ESR-GAN: Environmental Signal Reconstruction Learning With Generative Adversarial Network,IEEE Internet of Things Journal

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ESR-GAN: Environmental Signal Reconstruction Learning With Generative Adversarial Network
IEEE Internet of Things Journal ( IF 10.6 ) Pub Date : 2020-08-21 , DOI: 10.1109/jiot.2020.3018621
Xu Kang , Liang Liu , Huadong Ma

Monitoring the status of urban environmental phenomenon, which provides fundamental sensory information, is of great significance for various field of urban research. In this article, we propose a new framework, environmental signal reconstruction generative adversarial network, for reconstructing high-quality environmental signal via sensory data from sparsely distributed monitoring sites. Our framework is based on the generative adversarial network (GAN), in which a three-layer convolutional neural network (CNN)-based generative model is proposed to learn an end-to-end mapping between low- and high-quality signals and a discriminative model is introduced for quantizing the reconstruction accuracy. Considering the scattered distribution of sensory data, we further propose a metric called impact map for building loss function and guiding the adversarial training. Experiments with real-world air quality data of Beijing demonstrate that our method outperforms the state-of-the-art data inference techniques in terms of signal recovery accuracy.

更新日期：2020-08-21

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