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.

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.

We address the problem of minimising latency with computation offloading in digital twin wireless edge networks in industrial Internet-of-Things environment via ultra-reliable and low latency communications links. The minimised latency is obtained by jointly optimising both communication and computation variables, namely transmit power, user association of IoT devices, offloading portions, the processing rate of users and edge servers. To deal with this challenging problem, we propose an iterative algorithm based on alternating optimisation approach combined with inner convex approximation framework. Simulation results demonstrate the proposed algorithm's effectiveness in reducing the latency compared with other benchmark schemes.

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This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computationintensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.Index Terms-Alternating optimisation, digital twin, industrial Internet-of-things, mobile edge computing (MEC), ultra-reliable and low latency communications (URLLC).