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.

传感器的放置是最近已引起广泛关注的优化问题。为了获得准确的在线预测模型更新，使用传感器提供观测结果。当最优选择传感器位置时，预​​测模型可以大大减少其内部误差。贪婪选择算法用于从数字嵌入空间中定位这些最佳空间位置。提出了一种基于变分高斯过程的解决此大数据问题的新颖体系结构。通过对其输入进行预处理，可以进一步提高模型的通用性：实施蒙版自回归流以了解条件建模的空间特征的非线性可逆转换。最后，提出了一种全局优化策略，扩展了基于互信息的优化和所选最优位置的微调。尽管与空间建模和放置任务相关的复杂性很高，但该方法已并行化以加快计算时间，从而使这些工具非常快速。考虑到位于英国伦敦的大象和城堡的克拉伦斯中心大楼内的一个房间，将模型应用于实际的三维测试案例。