A Power-aware Approach for Smart Health Monitoring and Decision Support

Internet of Things (IoT) based smart health and wellness systems are increasingly gaining popularity for the next generation of medical services. Systems of medical devices that communicate seamlessly integrated and securely can improve patient outcomes, reduce medical errors, lower costs and overcome the existing medical systems limitations. While these innovative medical systems are emerging, they also bring new challenges as data protection and power management. This paper proposes an IoT architecture that allows to collect and analyze huge volumes of heterogeneous clinical data in order to monitor health status of patients and improve clinical decision support, by tackling the energy shortage challenge. The key feature of the proposed approach is a power-aware strategy based on energy load balancing in order to keep on the greatest number of power operated devices involved in the computational tasks. The paper presents a case study of power-aware smart health monitoring exploiting different types of data mining techniques. The experimental results show the effectiveness of the approach able to achieve energy savings by means of the proposed power-aware strategy.