Graph Neural Networks in IoT: A Survey

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems 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 codes 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 GNN4IoT.

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152

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References

Details

Published: Apr 05, 2023
Vol/Issue: 19(2)
Pages: 1-50
License: View

Authors

G

Guimin Dong

Amazon and University of Virginia

M

Mingyue Tang

University of Virginia

Z

Zhiyuan Wang

University of Virginia

J

Jiechao Gao

University of Virginia

S

Sikun Guo

University of Virginia

L

Lihua Cai

University of Virginia and South China Normal University

R

Robert Gutierrez

University of Virginia

B

Bradford Campbel

University of Virginia

L

Laura E. Barnes

University of Virginia

M

Mehdi Boukhechba

University of Virginia

Funding

National Cancer Institute of the National Institutes of Health Award: R01CA239246

DARPA Warfighter Analytics using Smartphones for Health (WASH) program

University of Virginia Engineering in Medicine Seed Award

Cite This Article

Guimin Dong, Mingyue Tang, Zhiyuan Wang, et al. (2023). Graph Neural Networks in IoT: A Survey. ACM Transactions on Sensor Networks, 19(2), 1-50. https://doi.org/10.1145/3565973

Graph Neural Networks in IoT: A Survey

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