Power-error analysis of sensor array regression algorithms for gas mixture quantification in low-power microsystems

Reliable gas sensors are highly desired for many applications, but their typically poor specificity requires arrays of cross-sensitive sensors to predict identity and concentrations of gas mixtures. A relationship between sensor outputs and gas concentrations can be formulated using regression models. This paper presents a detailed analysis of regression models generated using different algorithms. The analysis incorporates a variety of sensor parameters as well as the power consumption of each model when implemented within a low-power microcontroller. The results provide new insight into the effects of sensor array parameters on prediction errors and the tradeoffs between prediction errors and power for different regression models.