Graph Powered Machine Learning in Smart Sensor Networks

Shrivastava, Namita; Bhagat, Amit; Nair, Rajit

doi:10.1007/978-3-030-77214-7_9

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…For example, in a multi-modal sensor network (e.g., lighting, environment), each sensor can be represented as a node in a graph, and their latent interconnections need to be learned by using data-driven approaches. Established works [19,25,30,40,42,56,58,71,160,161,175,201,207,208,239,258,284,286,290,318,320,321,334] illustrated the performance of applied GNNs in smart city applications that involved IoT sensor interconnections. Table 3 summarizes the sensor infrastructures, GNN models, and learning targets in the collected works.…”

Section: Iot Sensor Interconnection (Isi)mentioning

confidence: 99%

“…The result demonstrates better performances than those from ARIMA and LSTM methods. Other related works [40,56,239,334] also tried to find latent relationships among sensor data in the above mentioned scenarios by using the sensor to create a graph structure, followed by data-driven approaches to learn the correlations. For example, Chen et al [40] worked on the traffic prediction problem, however, they used cars as nodes to create the graph structure, and proposed a novel end-to-end multiple Res-RGNNs framework to find the dynamic connections among cars while discovering latent relationships between cars before performing traffic prediction.…”

Section: Graph Modeling Of Iot Sensor Interconnectionmentioning

confidence: 99%

“…Edge connection between sensors can be their dynamic connections or knowledge representation. Works in [25,40,56,161,239,284,334] used deep learning methods such as CNNs and RNNs to generate potential representations of the edges, which encode the temporal and local graph structural changes of the nodes in the graph.…”

Section: Graph Modeling Of Iot Sensor Interconnectionmentioning

confidence: 99%

“…The first type of GNNs for ISI is spatio-temporal GNNs, which are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features as demonstrated in the these works [30,40,58,128,201,207,264,284,321]. The second type of GNNs for ISI is graph autoencoder, which maps graph data into a low-dimensional space for graph analytics [56,239,284,320]. Spatio-temporal GNNs: systems consisting of information about structural relationships in space and time can be considered as a spatio-temporal graph.…”

Section: Graph Neural Network For Iot Sensor Interconnectionmentioning

confidence: 99%

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Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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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 GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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