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Energy-Efficient Air Pollution Monitoring with Optimum Duty-Cycling on a Sensor HubPublished in Institute of Electrical and Electronics Engineers Inc.
2019
Air pollution monitoring systems with energy-intensive sensors cannot afford to sample frequently in order to maximize time between successive recharges. In this paper, we propose an energy-efficient machine learning based sensor duty-cycling method for a sensor hub receiving data from the air-pollution sensors. In particular, we demonstrate that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information. Support Vector Regression is used to predict the missing samples during the period sensor is turned off. © 2018 IEEE.
Topics: Sensor hub (58)%58% related to the paper, Energy consumption (52)%52% related to the paper and Efficient energy use (51)%51% related to the paper
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Figures & Tables (13)
Figure 1: Schematic of the mobile sensing node used 
Table I: Environmental sensors 
Figure 3: Control flow of proposed methodology 
Figure 2: Time-series of always ON v/s duty-cycled sensing 
Figure 4: Experimental set-up 
Figure 5: Lognormal distribution of PM2.5 data 
Figure 8: RMSE of SVR based prediction for different lags ... 
Figure 6: Fraction of each class for different thresholds - 5 figures hidden
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Published in Institute of Electrical and Electronics Engineers Inc.
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