Fog Computing Integration for Real-Time Iot Data Processing

Zahraa Kadhim Alitbi and Seyed Amin Hosseini Seno

References:

1. S. Hamdan, M. Ayyash, and S. Almajali, 2020, “Edgecomputing architectures for internet of things applications: A survey,” Sensors, vol. 20, no. 22, p. 6441.
2. F. Alenizi and O. Rana, 2021, “Dynamically controlling offloading thresholds in fog systems,” Sensors, vol. 21, no. 7, p. 2512.
3. Y.-A. Daraghmi, E. Y. Daraghmi, R. Daraghma, H. Fouchal, and M. Ayaida, 2022, “Edge–fog–cloud computing hierarchy for improving performance and security of NB-IoT-based health monitoring systems,” Sensors, vol. 22, no. 22, p. 8646.
4. U. Vadde and V. S. Kompalli, 2022, “Energy efficient service placement in fog computing,” PeerJ Computer Science, vol. 8, p. e1035.
5. A. Alatoun, H. Otrok, R. Mizouni, and J. Bentahar, 2022, “A novel low-latency and energy-efficient task scheduling framework for internet of medical things in an edge-fog-cloud system,” Sensors, vol. 22, no. 14, p. 5327.
6. A. Gupta, S. K. Gupta, and P. R. Gautam, 2025, “Dynamic task allocation in fog computing using enhanced fuzzy logic approaches,” Scientific Reports, vol. 15, p. 25121.
7. D. S. N. K. P. Ali Kumar and P. K. Sahu, 2022, “Green demand-aware fog computing: A prediction-based framework,” Electronics, vol. 11, no. 4, p. 608.
8. K. Oliullah, M. Whaiduzzaman, M. J. N. Mahi, T. Jan, and A. Barros, 2025, “A machine learning based authentication and intrusion detection scheme for IoT users anonymity preservation in fog environment,” PLOS ONE, vol. 20, no. 6, p. e0323954.
9. H. M. Ali, A. B. Bomgni, S. A. C. Bukhari, T. Hameed, and J. Liu, 2023, “Power-aware fog supported IoT network for healthcare infrastructure using swarm intelligence-based algorithms,” Mobile Networks and Applications, vol. 28, pp. 824–838.
10. S. H. Alsamhi, O. Ma, M. S. Ansari, and N. S. Rajput, 2021, “Toward IoT fog computing-enabled system energy consumption modeling and optimization by adaptive TCP/IP protocol,” PeerJ Computer Science, vol. 7, p. e673.
11. A. B. M. Monjur et al., 2023, “An overview of fog data analytics for IoT applications,” Sensors, vol. 23, no. 1, p. 199.
12. P. R. Kumar and S. Goel, 2025, “A secure and efficient encryption system based on adaptive and machine learning for securing data in fog computing,” Scientific Reports, vol. 15, p. 11654.
13. M. T. Islam, M. A. Razzaque et al., 2020, “Fog computing at industrial level, architecture, latency, energy, and security: A review,” Heliyon, vol. 6, no. 4, p. e03712.
14. S. K. Routray, S. Ramasubbareddy, and P. K. Jana, 2023, “A comprehensive survey on resource allocation strategies in fog/cloud environments,” Sensors, vol. 23, no. 11, p. 4974.
15. M. N. Najeeb, H. R. Bhatnagar, and S. Kumar, 2025, “A hybrid fog-edge computing architecture for real-time health monitoring in IoMT systems with optimized latency and threat resilience,” Scientific Reports, vol. 15, p. 16487.
16. M. Hasan, M. A. Razzaque, and M. M. Alam, 2025, “Securing fog computing in healthcare with a zero-trust approach and blockchain,” EURASIP Journal on Wireless Communications and Networking, p. 14.
17. J. Bhatia, K. Italiya, K. Jadeja, M. Kumhar, U. Chauhan, S. Tanwar, M. Bhavsar, R. Sharma, D. L. Manea, M. Verdes, and M. S. Raboaca, 2022, “An overview of fog data analytics for IoT applications,” Sensors (Basel), vol. 23, no. 1, p. 199.