Where Are the Red Lines? Towards Ethical Server-Side Scans in Security and Privacy Research
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Computer Science > Machine Learning
Title: survey of graph neural network for internet of things and nextg networks.
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.
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Fine-Grained Non-Interactive Key-Exchange without Idealized Assumptions
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