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Quality Aware Graph Learning Regularization For Heterogeneous Air Quality Sensor Networks

TitleQuality Aware Graph Learning Regularization For Heterogeneous Air Quality Sensor Networks
Publication TypeConference Paper
Year of Publication2023
AuthorsFerrer-Cid, P, Ordinas, JMBarcelo, Garcia-Vidal, J
Conference NameProceedings of the 2023 International Conference on Embedded Wireless Systems and Networks (EWSN'23)
Date Published12/2023
PublisherAssociation for Computing Machinery (ACM)
Conference LocationRende, Italy, September 25 - 27
KeywordsAir quality, Graph learning, Low-cost sensors, Regularization, Sensor networks
Abstract

In recent years, the graph signal processing (GSP) field has brought signal processing techniques to numerous areas. Among them, graphs have been used for various applications in sensor networks for air quality monitoring. One of the main tasks consists of learning the graph that describes the relationships between sensors in a network.Although many data-driven graph learning techniques exist, the heterogeneous nature of air quality low-cost sensor networks, where low-cost sensors and high-precision instruments coexist, imposes the need to include information about the quality of the sensors used. Therefore, in this paper, we propose a graph learning regularization framework that allows for taking into account the reliability of the different nodes of an IoT network, in what we call quality-aware graph learning regularization (QAGLR). The regularization framework is evaluated for signal reconstruction and it is also assessed in the case of the creation of GSP-based virtual sensors, showing the benefits of introducing information about sensors’ reliability.

DOI10.5555/3639940.3639954