Sensor Selection for Crowdsensing Dynamical Systems
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:829-837, 2015.
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.