Energy-Efficient 6G IoT Networks for Wearable Healthcare Devices Using Hybrid Reinforcement Learning

Combining IoT networks with 6G technology has opened fresh prospects for sophisticated, energy-efficient healthcare solutions. This study offers a hybrid reinforcement learning (HRL) method for usage in wearable healthcare devices in order to optimize energy consumption and increase communication dependability in 6G-enabled IoT networks. utilizing deep reinforcement learning (DRL), one can forecast optimal resource allocation; utilizing adaptive decision-making in dynamic network environments, one can provide Q-learning guarantees. Important problems such network scalability, latency, and energy constraints in wearable healthcare devices are addressed by the hybrid method. The simulation results indicate significant increases in efficiency, latency, and reliability as compared to more traditional approaches. This paradigm emphasizes in the era of 6G IoT networks the possibilities of HRL to support smart, sustainable, patient-centric healthcare systems.