ENERGY EFFICIENT NANO WEARABLE DEVICES OPTIMISED WITH INTEGRATION OF GAINING SHARING KNOWLEDGE ALGORITHM FOR REALTIME HEALTH MONITORING

Nano-wearable healthcare devices that capture physiological data precisely and continuously for real-time health monitoring. However, maintaining continuous and efficient functioning is difficult on finite energy sources, including energy harvesters. A task scheduling technique working on the Sharing Knowledge (TS-GSK) methodology is presented in the present research to maximise energy consumption in nano-wearable health monitoring devices. The system dynamically distributes computer resources to activities carried out by nanoscale temperature and heart rate sensors, reducing duplicated operations and increasing power efficiency. This research for-lab nano-wearable device's data validates the suggested architecture, significant gains in sensor performance, job completion delays, energy efficiency, and dependability. Furthermore, GSKbased scheduling outperforms existing methods to efficiently handle resource constraints without compromising overall responsiveness. This task scheduling system ensures nano-wearable healthcare devices' operation and dependability by offering a workable and effective solution to energy issues.