Year: 2025
Type: conference-paper
Abstract: The expeditious expansion of the Internet of Things (IoT) demands efficient resource management strategies.Existing algorithms often struggle with scalability and efficiency in dynamic IoT environments.This study aims to develop a novel resource management algorithm that enhances scalability, energy efficiency, and latency performance in IoT networks.We propose the Context-Aware Hierarchical Resource Management (CAHRM) algorithm, which integrates context-awareness with hierarchical clustering to optimize resource allocation.The architecture comprises a hierarchical clustering mechanism organizing devices into clusters managed by local coordinators; a context-awareness module collecting realtime data on device status and network conditions; an adaptive learning module employing lightweight online learning to adjust resource allocation strategies; and an optimization process balancing energy consumption with latency requirements.Simulations over 1000 seconds compared CAHRM with existing state-of-the-art (SPDR, MORA, RATS, DRAH, RMRL, and EERA).Results showed that CAHRM outperformed existing methods, achieving up to 52% residual energy under high-load conditions, reducing average task completion time to 2.5 seconds under high traffic, and maintaining network throughput of 88% in large-scale networks of 500 devices.
Authors: Kamran Awan, Abdullah Alqahtani
Institutions: University of Haripur, Jazan University
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7084076168