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 and John G. Breslin
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Satheesh Abimannan: Amity School of Engineering and Technology, Amity University Maharashtra, Mumbai 410206, India
El-Sayed M. El-Alfy: Fellow SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Interdisciplinary Research Center of Intelligent Secure Systems, Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Shahid Hussain: Innovation Value Institute (IVI), School of Business, National University of Ireland Maynooth (NUIM), W23 F2H6 Maynooth, Ireland
Yue-Shan Chang: Department of Computer Science and Information Engineering, National Taipei University, Taipei 10608, Taiwan
Saurabh Shukla: Department of Computer Science (CS), Indian Institute of Information Technology, Lucknow (IIIT L), Lucknow 226002, India
Dhivyadharsini Satheesh: School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore 632014, India
John G. Breslin: Department of Electrical and Electronic Engineering, Data Science Institute, University of Galway, H91 TK33 Galway, Ireland

Sustainability, 2023, vol. 15, issue 18, 1-34

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.

Keywords: federated learning; multi-access edge computing; air quality monitoring; climate change; privacy-preserving methods; sustainable urban environments (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
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