A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks

• URL: http://arxiv.org/abs/2410.21006v1
• Date: Mon, 28 Oct 2024 13:30:22 GMT
• Title: A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks
• Authors: Pau Ferrer-Cid, Jose M. Barcelo-Ordinas, Jorge Garcia-Vidal,
• Abstract summary: We focus on graph-based models for data quality control in monitoring sensor networks. We identify future trends and challenges such as graph-based models for digital twins or model transferability and generalization.
• Score: 0.8192907805418583
• License:
• Abstract: The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect for the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as the graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. In this survey, we focus on graph-based models for data quality control in monitoring sensor networks. Furthermore, we delve into the technical details that are commonly leveraged for providing powerful graph-based solutions for data quality tasks in sensor networks, including missing value imputation, outlier detection, or virtual sensing. To conclude, we have identified future trends and challenges such as graph-based models for digital twins or model transferability and generalization.

Related papers

• Survey of Graph Neural Network for Internet of Things and NextG Networks [3.591122855617648]
  Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights. This survey provides a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems.
  arXiv  Detail & Related papers  (2024-05-27T16:10:49Z)
• Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things [6.374763930914524]
  The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. achieving highly reliable IIoT can be hindered by factors such as the cost of installing large numbers of sensors, limitations in retrofitting existing systems with sensors, or harsh environmental conditions that may make sensor installation impractical. We propose physics-enhanced Graph Neural Networks (GNNs), which integrate principles of physics into graph-based methodologies.
  arXiv  Detail & Related papers  (2024-04-11T18:03:59Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Representation Learning of Knowledge Graph for Wireless Communication Networks [21.123289598816847]
  This article aims to understand the endogenous relationship of wireless data by constructing a knowledge graph according to the wireless communication protocols. A novel model based on graph convolutional neural networks is designed to learn the representation of the graph, which is used to classify graph nodes and simulate the relation prediction.
  arXiv  Detail & Related papers  (2022-08-22T07:36:34Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Data Augmentation for Deep Graph Learning: A Survey [66.04015540536027]
  We first propose a taxonomy for graph data augmentation and then provide a structured review by categorizing the related work based on the augmented information modalities. Focusing on the two challenging problems in DGL (i.e., optimal graph learning and low-resource graph learning), we also discuss and review the existing learning paradigms which are based on graph data augmentation.
  arXiv  Detail & Related papers  (2022-02-16T18:30:33Z)
• A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring [0.7519872646378835]
  We present a framework based on Complex Network Modeling, integrating Graph Signal Processing (GSP) and Graph Neural Network (GNN) approaches. We focus on a prominent real-world structural health monitoring use case, i.e., modeling and analyzing sensor data (strain, vibration) of a large bridge in the Netherlands.
  arXiv  Detail & Related papers  (2021-05-01T10:45:57Z)
• Graph Signal Processing for Geometric Data and Beyond: Theory and Applications [55.81966207837108]
  Graph Signal Processing (GSP) enables processing signals that reside on irregular domains. GSP methodologies for geometric data in a unified manner by bridging the connections between geometric data and graphs. Recently developed Graph Neural Networks (GNNs) interpret the operation of these networks from the perspective of GSP.
  arXiv  Detail & Related papers  (2020-08-05T03:20:16Z)
• Graph signal processing for machine learning: A review and new perspectives [57.285378618394624]
  We review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. We discuss exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. We provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.
  arXiv  Detail & Related papers  (2020-07-31T13:21:33Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.