  The Internet of Things (IoT) boom has revolutionized almost every corner of
people's daily lives: healthcare, home, transportation, manufacturing, supply
chain, and so on. With the recent development of sensor and communication
technologies, IoT devices including smart wearables, cameras, smartwatches, and
autonomous vehicles can accurately measure and perceive their surrounding
environment. Continuous sensing generates massive amounts of data and presents
challenges for machine learning. Deep learning models (e.g., convolution neural
networks and recurrent neural networks) have been extensively employed in
solving IoT tasks by learning patterns from multi-modal sensory data. Graph
Neural Networks (GNNs), an emerging and fast-growing family of neural network
models, can capture complex interactions within sensor topology and have been
demonstrated to achieve state-of-the-art results in numerous IoT learning
tasks. In this survey, we present a comprehensive review of recent advances in
the application of GNNs to the IoT field, including a deep dive analysis of GNN
design in various IoT sensing environments, an overarching list of public data
and source code from the collected publications, and future research
directions. To keep track of newly published works, we collect representative
papers and their open-source implementations and create a Github repository at
https://github.com/GuiminDong/GNN4IoT.
