Abstract
Urban air pollution monitoring with mobile, portable, low-cost sensors has attracted increasing research interest for their wide spatial coverage and affordable expenses to the general public. However, low-cost air quality sensors not only drift over time but also suffer from cross-sensitivities and dependency on meteorological effects. Therefore calibration of measurements from low-cost sensors is indispensable to guarantee data accuracy and consistency to be fit for quantitative studies on air pollution. In this work we propose sensor array network calibration (SCAN), a multi-hop calibration technique for dependent low-cost sensors. SCAN is applicable to sets of co-located, heterogeneous sensors, known as sensor arrays, to compensate for cross-sensitivities and dependencies on meteorological influences. SCAN minimizes error accumulation over multiple hops of sensor arrays, which is unattainable with existing multi-hop calibration techniques. We formulate SCAN as a novel constrained least-squares regression and provide a closed-form expression of its regression parameters. We theoretically prove that SCAN is free from regression dilution even in presence of measurement noise. In-depth simulations demonstrate that SCAN outperforms various calibration techniques. Evaluations on two real-world low-cost air pollution sensor datasets comprising 66 million samples collected over three years show that SCAN yields 16% to 60% lower error than state-of-the-art calibration techniques.
- Alphasense. 2014. CO-B4 4-Electrode carbon monoxide sensor (datasheet). http://goo.gl/egp6Sm. (2014).Google Scholar
- Adrian Arfire, Ali Marjovi, and Alcherio Martinoli. 2016. Enhancing Measurement Quality through Active Sampling in Mobile Air Quality Monitoring Sensor Networks. In Proc. of AIM. IEEE, 1022--1027.Google ScholarDigital Library
- Adrian Arfire, Ali Marjovi, and Alcherio Martinoli. 2016. Mitigating Slow Dynamics of Low-Cost Chemical Sensors for Mobile Air Quality Monitoring Sensor Networks. In Proc. of EWSN. ACM, 159--167. Google ScholarDigital Library
- Sudipto Banerjee and Anindya Rov. 2014. Linear Algebra and Matrix Analysis for Statistics. Taylor 8 Francis Group.Google Scholar
- N. Barsan, D. Koziej, and U. Weimar. 2007. Metal oxide-based gas sensor research: How to? Sensors and Actuators B: Chemical 121, 1 (2007), 18 -- 35.Google ScholarCross Ref
- R. Bro. 2003. Multivariate calibration What is in chemometrics for the analytical chemist? Analytica Chimica Acta 500 (2003), 185--194.Google ScholarCross Ref
- Matthias Budde, Rayan El Masri, Till Riedel, and Michael Beigl. 2013. Enabling Low-cost Particulate Matter Measurement for Participatory Sensing Scenarios. In Proc. of MUM. ACM, 19:1--19:10. Google ScholarDigital Library
- Matthias Budde, Marcel Köpke, and Michael Beigl. 2015. Robust In-situ Data Reconstruction from Poisson Noise for Low-cost, Mobile, Non-expert Environmental Sensing. In Proc. of ISWC. ACM, 179--182. Google ScholarDigital Library
- Yaron Danon and Mark Embrechts. 1992. Least Squares Fitting Using Artificial Neural Networks. Intelligent Engineering Systems through Artificial Neural Networks 2 (1992).Google Scholar
- Norman Draper and Yonghong Yang. 1997. Generalization of the Geometric Mean Functional Relationship. Computational Statistics and Data Analysis 23, 3 (1997), 355--372. Google ScholarDigital Library
- W. Eugster and G. W. Kling. 2012. Performance of a low-cost methane sensor for ambient concentration measurements in preliminary studies. Atmospheric Measurement Techniques 5, 8 (2012), 1925--1934.Google ScholarCross Ref
- Chris Frost and Simon G. Thompson. 2000. Correcting for regression dilution bias: comparison of methods for a single predictor variable. Journal of the Royal Statistical Society Series A 163, 2 (2000), 173--189.Google ScholarCross Ref
- Kaibo Fu, Wei Ren, and Wei Dong. 2017. Multihop Calibration for Mobile Sensing: k-hop Calibratability and Reference Sensor Deployment. In Proc. of INFOCOM. IEEE.Google ScholarCross Ref
- Gene H. Golub and Charles F. Van Loan. 1980. An Analysis of the Total Least Squares Problem. SIAM J. Numer. Anal. 17, 6 (1980), 883--893. http://www.jstor.org/stable/2156807Google ScholarDigital Library
- David Hasenfratz, Olga Saukh, and Lothar Thiele. 2012. On-the-fly calibration of low-cost gas sensors. In Proc. of EWSN. ACM, 228--244. Google ScholarDigital Library
- W. Jiao, G. Hagler, R. Williams, R. Sharpe, R. Brown, D. Garver, R. Judge, M. Caudill, J. Rickard, M. Davis, L. Weinstock, S. Zimmer-Dauphinee, and K. Buckley. 2016. Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. Atmospheric Measurement Techniques 9, 11 (2016), 5281--5292.Google ScholarCross Ref
- Henry D. Kahn. 1973. Note On The Distribution of Air Pollutants. Journal of the Air Pollution Control Association 23, 11 (1973), 973--973.Google ScholarCross Ref
