.png){kind=icon}
.png){kind=icon}
Return
Graph Signal Processing for IoT Sensor Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/COMPSAC54236.2022.00264.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
GSP
IoT
sampling and interpolation
Journal
I
IF:
0
Papers:
5
Citations:
0