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• W4379743268 abstract "Summary The power of artificial intelligence of things (AIoT) stems from adapting machine learning (ML) and artificial intelligence (AI) models into abundant intelligent IoT fields, based on a large data stream with different formats, sizes, and timestamps generated by massive numbers of heterogeneous sensors. On the one hand, data acquisition is the fundamental basis for any AIoT systems, but data sensed by massive IoT devices may be noisy and even contain adversarial samples. On the other hand, ensuring the efficiency and robustness in data acquisition is vitally important for data‐driven ML and AI. Recently, besides perceiving ability, the literature has witnessed great development of empowering things with learning and reasoning ability through deep learning models, including recurrent neural networks (RNNs) and/or convolutional neural network (CNNs). However, the existing works have one significant weakness: fail to explicitly leverage the geospatial implications and latent connections among sensors for high‐quality data acquisition and quality control. Graphs are intrinsically suitable for representing the dependencies and inter‐relationships between AIoT data sensing devices. Due to the ability of capturing the complex interactive relationships between nodes and producing high‐level representations of the graph input, graph neural networks (GNNs) have exploded onto various ML and AI fields, to learn from graph‐structured data. Our review covers the latest progresses in GNN for the fundamental atomic task of data acquisition in AIoT. Instead of surveying the abundant GNN schemes in vertically various IoT sensing applications, this paper systematically reviews the horizontal infrastructure that all AIoT fields should have, that is, AIoT data acquisition, based on GNN and other related emerging AI factors. Our contributions include the following aspects: Provide the latest progresses in GNN for the horizontal task of data acquisition in AIoT, propose the unified GNN pipeline based on encoder–decoder paradigm, and systematically categorize and summarize the emerging technologies helpful to address the issues in AIoT data acquisition, especially the noisy and adversarial data, and point out some future directions about GNN‐based AIoT data acquisition." @default.
• W4379743268 created "2023-06-08" @default.
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• W4379743268 date "2023-06-06" @default.
• W4379743268 modified "2023-10-14" @default.
• W4379743268 title "Artificial intelligence of things (<scp>AIoT</scp>) data acquisition based on graph neural networks: A systematical review" @default.
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• W4379743268 doi "https://doi.org/10.1002/cpe.7827" @default.
• W4379743268 hasPublicationYear "2023" @default.
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