Air Quality Prediction and Multi-Task Offloading based on Deep Learning Methods in Edge Computing-Reference-Cited by-同舟云学术

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Air Quality Prediction and Multi-Task Offloading based on Deep Learning Methods in Edge Computing

Published:2023-06 Issue:2 Volume:21 Page:
ISSN:1570-7873
Container-title:Journal of Grid Computing
language:en
Short-container-title:J Grid Computing

Author:

Sun Changyuan,Li Jingjing,Sulaiman Riza,Alotaibi Badr S.,Elattar Samia,Abuhussain Mohammed

Funder

Princess Nourah Bint Abdulrahman University

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10723-023-09671-0.pdf

Reference59 articles.

1. Wardana, I NyomanKusama., Gardner, Julian W., Fahmy, Suhaib A.: Optimising deep learning at the edge for accurate hourly air quality prediction. Sensors 21(4), 1064 (2021)

2. Mu, Liting, et al.: Multi-Task Offloading Based on Optimal Stopping Theory in Edge Computing Empowered Internet of Vehicles. Entropy 24(6), 814 (2022)

3. Alkabbani, Hanin, et al.: An improved air quality index machine learning-based forecasting with multivariate data imputation approach. Atmosphere 13(7), 1144 (2022)

4. Chen, H., Guan, M., Li, H.: Air quality prediction based on integrated dual LSTM model. IEEE Access 9, 93285–93297 (2021)

5. Islam, N., Saikia, B.K.: Atmospheric particulate matter and potentially hazardous compounds around residential/road side soil in an urban area. Chemosphere 259, 127453 (2020)

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