AI-Based Optimization of Edge Computing Systems for Real-Time Intelligent Applications

Authors

  • Cailin Bohan Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA. Author
  • Orlaith McMullen Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA. Author
  • Tuva Haraldsson Department of Computer Science, University of Houston, Houston, TX, USA. Author

Keywords:

edge computing; edge intelligence; real-time systems; resource orchestration; federated learning; sustainable infrastructure; governance; robustness

Abstract

Edge computing has emerged as a pivotal architectural response to the latency, bandwidth, and governance constraints of centralized cloud intelligence. This paper examines artificial intelligence based optimization of edge computing systems for real-time intelligent applications. It argues that optimization cannot be reduced to model compression or task offloading; rather, it must be understood as a socio-technical orchestration problem involving heterogeneous infrastructure, dynamic workloads, data protection, energy accounting, and multi-stakeholder governance. The discussion synthesizes research on edge intelligence, federated learning, multi-access edge computing, distributed machine learning, and secure aggregation. It analyzes structural trade-offs among latency, accuracy, energy, privacy, fairness, and resilience, and considers how artificial intelligence can assist resource allocation, placement, scheduling, and failure management. The paper also evaluates deployment barriers in urban, industrial, healthcare, and transportation settings. Particular attention is given to sustainability, robustness under network volatility, and policy implications for interoperability and accountability. The conclusion suggests that future edge systems require adaptive, uncertainty-aware optimization frameworks that treat fairness and environmental cost as first-class constraints rather than post hoc corrections.

References

1. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30-39. https://doi.org/10.1109/MC.2017.9

2. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637-646. https://doi.org/10.1109/JIOT.2016.2579198

3. Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322-2358. https://doi.org/10.1109/COMST.2017.2745201

4. Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450-465. https://doi.org/10.1109/JIOT.2017.2750180

5. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762. https://doi.org/10.1109/JPROC.2019.2918951

6. Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020). Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22(2), 869-904. https://doi.org/10.1109/COMST.2020.2970550

7. Deng, S., Zhao, H., Fang, W., Yin, J., Dustdar, S., & Zomaya, A. Y. (2020). Edge intelligence: The confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal, 7(8), 7457-7469. https://doi.org/10.1109/JIOT.2020.2984887

8. Chen, J., & Ran, X. (2019). Deep learning with edge computing: A review. Proceedings of the IEEE, 107(8), 1655-1674. https://doi.org/10.1109/JPROC.2019.2921977

9. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175-1191. https://doi.org/10.1145/3133956.3133982

10. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60. https://doi.org/10.1109/MSP.2020.2975749

11. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 1273-1282. https://proceedings.mlr.press/v54/mcmahan17a.html

12. Zhang, C., Patras, P., & Haddadi, H. (2019). Deep learning in mobile and wireless networking: A survey. IEEE Communications Surveys & Tutorials, 21(3), 2224-2287. https://doi.org/10.1109/COMST.2019.2904897

13. Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., & Sabella, D. (2017). On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials, 19(3), 1657-1681. https://doi.org/10.1109/COMST.2017.2705720

14. Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 13-16. https://doi.org/10.1145/2342509.2342513

15. Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680-698. https://doi.org/10.1016/j.future.2016.11.009

16. Xu, J., Wang, S., Bhargava, B. K., & Yang, F. (2019). A blockchain-enabled trustless crowd-intelligence ecosystem on mobile edge computing. IEEE Transactions on Industrial Informatics, 15(6), 3538-3547. https://doi.org/10.1109/TII.2018.2879704

17. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), Article 12. https://doi.org/10.1145/3298981

18. Murshed, M. G. S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., & Hussain, F. (2021). Machine learning at the network edge: A survey. ACM Computing Surveys, 54(8), Article 170. https://doi.org/10.1145/3469029

19. Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2020). A survey on distributed machine learning. ACM Computing Surveys, 53(2), Article 30. https://doi.org/10.1145/3377454

20. Park, J., Samarakoon, S., Bennis, M., & Debbah, M. (2019). Wireless network intelligence at the edge. Proceedings of the IEEE, 107(11), 2204-2239. https://doi.org/10.1109/JPROC.2019.2941458

Downloads

Published

2026-07-10

Issue

Vol. 1 No. 1 (2026): Computational Intelligence Systems

Section

Articles

License

Copyright (c) 2026 Computational Intelligence Systems

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.