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Federated learning-based air quality prediction for smart cities using BGRU model

Published: 14 October 2022 Publication History

Abstract

Nowadays, Internet of Things (IoT) has become very popular due to its applications in various fields such as industry, commerce, and education. Cities become smart cities by utilizing lots of applications and services of IoT. However, these intelligent applications and services significantly threaten the environment regarding air pollution. Therefore, high accuracy in air pollution monitoring and future air quality predictions have become our primary concern to save human beings from health issues coming from air pollution. In general, deep learning (DL) and federated learning (FL) techniques are suitable for solving various forecasting problems and dealing with the high volatile air components in heterogeneous big data scenarios. This ambiance of DL and FL motivates us to exploit the DL-based Bidirectional Gated Recurrent Unit (BGRU) method for future air quality prediction using big data and federated learning (FL) to train our model in a distributed, decentral, and secure ways. This paper proposes a novel distributed and decentralized FL-based BGRU model to accurately predict air quality using the smart city's big data. The effectiveness of the FL-based BGRU Model is estimated with other machine learning (ML) models by using various evaluation metrics.

References

[1]
Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek, and H. Vincent Poor, "Scheduling Policies for Federated Learning in Wireless Networks", IEEE Transactions on Communications, vol. 68, no. 1, pp. 317--333, 2020.
[2]
Prateek Chhikara, Rajkumar Tekchandani, Neeraj Kumar, Sudeep Tanwar, and Joel J. P. C. Rodrigues, "Federated Learning for Air Quality Index Prediction using UAV Swarm Networks", 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1--6, 2021.
[3]
https://www.kaggle.com/datasets/fedesoriano/air-quality-data-in-india

Cited By

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  • (2024)The Impact of Federated Learning on Urban ComputingJournal of Internet Services and Applications10.5753/jisa.2024.400615:1(380-409)Online publication date: 21-Sep-2024
  • (2024)Federated Learning: Navigating the Landscape of Collaborative IntelligenceElectronics10.3390/electronics1323474413:23(4744)Online publication date: 30-Nov-2024
  • (2024) Apict: A ir Pollution E pi demiology Using Green AQI Predi ct ion During Winter Seasons in India IEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33439229:3(559-570)Online publication date: May-2024
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cover image ACM Conferences
MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
October 2022
932 pages
ISBN:9781450391818
DOI:10.1145/3495243
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2022

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Author Tags

  1. GRU
  2. IoT
  3. SVR
  4. federated learning (FL)
  5. smart city

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Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Cited By

View all
  • (2024)The Impact of Federated Learning on Urban ComputingJournal of Internet Services and Applications10.5753/jisa.2024.400615:1(380-409)Online publication date: 21-Sep-2024
  • (2024)Federated Learning: Navigating the Landscape of Collaborative IntelligenceElectronics10.3390/electronics1323474413:23(4744)Online publication date: 30-Nov-2024
  • (2024) Apict: A ir Pollution E pi demiology Using Green AQI Predi ct ion During Winter Seasons in India IEEE Transactions on Sustainable Computing10.1109/TSUSC.2023.33439229:3(559-570)Online publication date: May-2024
  • (2024)Urban Air Quality Index Forecasting using Multivariate Convolutional Neural Network based Customized Stacked Long Short-Term Memory ModelProcess Safety and Environmental Protection10.1016/j.psep.2024.08.076Online publication date: Aug-2024
  • (2024)FeLLU: Federated Learning-Based LSU Model for Smart Cities Air Quality ForecastingPollution Control for Clean Environment — Volume 210.1007/978-981-97-7846-1_5(47-56)Online publication date: 2-Dec-2024
  • (2024)Fed-ReST: Federated Learning-Based Recurrent Long Short-Term Memory Model for Smart Cities Air Quality PredictionProceedings of Third International Conference on Advanced Computing and Applications10.1007/978-981-97-4799-3_29(383-395)Online publication date: 23-Dec-2024
  • (2024)GPA: Uni-directional GRU-Based Traffic Prediction Model for Minimizing Air PollutionProceedings of 4th International Conference on Frontiers in Computing and Systems10.1007/978-981-97-2611-0_7(97-107)Online publication date: 29-Jun-2024
  • (2023)Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and AssessmentSustainability10.3390/su15181395115:18(13951)Online publication date: 20-Sep-2023

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