Enhancing Energy Efficiency in Telehealth IoT through Multi-Objective Optimization

This paper proposes a novel approach to enhance energy efficiency in Telehealth Internet of Things (IoT) through the integration of multi-objective optimization in a hybrid fog/cloud computing platform. Extending beyond existing fog-based telehealth models, the study employs established algorithms (NSGA-II and SPEA2) to optimize diverse and occasionally conflicting metrics such as energy efficiency, response time, throughput, and resource utilization. The resulting Pareto front provides decision-makers with tailored solutions, revealing a harmonious balance between energy efficiency and performance attributes. Simulated experiments demonstrate the effectiveness of this approach, highlighting its potential to reshape optimization paradigms for Telehealth IoT deployments in fog/cloud environments. This research pioneers a comprehensive strategy for reconciling energy efficiency and performance in Telehealth IoT systems, offering insights for informed decision-making and a sustainable future for energy-saving initiatives.