@Article{cmes.2025.061472,
AUTHOR = {Abdullah M. Alqahtani, Kamran Ahmad Awan, Abdulaziz Almaleh, Osama Aletri},
TITLE = {ANNDRA-IoT: A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {142},
YEAR = {2025},
NUMBER = {3},
PAGES = {3155--3179},
URL = {http://www.techscience.com/CMES/v142n3/59779},
ISSN = {1526-1506},
ABSTRACT = {Efficient resource management within Internet of Things (IoT) environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities. This study introduces a neural network-based model that uses Long-Short-Term Memory (LSTM) to optimize resource allocation under dynamically changing conditions. Designed to monitor the workload on individual IoT nodes, the model incorporates long-term data dependencies, enabling adaptive resource distribution in real time. The training process utilizes Min-Max normalization and grid search for hyperparameter tuning, ensuring high resource utilization and consistent performance. The simulation results demonstrate the effectiveness of the proposed method, outperforming the state-of-the-art approaches, including Dynamic and Efficient Enhanced Load-Balancing (DEELB), Optimized Scheduling and Collaborative Active Resource-management (OSCAR), Convolutional Neural Network with Monarch Butterfly Optimization (CNN-MBO), and Autonomic Workload Prediction and Resource Allocation for Fog (AWPR-FOG). For example, in scenarios with low system utilization, the model achieved a resource utilization efficiency of 95% while maintaining a latency of just 15 ms, significantly exceeding the performance of comparative methods.},
DOI = {10.32604/cmes.2025.061472}
}