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Optimization of Wireless Sensor Network Deployment for Spatiotemporal Reconstruction and Prediction by Chen, Jiahong; Li, Teng; Wang, Jing; de Silva, Clarence W. is a scholarly article available to read on EtoBox.

What is Optimization of Wireless Sensor Network Deployment for Spatiotemporal Reconstruction and Prediction about?

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.

Author

Chen, Jiahong; Li, Teng; Wang, Jing; de Silva, Clarence W.

Published

2019

Language

EN