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Optimization of Wireless Sensor Network Deployment for Spatiotemporal Reconstruction and Prediction

This paper addresses the problem of optimizing sensor deployment locations to reconstruct and also predict a spatiotemporal field. A novel deep learning framework is developed to find a limited number of optimal sampling locations and based on that, improve the accuracy of spatiotemporal field reconstruction and prediction. The proposed approach first optimizes the sampling locations of a wireless sensor network to retrieve maximum information from a spatiotemporal field. A spatiotemporal reconstructor is then used to reconstruct and predict the spatiotemporal field, using collected in-situ measurements. A simulation is conducted using global climate datasets from the National Oceanic and Atmospheric Administration, to implement and validate the developed methodology. The results demonstrate a significant improvement made by the proposed algorithm. Specifically, compared to traditional approaches, the proposed method provides superior performance in terms of both reconstruction error and long-term prediction robustness.

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