Development an AI-Optimized RF Energy Harvesting for Sustainable IoT Networks: A Machine Learning Approach

The rapid expansion of the Internet of Things (IoT) has led to an increasing demand for energy-efficient solutions, particularly for battery-dependent devices with limited lifespans. RF energy harvesting presents a promising alternative, enabling IoT devices to capture ambient radio frequency signals to extend operational life and reduce reliance on traditional power sources. However, conventional RF energy harvesting systems suffer from low efficiency, inconsistent energy supply, and an inability to adapt to fluctuating environmental conditions. This study proposes an AI-optimized RF energy harvesting system that leverages machine learning algorithms to dynamically predict energy availability, optimize power conversion, and enhance energy storage utilization. The system employs regression models, reinforcement learning, and real-time data analytics to improve power efficiency and ensure sustainable IoT network operations. Experimental results demonstrate that the AI-driven system significantly outperforms traditional methods, improving energy harvesting efficiency by 40%, power adaptability by 50%, and overall device power optimization by 50%. This research contributes to the development of next-generation sustainable IoT infrastructures by integrating AI-driven decision-making into RF energy harvesting technologies.