论文标题
可分开的时空kriging用于快速虚拟传感
Separable spatio-temporal kriging for fast virtual sensing
论文作者
论文摘要
环境监视是一项任务，需要具有有限的传感器读数的系统范围内的信息。在接近原则下，环境监测系统可以基于虚拟传感逻辑，然后依靠基于距离的预测方法，例如$ k $ -neart-neart-neart-neegnbors，逆距离加权回归和时空kriging。最后一个数据集很麻烦，但是我们表明，合适的可分离性假设降低了其计算成本的程度，而不是考虑到较大的。仅需要以集中式方式进行空间插值，而预测可以委派给每个传感器。这种简化主要与涉及两个单独模型的事实有关，一个在时间域中，一个在空间域中。可以在复合可能性方法下重新估算另一个模型中的任何一个中的任何一个。此外，使用方便的空间模型和时间模型可以减轻计算。我们表明，对于Kriging的看法即使在高数据集的情况下也可以执行虚拟传感。
Environmental monitoring is a task that requires to surrogate system-wide information with limited sensor readings. Under the proximity principle, an environmental monitoring system can be based on the virtual sensing logic and then rely on distance-based prediction methods, such as $k$-nearest-neighbors, inverse distance weighted regression and spatio-temporal kriging. The last one is cumbersome with large datasets, but we show that a suitable separability assumption reduces its computational cost to an extent broader than considered insofar. Only spatial interpolation needs to be performed in a centralized way, while forecasting can be delegated to each sensor. This simplification is mostly related to the fact that two separate models are involved, one in time and one in the space domain. Any of the two models can be replaced without re-estimating the other under a composite likelihood approach. Moreover, the use of convenient spatial and temporal models eases up computation. We show that this perspective on kriging allows to perform virtual sensing even in the case of tall datasets.
