An IoT enabled system for enhanced air quality monitoring and prediction on the edge

Ahmed Samy Moursi, Nawal El-Fishawy, Soufiene Djahel, Marwa Ahmed Shouman

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)
9 Downloads (Pure)

Abstract

Air pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide. Particulate Matter less than 2.5 µm in diameter (PM 2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM 2.5 concentration on both edge devices and the cloud. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). It uses the past 24 h of PM 2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM 2.5. This system was tested on a PC to evaluate cloud prediction and a Raspberry P i to evaluate edge devices’ prediction. Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. The performance of our system was assessed using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), coefficient of determination (R 2), Index of Agreement (IA), and duration in seconds. The obtained results highlighted that NARX/LSTM achieved the highest R 2 and IA and the least RMSE and NRMSE, outperforming other previously proposed deep learning hybrid algorithms. In contrast, NARX/XGBRF achieved the best balance between accuracy and speed on the Raspberry P i.

Original languageEnglish
Pages (from-to)2923-2947
Number of pages25
JournalComplex & Intelligent Systems
Volume7
Issue number6
Early online date29 Jul 2021
DOIs
Publication statusE-pub ahead of print - 29 Jul 2021

Bibliographical note

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Funder


The researcher Ahmed Samy Moursi is funded by a full scholarship “Newton-Mosharafa” from the Ministry of Higher Education of the Arab Republic of Egypt.

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • Air pollution forecast
  • Edge computing
  • IoT
  • Machine learning
  • NARX architecture
  • PM

ASJC Scopus subject areas

  • Computational Mathematics
  • Artificial Intelligence
  • Information Systems
  • Engineering (miscellaneous)

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