Year: 2025
Type: article
Abstract: The rapid proliferation of Internet of Things (IoT) applications necessitates real-time data processing and low-latency communication, challenging traditional cloud computing paradigms. This research addresses these challenges by integrating edge computing with emerging 6G networks, proposing the ARMO (Adaptive Resource Management and Offloading) model. The ARMO model leverages intelligent task scheduling, dynamic resource allocation, and energy-efficient strategies to enhance the performance of edge computing environments. Our comprehensive methodology involves collecting and preprocessing data from IoT devices, extracting relevant features, predicting resource demand, optimizing task offloading, and continuously monitoring and adjusting resource allocation using advanced machine learning techniques. The results demonstrate significant improvements, including a 47% reduction in average latency, a 40% decrease in total energy consumption, and a 20% increase in resource utilization. Additionally, the model achieved a 98% task completion rate and consistently higher network throughput compared to previous models. These findings underscore the ARMO model’s potential to support the next generation of real-time IoT applications, providing a robust, efficient, and scalable solution for integrating edge computing with 6G networks.
Source: Mathematics
DOI: 10.3390/math13091540
Author: Ahmed M. Alwakeel
Institution: University of Tabuk
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: University of Tabuk
Award: G1663343393
OpenAlex ID: W4410155808