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Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors
IEEE Internet of Things Journal ( IF 10.6 ) Pub Date : 2020-06-02 , DOI: 10.1109/jiot.2020.2999446
Tien Huu Do , Evaggelia Tsiligianni , Xuening Qin , Jelle Hofman , Valerio Panzica La Manna , Wilfried Philips , Nikos Deligiannis

Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizens. In this article, we present an IoT pipeline for acquisition, processing, and visualization of air pollution data over the city of Antwerp, Belgium. Our system employs IoT devices mounted on vehicles as well as static reference stations to measure a variety of city parameters, such as humidity, temperature, and air pollution. Mobile measurements cover a larger area compared to static stations; however, there is a tradeoff between temporal and spatial resolution. We address this problem as a matrix completion on graphs problem and rely on variational graph autoencoders to propose a deep learning solution for the estimation of the unknown air pollution values. Our model is extended to capture the correlation among different air pollutants, leading to improved estimation. We conduct experiments at different spatial and temporal resolution and compare with state-of-the-art methods to show the efficiency of our approach. The observed and estimated air pollution values can be accessed by interested users through a Web visualization tool designed to provide an air pollution map of the city of Antwerp.

中文翻译:

来自移动物联网传感器的基于图深度学习的细颗粒空气质量推断

物联网(IoT)技术结合了多种不同的传感设备和通信技术,可为各种应用程序收集大量数据。智慧城市利用物联网基础设施来构建对城市和公民管理有用的服务。在本文中,我们介绍了一个物联网管道,用于比利时安特卫普市的空气污染数据的采集,处理和可视化。我们的系统采用安装在车辆上的IoT设备以及静态参考站来测量各种城市参数,例如湿度,温度和空气污染。与静态站点相比,移动设备的测量范围更大。但是,在时间和空间分辨率之间需要权衡。我们将这个问题作为图问题的矩阵完成问题来解决,并依靠变分图自动编码器为估计未知空气污染值提出深度学习解决方案。我们的模型被扩展以捕获不同空气污染物之间的相关性,从而改善了估算。我们以不同的时空分辨率进行实验,并与最先进的方法进行比较,以证明我们方法的有效性。感兴趣的用户可以通过Web可视化工具访问观测和估计的空气污染值,该Web可视化工具旨在提供安特卫普市的空气污染图。导致改进的估算。我们以不同的时空分辨率进行实验,并与最先进的方法进行比较,以证明我们方法的有效性。感兴趣的用户可以通过Web可视化工具访问观测和估计的空气污染值,该Web可视化工具旨在提供安特卫普市的空气污染图。导致改进的估算。我们以不同的时空分辨率进行实验,并与最先进的方法进行比较,以证明我们方法的有效性。感兴趣的用户可以通过Web可视化工具访问观测和估计的空气污染值,该Web可视化工具旨在提供安特卫普市的空气污染图。
更新日期:2020-06-02
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