Adaptive Energy-Aware Machine Learning Framework for Dynamic IoT Environments

Abstract

The rapid expansion of Internet of Things (IoT) ecosystems has intensified the deployment
of machine learning (ML) models at the network edge to enable real-time analytics and intelligent
decision-making. However, IoT devices are inherently resource-constrained, operating under limited
battery capacity, restricted memory, and dynamic workload conditions.