An investigation on duty-cycle for particulate matter monitoring with light-scattering sensors

Air pollution is a critical phenomenon of the era we live in. Traditional approaches to monitor this phenomenon involve the use of a sparse network of high-cost, high-precision fixed devices. Thanks to the decreasing cost of low-end sensors, it is now possible to implement much cheaper air pollution monitoring devices with respect to the past. Despite having lower accuracy, these devices are able to correctly monitor quantities such as Particulate Matter (PM). However, devices operating within the Internet of Things (IoT) framework still present several issues, such as power supply constraint, logging of redundant information, aging of sensors. To address these issues, this paper analyses how a change in the duty-cycle operation of PM sensors impacts aggregated data, in order to evaluate the loss of information. This analysis has been applied to both raw and calibrated values. The calibration adopted is a Multivariate Linear Regression using Relative Humidity as an additional independent variable. Results show that, in certain circumstances, even a great reduction of the active time does not significantly increase the information loss. The adoption of a duty-cycle operation mode enables a significant reduction of power consumption, slows the aging of sensors, and reduces logging and transmission of redundant information. Furthermore, since a reduced number of samples are generally required in a single location, mobile continuous sampling strategies can be adopted in order to increase the coverage of PM sensors.