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| Title | Cloud-Edge Hybrid Deep Learning Framework For Scalable Iot Resource Optimization |
|---|---|
| ID_Doc | 14476 |
| Authors | Lilhore U.K.; Simaiya S.; Sharma Y.K.; Rai A.K.; Padmaja S.M.; Nabilal K.V.; Kumar V.; Alroobaea R.; Alsufyani H. |
| Year | 2025 |
| Published | Journal of Cloud Computing, 14, 1 |
| DOI | http://dx.doi.org/10.1186/s13677-025-00729-w |
| Abstract | In the dynamic environment of the Internet of Things (IoT), edge and cloud computing play critical roles in analysing and storing data from numerous connected devices to produce valuable insights. Efficient resource allocation and workload distribution are vital to ensuring continuous and reliable service in growing IoT ecosystems with increasing data volumes and changing application demands. This study proposes a novel optimisation approach utilising deep learning to tackle these challenges. The integration of Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) offers a practical approach to addressing the dynamic characteristics of IoT applications. The hybrid algorithm's primary characteristic is its capacity to simultaneously fulfil multiple objectives, including reducing response times, enhancing resource efficiency, and decreasing operational costs. DQN facilitates the formulation of optimal resource allocation strategies in intricate and unpredictable environments. PPO enhances policies in continuous action spaces to guarantee reliable performance in real-time, dynamic IoT settings. This method achieves an optimal equilibrium between policy learning and optimisation, rendering it suitable for contemporary IoT systems. This method improves numerous IoT applications, including smart cities, industrial automation, and healthcare. The hybrid DQN-PPO-GNN-RL model addresses bottlenecks by dynamically managing computing and network resources, allowing for efficient operations in low-latency, high-demand environments such as autonomous systems, sensor networks, and real-time monitoring. The use of Graph Neural Networks (GNNs) improves the accuracy of resource representation, while reinforcement learning-based scheduling allows for seamless adaptation to changing workloads. Simulations using real-world IoT data on the iFogSim platform showed significant improvements: task scheduling time was reduced by 21%, operational costs by 17%, and energy consumption by 22%. The method reliably provided equitable resource distribution, with values between 0.93 and 0.99, guaranteeing efficient allocation throughout the network. This hybrid methodology establishes a novel benchmark for scalable, real-time resource management in extensive, data-centric IoT ecosystems, consequently enhancing system performance and operational efficiency. © The Author(s) 2025. |
| Author Keywords | Cloud load balancing; Deep Q-Networks; GNN; IoT edge networks; Proximal policy optimization; Reinforcement learning |
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