Sensor Selection for Crowdsensing Dynamical Systems

@InProceedings{pmlr-v38-schnitzler15,
  title = 	 {{Sensor Selection for Crowdsensing Dynamical Systems}},
  author = 	 {Schnitzler, Francois and Yuan Yu, Jia and Mannor, Shie},
  booktitle = 	 {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
  pages = 	 {829--837},
  year = 	 {2015},
  editor = 	 {Lebanon, Guy and Vishwanathan, S. V. N.},
  volume = 	 {38},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {San Diego, California, USA},
  month = 	 {09--12 May},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v38/schnitzler15.pdf},
  url = 	 {https://proceedings.mlr.press/v38/schnitzler15.html},
  abstract = 	 {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 traffic 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.}
}