Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing
This letter proposes and formulate a practical end-to-end latency minimization 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 and solves the proposed latency minimizations problem by iteratively optimizing the transmit power of IoT devices, user association, intelligent task offloading, and estimated CPU processing rate of the devices.
Abstract
In this letter, 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 minimization 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 minimization problem by iteratively optimizing 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.