Data-driven Sensor Deployment for Spatiotemporal Field Reconstruction

TL;DR

This paper introduces a data-driven sensor deployment method that uses PCA to select the most informative locations, significantly improving the accuracy of reconstructing large spatiotemporal fields compared to traditional model-based approaches.

Contribution

The paper presents a novel PCA-based sensor deployment strategy that enhances field reconstruction accuracy by selecting the most informative sampling locations, overcoming limitations of model-based methods.

Findings

Achieved the lowest reconstruction error on NOAA sea surface temperature data.

Demonstrated superior performance over existing methods in field reconstruction.

Effectively identified key sampling locations for accurate spatiotemporal field representation.

Abstract

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.