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Graph Signal Processing for IoT Sensor Networks
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AI
DOI：10.1109/COMPSAC54236.2022.00264.png)
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摘要
En 中文
IoT sensors networks are often characterized by stringent power requirements and a high probability of sensor fault. This paper, thanks to Graph Signal Processing (GSP), aims to model an IoT scenario to find an optimal sensor configuration for battery-saving applications. In detail, the Girwan Newman method is applied to the graph to find clusters and the performance of the described method is evaluated in terms of signal-noise ratio depending on the fraction of sampled sensors. Tests were performed both on simulated data and real data from the European Environment Agency considering several air pollutants concentrations.
Keyword:
GSP
IoT
sampling and interpolation
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