Optimizing Edge Computing Architectures for Low-Latency IoT Applications: A Systematic Review
Keywords:
Edge Computing, Low-Latency IoT, Task Offloading, Edge AI, Resource Orchestration, 5G/6G Integration, Systematic ReviewAbstract
Edge computing has become crucial for addressing the demanding low-latency needs of contemporary Internet of Things (IoT) applications, including autonomous vehicles, industrial automation, smart healthcare, and real-time environmental monitoring. By processing data nearer to its origin, edge architectures greatly decrease the round-trip times to centralized clouds, reduce bandwidth consumption, and improve privacy and reliability. This systematic review explores cutting-edge edge computing architectures tailored for low-latency IoT, synthesizing insights from over 120 studies conducted between 2020 and 2026. We examine layered models (device-edge-cloud continuum), essential optimization strategies (task offloading, resource orchestration, AI-driven inference), and performance indicators (latency, throughput, energy efficiency). Key challenges encompass heterogeneity, scalability, security, and dynamic environments. Emerging solutions incorporate 5G/6G integration, Edge AI (TinyML), federated learning, and serverless paradigms. Comparative assessments reveal that hybrid edge-cloud systems can achieve end-to-end latency below 10ms in numerous scenarios, surpassing traditional cloud-only setups by 40-70%. The review highlights research gaps in sustainability, standardization, and large-scale real-world deployment. We propose a reference architecture and roadmap for future resilient, intelligent edge systems. This work acts as a comprehensive resource for researchers and practitioners involved in designing next-generation IoT infrastructures.
References
1. Ali, M., Riaz, N., Ashraf, M. I., Qaisar, S., & Naeem, M. (2018). Joint Cloudlet Selection and Latency Minimization in Fog Networks. IEEE Transactions on Industrial Informatics, 14(9), 4055–4063. https://doi.org/10.1109/tii.2018.2829751
2. Al-Araji, Z. J., Ahmad, S. S. S., Kausar, N., Farhani, A., Ozbilge, E., & Cagin, T. (2022). Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues. IEEE Access, 10, 126931–126956. https://doi.org/10.1109/access.2022.3225462
3. Kuchuk, H., & Malokhvii, E. (2024). INTEGRATION OF IOT WITH CLOUD, FOG, AND EDGE COMPUTING: A REVIEW. Advanced Information Systems, 8(2), 65–78. https://doi.org/10.20998/2522-9052.2024.2.08
4. Burhan, M., Alam, H., Arsalan, A., Rehman, R. A., Anwar, M., Faheem, M., & Ashraf, M. W. (2023). A Comprehensive Survey on the Cooperation of Fog Computing Paradigm-Based IoT Applications: Layered Architecture, Real-Time Security Issues, and Solutions. IEEE Access, 11, 73303–73329. https://doi.org/10.1109/access.2023.3294479
5. Wang, S. (2019). Edge Computing: Applications, State-of-the-Art and Challenges. Advances in Networks, 7(1), 8. https://doi.org/10.11648/j.net.20190701.12
6. Abdulazeez, A. H., & Abdulazeez, A. M. (2024). Integration of Machine Learning with Fog Computing for Health Care Systems Challenges and Issues: A Review. Indonesian Journal of Computer Science, 13(3). https://doi.org/10.33022/ijcs.v13i3.3986
7. Khairosheva, K. B. K., Sapina, L., & Imanbayeva, A. K. (2025). Comparative analysis of cloud and fog computing performance based on modeling. Recent Contributions to Physics, 92(1), 120–127. https://doi.org/10.26577/rcph202592113
8. Fernández-Cerero, D., Fernández-Rodríguez, J. Y., Álvarez-García, J. A., Soria-Morillo, L. M., & Fernández-Montes, A. (2019). Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things. Sensors (Basel, Switzerland), 19(13), 3026. https://doi.org/10.3390/s19133026
9. Sharma, P. K., Chen, M.-Y., & Park, J. H. (2018). A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT. IEEE Access, 6, 115–124. https://doi.org/10.1109/access.2017.2757955
10. Lee, E.-S., & Shin, B.-S. (2023). Enhancing the Performance of XR Environments Using Fog and Cloud Computing. Applied Sciences, 13(22), 12477. https://doi.org/10.3390/app132212477
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Fundamental Research & Development
This work is licensed under a Creative Commons Attribution 4.0 International License.
