Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management

Abstract

Advances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applications for remote health monitoring generate massive amounts of data in a short period. Internet of Things (IoT) and cloud systems, resulting in unprocessed and inadequate data for end-users. Since cloud technology is a dispersed intermediary layer between the edge network and the cloud infrastructure, the delay is significantly decreased, and reliable communications can be established using cloud computing on an IoT-enabled healthcare device. This study presents an Energy Efficient Healthcare Data Management Method (EE-HDMM), which enhances health monitoring using an IoT-assisted wearable sensor infrastructure. The difficulty of constrained sources and the energy consumption is minimized by proposing the optimum energy resource allocation method. Wearable sensor technology supported by cloud computing is the ideal and dependable platform for powerful life-critical systems that are unlikely to have communication delays. Compared to conventional techniques, the suggested EE-HDMM has a high precision rate of 97%, a sensitivity rate of 94%, and a system efficiency of 96%.