Graph Neural Network-Driven Networking for Robust Industrial Wireless Sensor Networks

Industrial wireless sensor networks (IWSNs) play a critical role in enabling real-time monitoring and intelligent automation in modern industrial applications. However, maintaining reliable communication and efficient data transmission in dynamic and interference-prone environments remains a significant challenge. To address these limitations, this article proposes a graph neural network (GNN)-driven networking approach for IWSNs, designed to enhance communication robustness and optimize data processing. Our approach incorporates a minimum capacity constraint and a trainable slack parameter, enabling adaptive network configuration in response to changing conditions. By modeling the network topology as a graph, we formulate a device-centric joint node selection and power allocation (JNP) strategy, leveraging GNNs for real-time decision-making. Simulations benchmark the proposed method against state-of-the-art methods, showing up to a 70% average increase in fifth percentile rate across various network conditions. These results highlight the effectiveness of the proposed JNP strategy in improving IWSN performance for industrial applications.