用图模型提升物联网传感网络数据质量，助力智能决策
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks
- 基于图结构建模传感器数据，融合图信号处理与图神经网络
- 解决数据缺失、异常检测、虚拟传感等关键质量问题
- 适合关注物联网数据治理与数字孪生的科研与工程人员
物联网技术的快速发展推动了各类监测网络在智慧城市、环境监控和精准农业中的广泛应用。近年来，研究重点转向基于图的方法以提升传感网络数据质量，这对决策支持、数字孪生等应用至关重要。通过利用图拓扑结构带来的数据有序性，图信号处理（GSP）和图神经网络（GNNs）等技术被广泛应用于数据质量增强任务。本文综述了图模型在传感网络数据质量控制中的应用，深入分析了缺失值填补、异常检测和虚拟传感等常见技术细节。最后，展望了面向数字孪生的图模型、模型可迁移性与泛化能力等未来挑战与趋势。
原文摘要 · Abstract (English)
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.
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