Variational Gaussian Process for Optimal Sensor Placement

Gabor Tajnafoi*, Rossella Arcucci, Laetitia Mottet, Carolanne Vouriot, Miguel Molina-Solana, Christopher Pain, Yi-Ke GUO

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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 sensor 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 nonlinear, 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.

Original languageEnglish
Pages (from-to)287-317
Number of pages31
JournalApplications of Mathematics
Volume66
Issue number2
DOIs
Publication statusPublished - Apr 2021

Scopus Subject Areas

  • Applied Mathematics

User-Defined Keywords

  • 65Z05
  • 68T99
  • mutual information
  • sensor placement
  • variational Gaussian process

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