AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS

The fast growth of Internet of Things (IoT) devices in smart cities, farms, and health care is creating big environmental problems. Most of these problems come from throwing away short‑life chemical batteries and the electronic waste they generate. This research looks at a wireless power‑transfer (WPT) system that uses artificial intelligence to cut those impacts. The system is built in three layers: * An intelligent radio‑frequency (RF) energy‑harvesting layer that uses adaptive rectenna arrays. * A power‑allocation engine that runs reinforcement‑learning algorithms. * A cloud‑native platform that coordinates energy across the network. The AI predicts how much power each device will need and then directs the RF energy to the

right places. That should raise the efficiency of power transfer and cut down on wasted transmission. The M.Tech project concentrates on simulating, modeling, and testing the reinforcement‑learning methods and the rectenna designs. The step‑by‑step methodology helps measure gains in energy efficiency, reductions in network waste, and longer device lifetimes. In the end, the goal is to create self‑sustaining, battery‑free IoT networks that lower the environmental footprint of large‑scale digital infrastructure.

Message: Hi FE, I need help with “AI-ENABLED WIRELESS POWER TRANSFER ARCHITECTURES FOR SUSTAINABLE IoT ECOSYSTEMS” in “Environmental Engineering”