[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 4, Issue 9 (12-2025) ::
3 2025, 4(9): 1-8 Back to browse issues page
Fog computing and its applications in distributed artificial intelligence
Alireza Dashtifamil , Ali Ezzati
Abstract:   (64 Views)
Digital health systems have introduced fog computing as a key solution in distributed environments. This paper, focusing on reinforcement learning (RL) algorithms and multi-objective optimization, reviews novel approaches to resource allocation and task scheduling in fog-based architectures. The performance of algorithms such as Q-Learning, DDPG, and PPO in reducing latency and energy consumption is analyzed, and their combination with evolutionary methods (such as NSGA-II) is introduced as an effective solution to solve resource conflict challenges. Also, key issues of scalability, stability in unstable environments, privacy preservation, and explainability of models are discussed. Future perspectives including the development of federated learning frameworks, self-regulating learning (Meta-RL), and integration with renewable energy sources are proposed as essential steps for designing smart and sustainable systems. This study provides a framework for designing optimal and reliable solutions in future distributed architectures.
 
Keywords: Fog computing, distributed artificial intelligence, resource allocation, reinforcement learning, multi-criteria optimization
Full-Text [PDF 381 kb]   (49 Downloads)    
Type of Study: Research | Subject: Special
Received: 2025/05/18 | Accepted: 2025/12/22 | Published: 2025/12/22
References
1. A. P. F. Araujo, J. Bachiega Jr., L. R. de Carvalho, and A. P. F. Araujo, “Computational Resource Allocation in Fog Computing: A Comprehensive Survey,” ACM Computing Surveys, vol. 55, no. 14s, Mar. 2023.
2. S. Duan, D. Wang, J. Ren, F. Lyu, Y. Zhang, H. Wu, and X. Shen, “Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey,” IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 591–624, Jan. 2022.
3. M. M. Shirmohammadi and M. Chahhardi, “ESEP: A Stable Cluster Head Selection Protocol in Heterogeneous Wireless Sensor Networks with Sleep and Wake Modes,” in *Proceedings of the Computer, Electrical and Information Technology Conference*, Islamic Azad University, Hamadan Branch, Hamadan, vol. 1, 2007
4. M. M. Shirmohammadi, M. Chahardoli, and H. Zargari, Introduction to Multimedia. Hamadan: Daneshjo, 2011. ISBN: 971-964-543-104-2.
5. M. M. Shirmohammadi, AI Unboxed: Tools & Techniques for the Future, 1st ed., vol. 1. [Online]. Available: https://www.researchgate.net/publication/389659219_AI_UNBOXED_TOOLS_TECHNIQUES_FOR_THE_FUTURE#fullTextFileContent, Mar. 2025, p. 144.
6. Y. Zhang, L. Chen, and W. Wang, “Hybrid Fog-Cloud Architectures for Distributed Deep Learning,” IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 145–160, 2023.
7. X. Chen and H. Zhang, “Privacy-Preserving Federated Learning in Fog Environments,” ACM SIGCOMM Computer Communication Review, vol. 53, no. 4, pp. 78–92, 2022
8. R. Wang et al., “Q-Learning-Based Resource Allocation for Fog-Assisted AI Systems,” Future Generation Computer Systems, vol. 135, pp. 300–315, 2023
9. S. Kumar et al., “Adversarial Robustness in Fog-Based Distributed AI Systems,” IEEE Internet of Things Journal, vol. 13, no. 1, pp. 45–59, 2024
10. A. Gupta et al., “Fog Computing for Smart City Applications: A Comprehensive Review,” IEEE Access, vol. 11, pp. 10245–10267, 2023
11. J. Li and Q. Wang, “Secure Medical Data Analytics Using Federated Learning in Fog Computing,” Nature Communications, vol. 13, no. 1, p. 6542, 2022
12. Y. Zhang, M. Chen, Y. Li, and L. Wang, “A Q-learning-based dynamic task offloading algorithm for mobile edge computing,” IEEE Access, vol. 8, pp. 13979–13989, 2020
13. L. Chen, T. Jiang, and Y. Zhu, “Resource allocation for edge computing using dueling DQN,” IEEE Internet Things Journal, vol. 8, no. 3, pp. 1876–1886, Feb. 2021
14. W. Wang, B. Li, and H. Zhang, “Energy-efficient resource management with double deep Q-network in vehicular edge computing,” IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1505–1519, 2022
15. M. M. Shirmohammadi, M. Chahardoli, Wireless Sensor Networks. Hamadan: Islamic Azad University, Hamadan Branch, 2012. ISBN: 978-964-543-103-5
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Dashtifamil A, Ezzati A. Fog computing and its applications in distributed artificial intelligence. 3 2025; 4 (9) :1-8
URL: http://jiis.iauh.ac.ir/article-1-43-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 4, Issue 9 (12-2025) Back to browse issues page
فصلنامه سیستم های اطلاعاتی هوشمند Intelligent Information Systems Journal
Persian site map - English site map - Created in 0.05 seconds with 35 queries by YEKTAWEB 4735