Graph Neural Networks in IoT: A Survey

TL;DR

This survey reviews recent advances in applying Graph Neural Networks to IoT, highlighting their ability to model complex sensor interactions and improve IoT data analysis, with a comprehensive analysis and future directions.

Contribution

It provides a detailed overview of GNN applications in IoT, including design analysis, public datasets, source code, and future research directions.

Findings

GNNs achieve state-of-the-art results in IoT tasks.

A comprehensive list of datasets and open-source implementations.

Identification of key challenges and future research directions.

Abstract

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.