Open Access

ARTICLE

ANNDRA-IoT: A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

Abdullah M. Alqahtani^1,*, Kamran Ahmad Awan², Abdulaziz Almaleh³, Osama Aletri⁴

1 Department of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, Jazan, 45142, Saudi Arabia
2 Department of Information Technology, The University of Haripur, Haripur, 22620, Pakistan
3 College of Computer Science, King Khalid University, Abha, 62529, Saudi Arabia
4 Department of Computing, College of Engineering and Computing, Umm Al-Qura University, Makkah, 21955, Saudi Arabia

* Corresponding Author: Abdullah M. Alqahtani. Email:

(This article belongs to the Special Issue: Leveraging AI and ML for QoS Improvement in Intelligent Programmable Networks)

Computer Modeling in Engineering & Sciences 2025, 142(3), 3155-3179. https://doi.org/10.32604/cmes.2025.061472

Received 25 November 2024; Accepted 30 January 2025; Issue published 03 March 2025

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.

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Keywords

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