Survey of Graph Neural Network for Internet of Things and NextG Networks

TL;DR

The survey presents a comprehensive overview of Graph Neural Networks (GNNs) and their applications in Internet of Things (IoT) and NextG networks, detailing GNN architectures, variants, and the core advantages of graph-structured learning for dynamic, interdependent wireless environments. It then systematically reviews GNN usage across IoT smart applications, data fusion, intrusion detection, spectrum awareness (RF sensing and modulation classification), networking (prediction, routing, congestion control, MEC, and digital twins), UAV networks, and tactical systems (target recognition and localization). The article distills key lessons, including scalability, real-time constraints, multi-modal data integration, robustness to adversarial threats, and the need for standardized benchmarks, while outlining a forward-looking roadmap with interpretability, privacy, zero-touch design, and sim-to-real bridging as central themes. Overall, it positions GNNs as a promising, flexible framework to enable intelligent, scalable, and secure IoT/NextG networks, while highlighting practical research directions to realize their full potential in real-world deployments.

Abstract

The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become one of the important thrusts. In addition to the advancement in wireless technology, it is important to efficiently use the resources available and meet the users' requirements. Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights which inherently exhibit complex network structures due to its high performance and accuracy, scalability, adaptability, and resource efficiency. There is a lack of a comprehensive survey that focuses on the applications and advances GNN has made in the context of IoT and Next Generation (NextG) networks. To bridge that gap, this survey starts by providing a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Then we provide a comprehensive survey of the advancements in applying GNNs for IoT from the perspective of data fusion and intrusion detection. Thereafter, we survey the impact GNN has made in improving spectrum awareness. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems. Through this survey, we aim to provide a comprehensive resource for researchers to learn more about GNN in the context of wireless networks, and understand its state-of-the-art use cases while contrasting to other machine learning approaches. Finally, we also discussed the challenges and wide range of future research directions to further motivate the use of GNN for IoT and NextG Networks.

Figures (16)

• Figure 1: Applications of IoT.
• Figure 2: Vision of GNN-enabled 6G Wireless Network.
• Figure 3: Overall Organization of the Survey.
• Figure 4: Architecture of Simple GNN.
• Figure 5: Architecture of GNN based on Message Passing Neural Networks.