University of Oulu

X. Su et al., "Intelligent and Scalable Air Quality Monitoring With 5G Edge," in IEEE Internet Computing, vol. 25, no. 2, pp. 35-44, 1 March-April 2021, doi: 10.1109/MIC.2021.3059189

Intelligent and scalable air quality monitoring with 5G edge

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Author: Su, Xiang1,2; Liu, Xiaoli1; Motlagh, Naser Hossein1;
Organizations: 1University of Helsinki, Helsinki, Finland
2University of Oulu, Oulu, Finland
3Wuhan University, Wuhan, China
4Beijing University of Chemical Technology, Beijing, China
5The Hong Kong University of Science and Technology, Hong Kong
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-14


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.

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Series: IEEE Internet computing
ISSN: 1089-7801
ISSN-E: 1941-0131
ISSN-L: 1089-7801
Volume: 25
Issue: 2
Pages: 35 - 44
Article number: 9354565
DOI: 10.1109/MIC.2021.3059189
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work is partially funded by Academy of Finland, grant numbers 3196669, 319670, 325774, 326305, 325570, 324576 and 335934. This work is partially supported by the Helsinki Center for Data Science (HiDATA) program, the European Union through the Urban Innovative Action Healthy Outdoor Premises for Everyone (UIA03-240), Business Finland Project 6884/31/2018 MegaSense Smart City, and project 16214817 from the Research Grants Council of Hong Kong. This work is also partially funded by the Ministry of Science and Technology of the People’s Republic of China (2019YFC0214701), the National Natural Science Foundation of China (41877306) and Beijing University of Chemical Technology. The authors wish to acknowledge CSC–IT Center for Science, Finland, for computational resources.
Academy of Finland Grant Number: 324576
Detailed Information: 324576 (Academy of Finland Funding decision)
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