Sensor Selection for Crowdsensing Dynamical Systems

We model crowdsensing as the selection of sensors with unknown variance to monitor a large linear dynamical system. To achieve low estimation error, we propose a Thompson sampling approach combining submodular optimization and a scalable online variational inference algorithm to maintain the posterior distribution over the variance. We also consider three alternative parameter estimation algorithms. We illustrate the behavior of our sensor selection algorithms on real trac data from the city of Dublin. Our online algorithm achieves significantly lower estimation error than sensor selection using a fixed variance value for all sensors.

[1]  Gaurav S. Sukhatme,et al.  Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena , 2012, UAI.

[2]  Elad Hazan,et al.  Beyond Convexity: Online Submodular Minimization , 2009, NIPS.

[3]  Fredrik Lindsten,et al.  Estimation of linear systems using a Gibbs sampler , 2012 .

[4]  Aditya G. Parameswaran,et al.  Evaluating the crowd with confidence , 2013, KDD.

[5]  R. Tibshirani,et al.  Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.

[6]  Markos Papageorgiou,et al.  Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing , 2008 .

[7]  Mohan S. Kankanhalli,et al.  Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes , 2014, ECML/PKDD.

[8]  John B. Moore,et al.  On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure , 1993, IEEE Trans. Signal Process..

[9]  Nicholas R. Jennings,et al.  Crowdsourcing Spatial Phenomena Using Trust-Based Heteroskedastic Gaussian Processes , 2013, HCOMP.

[10]  Andreas Krause,et al.  Nonmyopic Informative Path Planning in Spatio-Temporal Models , 2007, AAAI.

[11]  Eiji Takimoto,et al.  On-Line Estimation of Hidden Markov Model Parameters , 2000, Discovery Science.

[12]  Gerardo Hermosillo,et al.  Learning From Crowds , 2010, J. Mach. Learn. Res..

[13]  Lihong Li,et al.  An Empirical Evaluation of Thompson Sampling , 2011, NIPS.

[14]  Abhimanyu Das,et al.  Algorithms for subset selection in linear regression , 2008, STOC.

[15]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[16]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[17]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[18]  Nando de Freitas,et al.  A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot , 2009, Auton. Robots.

[19]  Andreas Krause,et al.  Toward Community Sensing , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[20]  Andreas Krause,et al.  Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach , 2007, ICML '07.

[21]  Chong Wang,et al.  Stochastic variational inference , 2012, J. Mach. Learn. Res..

[22]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[23]  Jaime G. Carbonell,et al.  A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy , 2010, SDM.

[24]  Simo Särkkä,et al.  Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations , 2009, IEEE Transactions on Automatic Control.

[25]  W. R. Thompson.  ON THE LIKELIHOOD THAT ONE UNKNOWN PROBABILITY EXCEEDS ANOTHER IN VIEW OF THE EVIDENCE OF TWO SAMPLES , 1933 .

[26]  Mohan S. Kankanhalli,et al.  Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes , 2014, ICML.

[27]  Matthew J. Streeter,et al.  An Online Algorithm for Maximizing Submodular Functions , 2008, NIPS.