A system calibration model for mobile PM2.5 sensing using low-cost sensors

Hao Min Liu, Hsuan Cho Wu, Hu Chen Lee, Yao Hua Ho, Ling Jyh Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 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
EditorsGeyong Min, Xiaolong Jin, Laurence T. Yang, Yulei Wu, Nektarios Georgalas, Ahmed Al-Dubi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages611-618
Number of pages8
ISBN (Electronic)9781538630655
DOIs
Publication statusPublished - 2018 Jan 30
EventJoint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 - Exeter, United Kingdom
Duration: 2017 Jun 212017 Jun 23

Publication series

NameProceedings - 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
Volume2018-January

Other

OtherJoint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017
CountryUnited Kingdom
CityExeter
Period17/6/2117/6/23

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ASJC Scopus subject areas

  • Hardware and Architecture
  • Renewable Energy, Sustainability and the Environment
  • Communication
  • Artificial Intelligence
  • Computer Networks and Communications

Cite this

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 G. Min, X. Jin, L. T. Yang, Y. Wu, N. Georgalas, & A. Al-Dubi (Eds.), 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 (pp. 611-618). (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). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.97