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

2024-05-27

arXiv.org

AI Summary

Despite the growing importance of Graph Neural Networks (GNNs) in IoT and 6G networks, no systematic survey exists to date. Method: This paper presents the first comprehensive review of GNNs for wireless systems, analyzing their technical evolution, application paradigms, and cross-layer coordination mechanisms. It synthesizes foundational architectures—including GCN, GAT, and GraphSAGE—within key wireless use cases: data fusion, intrusion detection, spectrum sensing, network optimization, and tactical communications, while integrating wireless channel modeling, distributed graph learning, and edge-coordinated inference. Contribution/Results: We propose a novel taxonomy for GNN applications in wireless networks, distill their structural modeling advantages, and systematically identify core challenges alongside 12 promising research directions. The work establishes the first authoritative, structured, and implementation-oriented theoretical framework and technical guideline for deploying GNNs in 6G intelligent air interfaces and autonomous networks.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Social Networks and Social Media: Applications and emergent phenomena

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.

Problem

• Surveying GNN applications in IoT and NextG networks
• Addressing the lack of comprehensive GNN survey for wireless systems
• Providing GNN resources and use cases for researchers

Innovation

• GNNs model IoT network structures efficiently
• GNNs enhance spectrum awareness in wireless networks
• GNNs improve data fusion and intrusion detection