A review of graph-powered data quality applications for IoT monitoring sensor networks
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 …
of monitoring networks for a wide variety of applications, such as smart cities, environmental …
GraphHD: Efficient graph classification using hyperdimensional computing
Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for
machine learning inspired by neuroscience. HDC exploits characteristics of biological …
machine learning inspired by neuroscience. HDC exploits characteristics of biological …
A global multiunit calibration as a method for large-scale IoT particulate matter monitoring systems deployments
S De Vito, G D'Elia, S Ferlito… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Scalable and effective calibration is a fundamental requirement for low-cost air quality (AQ)
monitoring systems and will enable accurate and pervasive monitoring in cities. Suffering …
monitoring systems and will enable accurate and pervasive monitoring in cities. Suffering …
Data reconstruction applications for IoT air pollution sensor networks using graph signal processing
The analysis of sensor networks for air pollution monitoring is challenging. Recent studies
have demonstrated the ability to reconstruct the network measurements with graphs derived …
have demonstrated the ability to reconstruct the network measurements with graphs derived …
Volterra graph-based outlier detection for air pollution sensor networks
Today's air pollution sensor networks pose new challenges given their heterogeneity of low-
cost sensors and high-cost instrumentation. Recently, with the advent of graph signal …
cost sensors and high-cost instrumentation. Recently, with the advent of graph signal …
Graph signal reconstruction techniques for iot air pollution monitoring platforms
Air pollution monitoring platforms play a very important role in preventing and mitigating the
effects of pollution. Recent advances in the field of graph signal processing have made it …
effects of pollution. Recent advances in the field of graph signal processing have made it …
Graph learning from band-limited data by graph Fourier transform analysis
A graph provides an effective means to represent the statistical dependence or similarity
among signals observed at different vertices. A critical challenge is to excavate graphs …
among signals observed at different vertices. A critical challenge is to excavate graphs …
Leveraging spatiotemporal correlations with recurrent autoencoders for sensor anomaly detection
The introduction of high-and low-cost Internet of Things (IoT) sensors in air quality
monitoring networks, in addition to providing a cost-effective solution for monitoring pollutant …
monitoring networks, in addition to providing a cost-effective solution for monitoring pollutant …
A graph-based approach for missing sensor data imputation
The Internet of Things (IoT) oriented intelligent services require high-quality sensor data
delivery in the wireless sensor networks (WSNs). However, either due to the sensor …
delivery in the wireless sensor networks (WSNs). However, either due to the sensor …
The deterministic sensor deployment problem for barrier coverage in WSNs with irregular shape areas
CF Cheng, CC Hsu - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
Most extant studies of barrier coverage in wireless sensor networks (WSNs) have assumed
that the region of interest (RoI) is in a rectangular shape and sensors are randomly …
that the region of interest (RoI) is in a rectangular shape and sensors are randomly …