Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.

中文翻译：

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利用5G Edge进行智能，可扩展的空气质量监测

空气污染给社会带来了重大挑战，在全球范围内，空气污染导致每年数百万人过早死亡。空气质量传感设备的大规模部署和对所得数据的数据分析将为实时智能应用和服务的开发铺平道路，例如，在个人或城市范围内将不良空气质量的暴露降至最低。5G和边缘计算通过无处不在的连接，高带宽，高速千兆位连接和超低延迟分析支持高分辨率的密集部署传感器。本文概念化了AI驱动的可扩展空气质量监控，并提出了两种校准低成本空气质量传感器的系统以及对由高光谱相机捕获的图片进行图像处理以更好地检测空气质量的系统。