Meta-Heuristic Optimization for Enhanced Sensor-Based Health Monitoring in Cloud Computing Environments

Abstract

In this research, the integration of meta-heuristic optimization into health monitoring systems is explored for its transformative potential. The study employs a comprehensive evaluation approach, focusing on Performance Metrics, Resource Utilization, and Scalability Testing. Results indicate a consistently high level of accuracy (90% to 97%) and swift response times (125 to 165 milliseconds), highlighting the reliability and efficiency of the enhanced system. Resource Utilization demonstrates optimal memory and CPU usage (110 to 130 MB and 30% to 47%, respectively), underscoring the sustainable and balanced operation of the system. Scalability Testing reveals the system's adaptability to changes in user numbers and data complexity, with response times ranging from 150 to 200 milliseconds. Meta-heuristic optimization emerges as a key enabler, fine-tuning predictive capabilities, optimizing resource usage, and ensuring seamless scalability.