Optimizing Computational Efficiency in IoT Ecosystems Using Hybrid Edge-Cloud Offloading and Adaptive Learning Models

Authors

  • Uma Maheshwari Hindusthan Institute of Technology, Coimbatore, India.
  • Yusuf Ceylan Selçuk University
  • Seher Polat Sakarya University, Sakarya-Turkiye
  • Nurdan Karpuz Amasya University, Amasya-Turkiye
  • Osman Gunay Yıldız Technical University, Istanbul-Turkiye
  • Mucize Sarıhan Istanbul Okan University, Istanbul-Turkiye
  • Feride Kulalı Istanbul Uskudar University, Istanbul-Turkiye
  • Sabiha Anas Boussaa CRTSE-Algeria

DOI:

https://doi.org/10.22399/ijcesen.1827

Keywords:

IoT, Hybrid Edge-Cloud Offloading, Adaptive Learning, Deep Q-Networks, Reinforcement Learning, Network Latency

Abstract

This research presents an innovative framework to enhance computational efficiency in Internet of Things (IoT) ecosystems by leveraging a hybrid edge-cloud offloading mechanism integrated with adaptive learning models. The proposed system dynamically selects offloading decisions by considering factors such as task complexity, network conditions, and device capabilities. An adaptive learning algorithm, utilizing Reinforcement Learning (RL) and Deep Q-Networks (DQN), optimizes task allocation between edge and cloud servers. Experimental results demonstrate a 32.5% reduction in task completion time, a 28.9% improvement in energy consumption efficiency, and a 24.7% increase in resource utilization compared to traditional offloading models. Furthermore, the framework ensures a 19.3% reduction in network latency under high-load scenarios, making it ideal for real-time IoT applications. The proposed system contributes to enhancing computational efficiency while ensuring seamless task management and improved Quality of Service (QoS) in IoT environments.

References

[1] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. DOI: https://doi.org/10.1016/j.future.2013.01.010

[2] Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009). The case for cloudlets: Challenges and opportunities. IEEE Pervasive Computing, 8(4), 14-23. DOI: https://doi.org/10.1109/MPRV.2009.82

[3] Zhang, W., Li, X., & Chen, Y. (2021). Adaptive task offloading in edge-cloud systems for IoT applications. IEEE Transactions on Industrial Informatics, 17(5), 3224-3234.

[4] Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628-1656. DOI: https://doi.org/10.1109/COMST.2017.2682318

[5] Chen, X., Jiao, L., Li, W., & Fu, X. (2015). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 24(5), 2795-2808. DOI: https://doi.org/10.1109/TNET.2015.2487344

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

[7] Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., & Wang, W. (2017). A survey on mobile edge networks: Convergence of computing, caching, and communications. IEEE Access, 5, 6757-6779. DOI: https://doi.org/10.1109/ACCESS.2017.2685434

[8] Huang, C., Hu, Q., Guo, L., & Zhang, X. (2020). Deep Q-network-based task offloading for edge computing. IEEE Internet of Things Journal, 7(8), 7252-7263.

[9] Guo, S., Guo, S., Sun, D., & Liu, X. (2019). Task offloading in edge computing systems with energy-aware models. IEEE Transactions on Cloud Computing, 8(4), 1101-1113.

[10] Liu, C., Yang, Y., Chen, J., & Jiang, T. (2020). Deep reinforcement learning for task offloading and resource allocation in edge computing systems. IEEE Transactions on Wireless Communications, 19(8), 5330-5344.

[11] "Prelims", Sood, K., Dhanaraj, R.K., Balusamy, B., Grima, S. and Uma Maheshwari, R. (Ed.) (2022), Big Data: A Game Changer for Insurance Industry (Emerald Studies in Finance, Insurance, and Risk Management), Emerald Publishing Limited, Leeds, pp. i-xxiii. https://doi.org/10.1108/978-1-80262-605-620221020 DOI: https://doi.org/10.1108/978-1-80262-605-620221020

[12] Janarthanan, R.; Maheshwari, R.U.; Shukla, P.K.; Shukla, P.K.; Mirjalili, S.; Kumar, M. (2021) Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems. Energies, 14, 6584. https://doi.org/10.3390/en14206584 DOI: https://doi.org/10.3390/en14206584

[13] Maheshwari, R.U., Kumarganesh, S., K V M, S. et al. (2024). Advanced Plasmonic Resonance-enhanced Biosensor for Comprehensive Real-time Detection and Analysis of Deepfake Content. Plasmonics https://doi.org/10.1007/s11468-024-02407-0 DOI: https://doi.org/10.1007/s11468-024-02407-0

[14] Appalaraju, M., Sivaraman, A.K., Vincent, R., Ilakiyaselvan, N., Rajesh, M., Maheshwari, U. (2022). Machine Learning-Based Categorization of Brain Tumor Using Image Processing. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_24 DOI: https://doi.org/10.1007/978-981-16-6448-9_24

[15] Maheshwari, R.U., B.Paulchamy, Pandey, B.K. et al. (2024). Enhancing Sensing and Imaging Capabilities Through Surface Plasmon Resonance for Deepfake Image Detection. Plasmonics https://doi.org/10.1007/s11468-024-02492-1 DOI: https://doi.org/10.1007/s11468-024-02492-1

[16] Maheshwari, Uma, and Kalpanaka Silingam. "Multimodal Image Fusion in Biometric Authentication." Fusion: Practice and Applications 1, no. 2 (2020): 7991. . DOI: https://doi.org/10.54216/FPA.010203

[17] Zhang, W., Li, X., & Chen, Y. (2021). Adaptive task offloading in edge-cloud systems for IoT applications. IEEE Transactions on Industrial Informatics, 17(5), 3224-3234.

[18] Xu, L., Wang, X., Chen, H., & Zhang, Y. (2020). Federated learning-based resource allocation for task offloading in edge computing. IEEE Internet of Things Journal, 7(6), 4901-4913.

[19] Huang, C., Hu, Q., Guo, L., & Zhang, X. (2020). Deep Q-network-based task offloading for edge computing. IEEE Internet of Things Journal, 7(8), 7252-7263.

[20] Cheng, Y., Zhu, L., Zhang, H., & Li, Y. (2021). Multi-agent reinforcement learning for collaborative task offloading in edge computing systems. IEEE Transactions on Cloud Computing, 9(3), 915-928.

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Published

2025-05-12

How to Cite

Maheshwari, U., Yusuf Ceylan, Seher Polat, Nurdan Karpuz, Osman Gunay, Mucize Sarıhan, … Sabiha Anas Boussaa. (2025). Optimizing Computational Efficiency in IoT Ecosystems Using Hybrid Edge-Cloud Offloading and Adaptive Learning Models. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1827

Issue

Vol. 11 No. 2 (2025): IJCESEN

Section

Research Article

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