  This paper concerns the data-driven sensor deployment problem in large
spatiotemporal fields. Traditionally, sensor deployment strategies have been
heavily dependent on model-based planning approaches. However, model-based
approaches do not typically maximize the information gain in the field, which
tends to generate less effective sampling locations and lead to high
reconstruction error. In the present paper, a data-driven approach is developed
to overcome the drawbacks of the model-based approach and improve the
spatiotemporal field reconstruction accuracy. The proposed method can select
the most informative sampling locations to represent the entire spatiotemporal
field. To this end, the proposed method decomposes the spatiotemporal field
using principal component analysis (PCA) and finds the top r essential entities
of the principal basis. The corresponding sampling locations of the selected
entities are regarded as the sensor deployment locations. The observations
collected at the selected sensor deployment locations can then be used to
reconstruct the spatiotemporal field, accurately. Results are demonstrated
using a National Oceanic and Atmospheric Administration sea surface temperature
dataset. In the present study, the proposed method achieved the lowest
reconstruction error among all methods.
