Empowering IoT Devices with Energy-Efficient AI and Machine Learning

Existing IoT devices frequently exhibit inefficiencies in energy consumption and decision-making processes, resulting in inadequate efficiency and environmental impact. The study describes a unique framework that uses artificial intelligence (AI) and machine learning (ML) methods to provide Internet of Things (IoT) devices with cognitive decision-making capabilities, hence improving energy efficiency and overall performance. The proposed method enables real-time autonomous decision-making in IoT contexts by collecting extensive data, preprocessing it, and designing features, then selecting, training, and deploying algorithms. Continuous learning mechanisms enable flexibility in changing situations, while thorough testing reveals considerable gains in energy efficiency, proposed system dependability, and user pleasure. The results show a 30% reduction in energy consumption, a 25% increase in resource utilization, a 5% improvement in proposed system uptime, and significant improvements in user satisfaction indicators. The proposed method is an exciting possibility for making IoT deployments more sustainable and efficient, paving the path for intelligent technology ecosystems.