WSN Sampling Optimization for Signal Reconstruction Using Spatiotemporal Autoencoder
IEEE Sensors Journal(2020)
摘要
This paper addresses the problem of optimizing sensor deployment locations of a wireless sensor network to reconstruct and also predict a spatiotemporal signal. Traditional sensor deployment optimization approaches only selects deployment locations and provide the feature in the sampled area. Hence, if the sampling resources are limited, the deployed sensors may not be able to provide all features in the field of interest (input space). To solve this issue, a deep learning framework is developed to reconstruct and predict the entire spatiotemporal signal from a limited amount of observations. In the beginning, the proposed approach optimizes sampling locations to retrieve sufficient features for reconstruction and prediction from historical data. Then, a spatiotemporal autoencoder is used to capture the nonlinear mappings from the in-situ measurements to the entire spatiotemporal signal. 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 spatial prediction robustness.
更多查看译文
关键词
Wireless sensor networks,sparse signal reconstruction,deep learning,long short-term memory
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络