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首页> 外文期刊>IEEE sensors journal >WSN Sampling Optimization for Signal Reconstruction Using Spatiotemporal Autoencoder
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WSN Sampling Optimization for Signal Reconstruction Using Spatiotemporal Autoencoder

机译：使用Spatiotemporal AutoEncoder进行信号重建的WSN采样优化

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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.
机译：本文解决了优化无线传感器网络的传感器部署位置来重建并预测时空信号的问题。传统的传感器部署优化方法仅选择部署位置，并在采样区域提供该功能。因此，如果采样资源受到限制，则部署的传感器可能无法提供感兴趣领域（输入空间）中的所有特征。为了解决这个问题，开发了一个深入的学习框架来从有限的观察结果重建和预测整个时空信号。在开始，所提出的方法优化采样位置以从历史数据中检索用于重建和预测的足够特征。然后，使用时空AutoEncoder用于捕获从原位测量到整个时空信号的非线性映射。使用来自国家海洋和大气管理局的全球气候数据集进行模拟，实施和验证发达的方法。结果表明了所提出的算法进行了显着改进。具体地，与传统方法相比，所提出的方法在重建误差和空间预测稳健性方面提供了卓越的性能。

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