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.

物联网和下一代网络领域，图神经网络的应用和进展作出了重要贡献，并提供了全面的调研，包括数据融合、入侵检测、频谱感知等方面，同时对网络和战术系统的利用进行了详细描述，为研究者提供了一个全面资源。

物联网和 NextG 网络的图神经网络综述

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

Future communication networks must address the scarce spectrum to accommodate
extensive growth of heterogeneous wireless devices. Wireless signal recognition
is becoming increasingly more significant for spectrum monitoring, spectrum
management, secure communications, among others. Consequently, comprehensive
spectrum awareness on the edge has the potential to serve as a key enabler for
the emerging beyond 5G networks. State-of-the-art studies in this domain have
(i) only focused on a single task - modulation or signal (protocol)
classification - which in many cases is insufficient information for a system
to act on, (ii) consider either radar or communication waveforms (homogeneous
waveform category), and (iii) does not address edge deployment during neural
network design phase. In this work, for the first time in the wireless
communication domain, we exploit the potential of deep neural networks based
multi-task learning (MTL) framework to simultaneously learn modulation and
signal classification tasks while considering heterogeneous wireless signals
such as radar and communication waveforms in the electromagnetic spectrum. The
proposed MTL architecture benefits from the mutual relation between the two
tasks in improving the classification accuracy as well as the learning
efficiency with a lightweight neural network model. We additionally include
experimental evaluations of the model with over-the-air collected samples and
demonstrate first-hand insight on model compression along with deep learning
pipeline for deployment on resource-constrained edge devices. We demonstrate
significant computational, memory, and accuracy improvement of the proposed
model over two reference architectures. In addition to modeling a lightweight
MTL model suitable for resource-constrained embedded radio platforms, we
provide a comprehensive heterogeneous wireless signals dataset for public use.

本文提出了一种基于深度神经网络的多任务学习 (MTL) 框架，同时学习调制和信号识别任务，考虑到电磁频谱中的多样性无线信号，该框架能有效地提高分类准确性和学习效率，适用于资源受限的嵌入式电台平台，并提供了广泛的异构无线信号数据集。