Meta Computing-Driven Optimization of AoI in Industrial IoT: A Hybrid Scheme With Self-Organizing Maps and Reinforcement Learning

Min Wang, Yike Wang, Nuanlai Wang, Shanchen Pang, Xiaobing Sun, Ming Li

Abstract: In the domain of Industrial Internet of Things (IIoT) applications, methodologies, such as meta computing are essential for ensuring timely and efficient data acquisition within intricate environments characterized by multiple edge devices and isolated sensor networks. The Age of Information (AoI), a key metric for evaluating data timeliness and relevance, has emerged as a focal point for improving decision-making and system responsiveness. However, managing these systems in resource-limited edge environments is challenging, as traditional methods struggle to link sink node selection with data collection path planning, leading to inefficiency and poor AoI performance. This article develops a collaborative optimization framework based on meta computing, which dynamically coordinates distributed computational resources as a unified virtual system. It combines self-organizing mapping (SOM) with reinforcement learning (RL) to optimize sink node selection and data collection paths using AoI metrics. Experimental results show the method significantly reduces AoI, improves data freshness, and optimizes energy use, highlighting meta computing’s potential for scalable, efficient IIoT solutions.

External IDs:dblp:journals/iotj/WangWWPSL25