Adaptive Context-Aware Hierarchical Resource Management Algorithm for

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.