A QoE-Driven Efficient Task Scheduling Method for Symbiotic Internet of Things in Industrial Intelligent Manufacturing Systems

Yujun Lan, Shuo Zhao, Zhongwen Guo, Bo Pang, Wenxiang Jiang, Hailei Zhao, Hui Xia

Abstract: The symbiotic Internet of Things (IoT) computing paradigm leverages high-speed transmission technologies, such as 6G, to execute large-scale model computations while preserving data privacy, thereby mitigating significant economic losses from data breaches. This paradigm has emerged as a pivotal focus in industrial intelligent manufacturing research. However, within this framework, edge devices must concurrently support both large-scale model computations and high-precision industrial core tasks, creating critical challenges in system efficiency and stability that impede technological advancement. To address these issues, this article introduces a novel Quality of Experience (QoE)-driven heuristic task resource scheduling algorithm that employs Composite Differential Evolution (CoDE) integrated with a tabular Kolmogorov–Arnold Network (KANTab), specifically designed for the comprehensive processes of industrial intelligent manufacturing systems. This methodology enables precise simulation of extensive industrial task requirements and efficient allocation of computational resources under constrained conditions, effectively resolving the resource allocation conflict between large-scale model computation tasks at the edge and primary tasks on terminal devices. We evaluate our approach on a custom-built large-scale task demand dataset from refrigerator manufacturing and demonstrate that it achieves superior overall performance compared to state-of-the-art algorithms.

External IDs:dblp:journals/iotj/LanZGPJZX25