Optimizing smart health monitoring systems: enahancing energy efficiency and reducing latency with multi-level clustering and grey wolf optimizer

The COVID-19 pandemic has accelerated the demand for more efficient Smart Health Monitoring Systems (SHMS) in healthcare. While clustering techniques are widely used to improve energy efficiency and reduce data latency in smart hospitals, existing solutions often fail to fully exploit the potential of clustering. This paper introduces a novel Multi-Level Clustering Algorithm (MLCA) to enhance energy efficiency and reduce network latency in SHMS. Additionally, the paper integrates wireless power transmission (WPT) technology with a Grey Wolf Optimizer-based Tour Optimization of Power Transmitter Device (GWO-TOP), uniquely considering patients’ real-time health status scores alongside traditional metrics like remaining energy and node lifetime. The incorporation of health status scores allows for more intelligent prioritization in the PTD’s charging schedule, leading to improved network longevity and better patient care. Simulation results demonstrate the superiority of the proposed method in extending network life, reducing delays, and optimizing patient monitoring. These findings highlight the potential of GWO-TOP to significantly advance SHMS by improving both system efficiency and patient outcomes.