In this paper we investigate the use of DL architectures for such scenarios, including the effects of weather in both drifting and sensor measurement.
This paper investigates the use of DL architectures for blind WSN calibration, including the effects of weather in both drifting and sensor measurement, ...
Sep 11, 2021 · It proved better than non-DL methods, thus leading to the hypothesis that a good DL model can be created for blind calibration.
Results show that our models reduce the calibration error with an order of magnitude compared to the baseline, showing that DL is a suitable method for WSN ...
Temperature, PM10, and humidity are used as the features of the deep learning models for predicting the PM2.5 values. The results show that both proposed ...
Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality.
This thesis will explore the applicability of DL for blind WSN calibration by improving upon the only previously existing DL model and explore other possible ...
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This paper proposes a novel deep learning method named projection-recovery network (PRNet) to blindly calibrate sensor measurements online, and uses a ...
Bibliographic details on Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks.
Compared with previous methods, PRNet can calibrate 2× of drifted sensors at the recovery rate of 80% under the same level of accuracy requirement. We also ...