Abstract
In modern smart buildings, the temperature measurement is crucial for smart temperature management implemented by a cyber-physical system (CPS). However, sensor drift emerges during the measurement and becomes an intractable obstacle to practical temperature measurement. Previous works on calibrating sensor drift either limit the number of drifted sensors or heavily rely on the partial information about the sensing matrix. In this paper, we establish a sensor spatial correlation model to calibrate drifts. Given prior knowledge, maximum-a-posteriori (MAP) is harnessed to estimate the coefficients of our model, which is formulated as a non-convex problem with three hyper-parameters. An alternating-based optimization method is proposed to solve this non-convex problem. Cross-validation and expectation-maximum with Gibbs sampling are exploited to tune hyper-parameters. Experimental results demonstrate that compared to state-of-the-art methods, the proposed framework can achieve a robust drift calibration and a better trade-off between accuracy and runtime on benchmarks generated by simulator EnergyPlus.
ⒸT. Chen et al. — ACM 2019. This is a minor revision of the work published in DAC’19, June 2–6, 2019, Las Vegas, NV, USA https://doi.org/10.1145/3316781.3317909
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References
X. Chen, X. Li, S.X.-D. Tan, Overview of cyber-physical temperature estimation in smart buildings: from modeling to measurements, in INFOCOM Workshops (2016), pp. 251–256
B. Lin, B. Yu, Smart building uncertainty analysis via adaptive lasso. IET Cyber-Phys. Syst. Theory Appl. 2(1), 42–48 (2017)
Q. Zhu, A. Sangiovanni-Vincentelli, S. Hu, X. Li, Design automation for cyber-physical systems. Proc. IEEE 106(9), 1479–1483 (2018)
K. Ni, N. Ramanathan, M.N.H. Chehade, L. Balzano, S. Nair, S. Zahedi, E. Kohler, G. Pottie, M. Hansen, M. Srivastava, Sensor network data fault types. ACM Trans. Sens. Netw. (TOSN) 5(3), 25 (2009)
Engineer’s Guide to Accurate Sensor Measurements (2016). http://download.ni.com/evaluation/daq/25188_Sensor_WhitePaper_IA.pdf
Findchips (2018). https://www.findchips.com
Y. Wang, A. Yang, Z. Li, P. Wang, H. Yang, Blind drift calibration of sensor networks using signal space projection and Kalman filter, in IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) (2015), pp. 1–6
M. Takruri, S. Challa, R. Yunis, Data fusion techniques for auto calibration in wireless sensor networks, in International Conference on Information Fusion (2009), pp. 132–139
M. Takruri, S. Rajasegarar, S. Challa, C. Leckie, Online drift correction in wireless sensor networks using spatio-temporal modeling, in International Conference on Information Fusion (2008), pp. 1–8
L. Balzano, R. Nowak, Blind calibration of sensor networks, in International Conference on Information Processing in Sensor Networks (IPSN) (2007), pp. 79–88
Y. Wang, A. Yang, Z. Li, X. Chen, P. Wang, H. Yang, Blind drift calibration of sensor networks using sparse Bayesian learning. IEEE Sensors J. 16(16), 6249–6260 (2016)
Z. Zhang, B.D. Rao, Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning. IEEE J. Sel. Top. Sign. Proces. 5(5), 912–926 (2011)
S. Ling, T. Strohmer, Self-calibration and bilinear inverse problems via linear least squares. SIAM J. Imag. Sci. (SIIMS) 11(1), 252–292 (2018)
X. Chen, X. Li, S. X.-D. Tan, From robust chip to smart building: CAD algorithms and methodologies for uncertainty analysis of building performance, in IEEE/ACM International Conference on Computer-Aided Design (ICCAD) (2015), pp. 457–464
2-Terminal IC Temperature Transducer (2013). https://www.analog.com/media/en/technical-documentation/data-sheets/AD590.pdf
F. Wang, P. Cachecho, W. Zhang, S. Sun, X. Li, R. Kanj, C. Gu, Bayesian model fusion: large-scale performance modeling of analog and mixed-signal circuits by reusing early-stage data. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (TCAD) 35(8), 1255–1268 (2016)
Q. Huang, C. Fang, F. Yang, X. Zeng, X. Li, Efficient multivariate moment estimation via Bayesian model fusion for analog and mixed-signal circuits, in ACM/IEEE Design Automation Conference (DAC) (2015), p. 169
Q. Huang, C. Fang, F. Yang, X. Zeng, D. Zhou, X. Li, Efficient performance modeling via dual-prior Bayesian model fusion for analog and mixed-signal circuits, in ACM/IEEE Design Automation Conference (DAC) (2016), pp. 1–6
G.H. Golub, C.F. Van Loan, Matrix Computations (JHU Press, Baltimore, 2012)
K. Ganchev, B. Taskar, J. Gama, Expectation maximization and posterior constraints, in Conference on Neural Information Processing Systems (NIPS) (2008), pp. 569–576
C. Robert, Machine Learning, a Probabilistic Perspective (Taylor & Francis, London, 2014)
T. Salimans, D. Kingma, M. Welling, Markov chain Monte Carlo and variational inference: bridging the gap, in International Conference on Machine Learning (ICML) (2015), pp. 1218–1226
OpenStudio® (2018). https://www.openstudio.net
National Renewable Energy Laboratory OpenStudio Standards (2018). https://github.com/NREL/openstudio-standards
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Chen, T., Lin, B., Geng, H., Yu, B. (2020). Smart Building Sensor Drift Calibration. In: Hu, S., Yu, B. (eds) Big Data Analytics for Cyber-Physical Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-43494-6_8
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DOI: https://doi.org/10.1007/978-3-030-43494-6_8
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