenges in designing the optimal offloading strategy with efficient resource allocation grow significantly due to the network size and dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…However, when large-scale scenarios with the heterogeneous deployment of IoT devices (UEs) and edge servers in the MEC system are considered, the challenges in designing the optimal offloading strategy with efficient resource allocation grow significantly due to the network size and dynamics. As a potential digital mapping technology, digital twin (DT) brings an excellent solution for intelligent resource allocation and network management in the MEC system by creating a real-time digital representation of the physical equipment [1]. By combining MEC and DT, the network status information can be efficiently monitored in real time and then provided directly to the decision-making module in the network in a centralised viewpoint.…”

Section: Introductionmentioning

confidence: 99%

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

Do‐Duy

Huynh

Dobre

et al. 2022

IEEE Wireless Commun. Lett.

101

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In this paper, 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 minimisation 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 minimisation problem by iteratively optimising 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.

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“…With fifth-generation (5G) networks being available now, the sixth-generation (6G) wireless network is currently under research, which is expected to provide superior performance to satisfy the growing demands of mobile equipment, such as latency-sensitive, energy-hungry, and computationally intensive services and applications [ 1 , 2 ]. For example, the Internet of Things (IoT) networks are being developed rapidly, where massive numbers of nodes are supposed to be connected together, and IoT nodes can not only communicate with each other, but also process acquired data [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Fang

Ding

2021

Entropy

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Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme.

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Reducing Offloading Latency for Digital Twin Edge Networks in 6G

Cited by 258 publications

References 27 publications

Delayed mobile data offloading scheme for quality of service traffic: Design and analysis

Delayed mobile data offloading scheme for quality of service traffic: Design and analysis

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

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

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