Graph-Convolutional-Network-Enabled Task Offloading for Industrial Image Recognition in Digital Twin Edge Networks

With the rapid advancement of 6G, the task offloading has emerged as a critical issue for enhancing computational efficiency in the Industrial Internet of Things (IIoT). However, industrial devices are often constrained by computing power, energy and mobility, challenging the delay-sensitive and compute-intensive tasks consisting of subtasks with complex dependencies, e.g., industrial image recognition. Given the increasing task complexity, developing efficient offloading strategies in dynamic multislot industrial scenarios with mobile devices remains a challenge. To address this issue, a task offloading scheme for industrial image recognition in digital twin edge networks (DITENs) is proposed. By leveraging the digital twin (DT) to accurately model the states of mobile industrial tasks and edge servers, the task offloading is formulated to optimize the weighted sum of task processing delay and energy consumption, that is proven to be NP-hard. Since the task of image recognition can be represented as a directed acyclic graph (DAG), the dependencies between subtasks are extracted using graph convolutional network (GCN) to generate optimal execution priorities for task offloading. Through proving the optimization problem as a Markov decision process (MDP), an improved multiagent deep deterministic policy gradient algorithm, named $\epsilon $ -ATN-MADDPG, incorporating the $\epsilon $ -greedy strategy and the self-attention mechanism to enable efficient decision-making in dynamic environments, is designed to offer a promising solution. Experimental results on the KolektorSDD dataset demonstrate that this solution outperforms compared methods.