- Sunyoung Kim, Eric Paulos, and Mark D. Gross. 2010. WearAir: Expressive T-shirts for Air Quality Sensing. In Proc. of TEI. ACM, 295--296. Google ScholarDigital Library
- Jason Jingshi Li, Boi Faltings, Olga Saukh, David Hasenfratz, and Jan Beutel. 2012. Sensing the Air We Breathe -- The OpenSense Zurich Dataset. In Proc. of AAAI. AAAI, 323--325. Google ScholarDigital Library
- Balz Maag, Olga Saukh, David Hasenfratz, and Lothar Thiele. 2016. Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors. In Proc. of EWSN. ACM, 169--180. Google ScholarDigital Library
- Jan F. Markert, Matthias Budde, Gregor Schindler, Markus Klug, and Michael Beigl. 2016. Private Rendezvous-based Calibration of Low-Cost Sensors for Participatory Environmental Sensing. In Proc. of Urb-IoT. ACM, 82--85. Google ScholarDigital Library
- Emiliano Miluzzo, Nicholas D. Lane, Andrew T. Campbell, and Reza Olfati-Saber. 2008. CaliBree: A Self-calibration System for Mobile Sensor Networks. In Proc. of DCOSS. IEEE, 314--331. Google ScholarDigital Library
- Luis Sánchez, Verónica Gutiérrez, Jose Antonio Galache, Pablo Sotres, Juan Ramón Santana, Javier Casanueva, and Luis Muñoz. 2013. SmartSantander: Experimentation and service provision in the smart city. In Proc. of WPMC. IEEE, 1--6.Google Scholar
- Olga Saukh, David Hasenfratz, and Lothar Thiele. 2015. Reducing Multi-Hop Calibration Errors in Large-Scale Mobile Sensor Networks. In Proc. of IPSN. ACM/IEEE, 274--285. Google ScholarDigital Library
- Olga Saukh, David Hasenfratz, Christoph Walser, and Lothar Thiele. 2013. On Rendezvous in Mobile Sensing Networks. In Proc. of RealWSN. Springer, 29--42.Google Scholar
- Peter H. Schönemann. 1966. A generalized solution of the orthogonal procrustes problem. Psychometrika 31, 1 (1966), 1--10.Google ScholarCross Ref
- Sensirion AG. 2016. SHTC1 Humidity and Temperature Sensor IC (datasheet). https://goo.gl/boJh2T. (2016).Google Scholar
- SGX Sensortech. 2014. MiCS-OZ-47 ozone sensor (datasheet). http://goo.gl/C49tcw. (2014).Google Scholar
- Laurent Spinelle, Michel Gerboles, Maria Gabriella Villani, Manuel Aleixandre, and Fausto Bonavitacola. 2015. Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide. Sensors and Actuators B: Chemical 215 (2015), 249 -- 257.Google ScholarCross Ref
- Yele Sun, Guoshun Zhuang, Ying Wang, Lihui Han, Jinghua Guo, Mo Dan, Wenjie Zhang, Zifa Wang, and Zhengping Hao. 2004. The air-borne particulate pollution in Beijing — concentration, composition, distribution and sources. Atmospheric Environment 38, 35 (2004), 5991 -- 6004.Google ScholarCross Ref
- Rundong Tian, Christine Dierk, Christopher Myers, and Eric Paulos. 2016. MyPart: Personal, Portable, Accurate, Airborne Particle Counting. In Proc. of CHI. ACM, 1338--1348. Google ScholarDigital Library
- Chris Tofallis. 2002. Model Fitting for Multiple Variables by Minimising the Geometric Mean Deviation. In Total Least Squares and Errors-In-Variables Modeling: Algorithms, Analysis And Applications.Google Scholar
- E. B. Woolley. 1941. The method of minimized areas as a basis for correlation analysis. Econometrica 9(1) (1941), 38--62.Google Scholar
- Haibo Ye, Tao Gu, Xianping Tao, and Jian Lu. 2014. SBC: Scalable Smartphone Barometer Calibration Through Crowdsourcing. In Proc. of MobiQuitous. Springer, 60--69. Google ScholarDigital Library
- Yan Zhuang, Feng Lin, Eun-Hye Yoo, and Wenyao Xu. 2015. AirSense: A Portable Context-sensing Device for Personal Air Quality Monitoring. In Proc. of MobileHealth. ACM, 17--22. Google ScholarDigital Library
Index Terms
- SCAN: Multi-Hop Calibration for Mobile Sensor Arrays
Recommendations
W-Air: Enabling Personal Air Pollution Monitoring on Wearables
Accurate, portable and personal air pollution sensing devices enable quantification of individual exposure to air pollution, personalized health advice and assistance applications. Wearables are promising (e.g., on wristbands, attached to belts or ...
A Direct Wideband Direction of Arrival Estimation under Compressive Sensing
MASS '13: Proceedings of the 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor SystemsCompressive Sensing (CS) theory witnesses great breakthrough in signal acquisition and processing. In this paper we focus on the application of direction of arrival(DoA) and proposed a compressive sensing based direct DoA estimation framework (CS-DDoA) ...
Software Calibration of Wirelessly Networked Sensors
SENSORCOMM '09: Proceedings of the 2009 Third International Conference on Sensor Technologies and ApplicationsThis paper introduces our work on the communication stack of wireless sensor networks. We present the IPv6 approach for wireless sensor networks called 6LoWPAN in its IETF charter. We then compare the different implementations of 6LoWPAN subsets for ...
Comments