In this paper, we present a system calibration model (SCM) for mobile PM2.5 sensing systems using COTS low-cost particle sensors. To implement such systems, we first assess the accuracy of low-cost dust sensors and identify the most reliable sensor through a comprehensive set of evaluations. We also investigate the inner working principle of the selected sensor. By conducting a set of lab-scale controlled experiments, we obtained a logarithmic regression model that models the impacts of mobility and ambient wind velocity on PM2.5 sensing results. Moreover, using a low-cost water flow sensor, we design a customized micro anemometer and apply a linear regression model to convert the flow rate readings from the sensor to wind velocity values. Finally, we conduct a field experiment to evaluate the proposed calibration model in a real-world setting. The results show that the accuracy of the PM2.5 measurement results improves significantly when the model is utilized. The calibration model is simple and effective, and it can be utilized by other mobile sensing applications that facilitate micro-scale environmental sensing on the move.
Liu, H. M., Wu, H. C., Lee, H. C., Ho, Y. H., & Chen, L. J. (2018). A system calibration model for mobile PM2.5 sensing using low-cost sensors. In Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017 (Vol. 2018-January, pp. 611–618). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.97