Design of an Intelligent Power Management System for IoT Devices Using Machine Learning.

The rapid expansion of Internet of Things (IoT) ecosystems has transformed how devices interact with each other and the environment. However, the exponential increase in connected devices has led to serious concerns over energy consumption, particularly in resource-constrained and battery-powered systems. Conventional power management techniques often employ static thresholds or rule-based heuristics, which fail to adapt to the dynamic and context-sensitive nature of IoT environments. This paper presents the design and development of an intelligent power management system for IoT devices using machine learning (ML). The proposed system employs time-series forecasting and supervised learning algorithms to predict workload patterns, environmental factors, and device usage. Based on these predictions, the system dynamically adjusts energy consumption through intelligent scheduling, adaptive sensor sampling, and communication frequency control. We trained and validated our models using real-world sensor datasets from environmental monitoring nodes. The experimental results show that our ML-based power optimization system achieves up to 35% energy savings while maintaining performance metrics such as latency and data fidelity. Furthermore, this system demonstrates adaptability across various IoT domains including agriculture, healthcare, and smart homes. The modular architecture ensures scalability and compatibility with modern microcontrollers. This research underscores the potential of machine learning in driving energy-aware intelligence in future IoT networks and paves the way for more sustainable and autonomous device ecosystems.