In this paper, we present an energy, data and cost efficient model for mobile opportunistic PM2.5 sensing via bicycles. To facilitate the implementation of such systems, we first investigate the accuracy issue of different Inertial Monitoring Unit (IMU) built into the Arduino 101 for stop detection. Then, by curve fitting and optimization calculation on system parameter tuning and modeling, each sensor could start up at the optimum time in order to achieve minimum energy consumption and maximum data usability. Also we propose a formula that can count the minimum number of required PM2.5 sensors under the condition of total experiment time spent and total expected number of sampling point. Finally, we conduct a field experiment to evaluate the proposed model in a real world setting. The results show that total time spent of PM2.5 data collection is similar to the expected time derived from the system modeling.
Mahajan, S., Liu, H. M., Huang, T. Y., Tsai, T. C., & Chen, L. J. (2017). Opportunistic PM2.5 Sensing: A Feasibility Study. In 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings (Vol. 2018-January, pp. 1–6). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/GLOCOM.2017.8254514