Hybrid Edge–Cloud Collaboration for Addressing Bandwidth Limitations and Latency in IoT Applications
DOI:
https://doi.org/10.63561/jca.v2i4.1079Keywords:
Edge–Cloud Collaboration, Internet of Things (IoT), Latency Reduction, Bandwidth Optimization, Hybrid Computing ArchitectureAbstract
The exponential growth of Internet of Things (IoT) ecosystems has intensified the demand for processing frameworks that can manage vast and continuous data flows with low latency and high efficiency. Conventional cloud-centric architectures, while offering scalable storage and computational power, are hindered by inherent limitations such as network congestion, high latency, and bandwidth bottlenecks. These constraints pose significant challenges for real-time and mission-critical IoT applications, including healthcare monitoring, autonomous vehicles, and industrial automation, where timely decision-making is paramount. To address these issues, Edge–Cloud collaboration has emerged as a hybridized computing paradigm that integrates the complementary strengths of edge and cloud layers. In this model, edge nodes perform immediate, latency-sensitive computations near the data source, ensuring rapid responsiveness, while more complex, resource-intensive tasks are delegated to the cloud for large-scale analysis, storage, and global optimization. This collaborative approach not only reduces the volume of raw data transmitted across networks, thereby alleviating bandwidth constraints, but also enhances scalability, reliability, and adaptability of IoT infrastructures. Despite its advantages, challenges such as dynamic task partitioning, interoperability, energy efficiency, and data security remain central to its successful implementation. This paper critically analyses the architecture, benefits, and limitations of Edge–Cloud collaboration, with a specific focus on its effectiveness in mitigating latency and bandwidth constraints. It also examines the applicability of this hybrid computing model across various IoT domains where distributed processing has become essential. The paper concludes with key insights and offers practical recommendations for practitioners seeking to optimize Edge–Cloud deployments.
References
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
Alharbi, H. A., Aldossary, M., Almutairi, J., & Elgendy, I. A. (2023). Energy-aware and secure ask offloading for multi tier edge cloud computing systems. Sensors, 23(6), 3254. https://doi.org/10.3390/s23063254
Bi, X., & Zhao, L. (2024). Two-layer edge intelligence for task offloading and computing capacity allocation with UAV assistance in vehicular networks. Sensors, 24(6), Article 1863. https://doi.org/10.3390/s24061863
Birhanie, H. M., & Adem, M. O. (2024). Optimized task offloading strategy in IoT edge computing network. Journal of King Saud University – Computer and Information Sciences, 36(2), Article 101942. https://doi.org/10.1016/j.jksuci.2024.101942
Chen, H., Qin, W., & Wang, L. (2022). Task partitioning and offloading in IoT cloud–edge collaborative computing framework: A survey. Journal of Cloud Computing: Advances, Systems and Applications, 11, Article 86. https://doi.org/10.1186/s13677-022-00365-8
Chen, J., Leng, Y., & Huang, J. (2023). An intelligent approach of task offloading for dependent
services in Mobile Edge Computing. Journal of Cloud Computing: Advances, Systems and Applications, 12(1), Article 107. https://doi.org/10.1186/s13677-023-00477-9
Chen, X., Zhou, Z., Guan, G., & Li, Y. (2022). Deep reinforcement learning–based offloading and resource allocation in edge cloud computing systems. Journal of Network and Computer Applications, 221, 102996.
Daruvuri, R., & Patibandla, K. K. (2023). Enhancing data security and privacy in edge computing: A comprehensive review of key technologies and future directions. International Journal of Research in Electronics and Computer Engineering, 11(1), 77–88.
Ferreira, R., Ranaweera, C., Lee, K., & Schneider, J.-G. (2023). Energy efficient node selection in edge-fog-cloud layered IoT architecture. Sensors, 23(13), 6039. https://doi.org/10.3390/s23136039
Hossain, M. I., Sumon, S. A., Hasan, H. M., Akter, F., Badhon, M. B., & Islam, M. N. U. (2024).
Quantum‑Edge Cloud Computing: A Future Paradigm for IoT Applications. arXiv preprint arXiv:2405.04824. https://doi.org/10.48550/arXiv.2405.04824
Liu, Y., Zhou, Z., Chen, X., Zhang, J., & Xu, K. (2023). Optimizing task offloading and resource allocation in edge–cloud networks: A deep reinforcement learning approach. Journal of Cloud Computing, 12, 1–17. https://doi.org/10.1186/s13677-023-00461-3
Luo, Z., Wang, S., Chen, X., & Lu, Y. (2022). A deep-learning-based collaborative edge–cloud
telemedicine system for retinopathy of prematurity screening. Sensors, 23(1), Article 276.
Mao, Y., Zhang, J., & Letaief, K. B. (2017). Resource allocation for mobile edge computation
offloading with reinforcement learning. IEEE Transactions on Wireless Communications, 17(7), 4958–4972.
Puligundla, N., Gangappa, M., Rajasekar, M., Sunil Kumar, T., & Reddy, G. S. (2024). Resource
allocation for content distribution in IoT edge cloud computing environments using deep reinforcement learning. Journal of High Speed Networks, 3, 409–426. https://doi.org/10.3233/JHS-230165
Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39.
https://doi.org/10.1109/MC.2017.9
Statista. (2024). Number of IoT connected devices worldwide 2019–2030.
https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/
Ullah, I., Lim, H.-K., Seok, Y.-J., & Han, Y.-H. (2023). Optimizing task offloading and resource allocation in edge-cloud networks: A DRL approach. Journal of Cloud Computing: Advances, Systems and Applications, 12, Article 112. https://doi.org/10.1186/s13677-023-00461-3
Yang, C., Wang, Y., Lan, S., Wang, L., Shen, W., Huang, G.Q.(2022). Cloud-edge-device collaboration mechanisms of deep learning models for smart robots in mass personalization. Robotics and Computer-Integrated Manufacturing, Volume 77,102351. ISSN 0736-5845. https://doi.org/10.1016/j.rcim.2022.102351
Yu, H., Chen, X., Xu, J., & Li, Z. (2023). Joint DNN partitioning and task offloading
in mobile edge computing via deep reinforcement learning. Journal of Cloud Computing, 12, 1–21. https://doi.org/10.1186/s13677-023-00493-9
Yuan, J., Xiao, H., Shen, Z., Zhang, T., & Jin, J. (2023). ELECT: Energy-efficient intelligent
edge–cloud collaboration for remote IoT services. Future Generation Computer Systems, 147, 179–194. https://doi.org/10.1016/j.future.2023.04.030
Zhang, X., Li, Y., & Wang, J. (2020). Research on new edge computing network
architecture and task offloading strategy for Internet of Things. Wireless Networks, 30, 3619–3631. https://doi.org/10.1007/s11276-020-02516-8
Zhang, W., Huang, L., Wang, H., & Ren, Y. (2024). Task partition-based computation
offloading and content caching for cloud–edge cooperation networks. Symmetry, 16(7), 906. https://doi.org/10.3390/sym16070906
