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

TL;DR

This survey reviews graph-based techniques for enhancing data quality in IoT sensor networks, emphasizing machine learning and signal processing methods like GSP and GNNs, and discusses future challenges such as digital twins and model transferability.

Contribution

It provides a comprehensive overview of graph-based models for data quality control in sensor networks, highlighting technical details and future research directions.

Findings

Graph signal processing and neural networks improve data quality tasks.

Techniques address missing data, outliers, and virtual sensing.

Future trends include digital twins and model transferability.

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.