Intelligent and Scalable Air Quality Monitoring With 5G Edge
Xiang Su, Xiaoli Liu, Naser Hossein Motlagh, Jacky Cao, Peifeng Su, Petri Pellikka, Yongchun Liu, Tuukka Petäjä, Markku Kulmala, Pan Hui, Sasu Tarkoma
Abstract
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.