Intelligent Hybrid Model for Energy-Efficiency on WBAN

The Intelligent Hybrid Model for energy efficiency on Wireless Body Area Networks (WBANs) has gained substantial attention for its potential to enable remote monitoring and healthcare applications. However, energy efficiency remains a crucial challenge in the design and operation of WBANs due to the constrained resources of wearable devices and the need for continuous operation. In this research paper, we recommend an intelligent hybrid framework for energy efficiency in WBANs. The model combines the benefits of two key techniques: dynamic duty cycling and machine learning-based optimization. By intelligently adapting the duty cycle of individual sensor nodes based on their activity levels and environmental context, the proposed framework may optimize energy consumption while maintaining reliable data transmission. Additionally, machine learning algorithms are used, that can also help in the prediction of sensor node activity patterns and optimize duty cycle settings in real-time. The intelligent hybrid framework may achieve significant energy savings that extend the WBAN devices' lifetime and also enhance their reliability for critical healthcare applications. The proposed model holds the potential to revolutionize the energy efficiency paradigm in WBANs, paving the way for widespread adoption and deployment in various healthcare and remote monitoring scenarios.