A review of graph-powered data quality applications for IoT monitoring sensor networks

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 of the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning (ML) 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 graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. This survey focuses on graph-based models for data quality control in monitoring sensor networks. In addition, it introduces the technical details that are commonly used to provide powerful graph-based solutions for data quality tasks in sensor networks, such as missing value imputation, outlier detection, or virtual sensing. To conclude, different challenges and emerging trends have been identified, e.g., graph-based models for digital twins or model transferability and generalization.