Miguel Molina-Solana

Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensors location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a Variational Gaussian Process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn non-linear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed-up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK.

@article{Tajnafoi2021,
  author = {Tajnafoi, Gabor and Arcucci, Rossella and Mottet, Laetitia and Vouriot, Carolanne and Molina-Solana, Miguel and Pain, Christopher and Guo, Yike},
  title = {Variational Gaussian Process for optimal sensor placement},
  journal = {Applications of Mathematics},
  year = {in press.},
  volume = {},
  doi = {},
  comment = {},
  timestamp = {29}
}