Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Satheesh Abimannan*, El Sayed M. El-Alfy, Shahid Hussain*, Yue Shan Chang, Saurabh Shukla, Dhivyadharsini Satheesh, John G. Breslin

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations

Abstract

Systems for monitoring air quality are essential for reducing the negative consequences of air pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. This paper critically reviews the state-of-the-art methodologies for federated learning and MEC-enabled air quality monitoring systems. It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications. Additionally, it highlights the challenges and requirements for developing and implementing real-time air quality monitoring systems, such as data quality, security, and privacy, as well as the need for interpretable and explainable AI-powered models. By leveraging such advanced techniques and technologies, air monitoring systems can overcome various challenges and deliver accurate, reliable, and timely air quality predictions. Moreover, this article provides an in-depth analysis and assessment of the state-of-the-art techniques and emphasizes the need for further research to develop more practical and affordable AI-powered decentralized systems with improved performance and data quality and security while ensuring the ethical and responsible use of the data to support informed decision making and promote sustainability.

Original languageEnglish
Article number13951
JournalSustainability
Volume15
Issue number18
DOIs
StatePublished - Sep 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • air quality monitoring
  • climate change
  • federated learning
  • multi-access edge computing
  • privacy-preserving methods
  • sustainable urban environments

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law

Fingerprint

Dive into the research topics of 'Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment'. Together they form a unique fingerprint.

Cite this