An Adaptive Energy Optimization Mechanism for Decentralized Smart Healthcare Applications

Body Sensor Networks (BSNs) is the emerging driver to revolutionize the entire landscape of the medical field. However, sensor-based handheld devices suffer from high power drain and limited battery life, due to their resource-constrained nature. Hybridization of different energy control methods and protocol layers is a best approach to enhance the performance perimeters for smart and connected healthcare. Thus, this paper mainly contributes in two ways. First, adaptive duty-cycle optimization algorithm (ADO), is proposed which optimizes the active time by considering the specific power level which leads to more energy saving instead of increasing the sleep period unlike the traditional methods. Second, joint Green and sustainable healthcare framework is proposed. Extensive theoretical and experimental analysis is performed by adopting real-time data sets with Monte Carlo simulation in MATLAB, and it is revealed that proposed algorithm enhance reliability and energy saving by 24.43%, 36.54%, respectively. Thus it can be said that proposed algorithm have more potential for energy constrained sensor devices in smart and connected healthcare platform.

Automated summary

This paper proposes an adaptive duty-cycle optimization algorithm and a joint Green and sustainable healthcare framework to enhance energy saving and reliability. Theoretical and experimental analysis shows that these methods can improve energy saving and reliability by 24.43% and 36.54% respectively. This suggests that the proposed algorithm has great potential for energy constrained sensor devices in smart and connected healthcare platform.