A Framework to Monitor Automotive Emission Through Internet of Things for Predictive Maintenance Toward Air Pollution Reduction

Martin Kuradusenge (), Jean Baptiste Minani, Antoine Gatera (), Faustin Irumva () and Emmanuel Mwumvaneza ()
Additional contact information
Martin Kuradusenge: University of Rwanda, Department of Information Systems, School of ICT, College of Science and Technology
Jean Baptiste Minani: Concordia University, Department of Computer Sciences and Software Engineering
Antoine Gatera: University of Rwanda, Department of Information Systems, School of ICT, College of Science and Technology
Faustin Irumva: University of Rwanda, Department of Computer and Software Engineering, School of ICT, College of Science and Technology
Emmanuel Mwumvaneza: University of Rwanda, Department of Computer and Software Engineering, School of ICT, College of Science and Technology

A chapter in Advancement in Embedded and Mobile Systems, 2026, pp 403-416 from Springer

Abstract: Abstract Air pollution poses a significant threat to global health and the environment, with automobile emissions being a primary contributor. Motor vehicles are among the major sources of urban air pollution. Traditional methods for monitoring vehicular air pollutants, such as Portable Emissions Measurement Systems (PEMS), are limited by their inability to transmit data to the cloud for comprehensive analysis and predictive modeling. The purpose of this study is to develop a framework for real-time monitoring of various pollutants, including carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), volatile organic compounds (VOCs), and particulate matter (PM), using Internet of Things (IoT) sensors. This study aims to leverage the collected data for predictive insights, enabling effective preventive maintenance and mitigation strategies for air pollution control. In this paper, we discuss in detail the framework consisting of seven layers: (i) sensing, (ii) processing and local storage, (iii) edge-based computing, (iv) network, (v) cloud-based storage, (vi) cloud-based computing, (vii) application. As a proof of concept, we implement and discuss an IoT solution using an MQ135 sensor, connected to a cloud server via WiFi or GSM network, where the data is stored and analyzed. Additionally, we integrate machine learning algorithms to predict future pollutant levels and enhance preparedness for preventive maintenance and air pollution reduction. The implementation of this framework is expected to enhance the accuracy of air quality assessments, provide actionable insights for improving urban air quality, and effectively reduce air pollution by monitoring and detecting pollutants early at their source. This enables vehicle owners, regulators, and fleet managers to take appropriate measures based on forecasted insights.

Keywords: Air pollution; Automotive emission; Internet of Things (IoT); Machine Learning (ML) (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-99219-3_27

Ordering information: This item can be ordered from
http://www.springer.com/9783031992193

DOI: 10.1007/978-3-031-99219-3_27

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().