An Enhanced Framework for 5g IoT Smart Healthcare System

Abstract

Internet of Things (IoT) is a buzzword in this modern world provides smart applications and connect the human life with tiny devices such as sensors. Smart healthcare is an application that is required remote patient monitoring including some real-time parameters for the analysis of patient health. For the real-time monitoring of patient, IoT devices are required high power radio connectivity such as Fifth Generation (5G) in data transmission. The rapid involvement of 5G-enabled IoT technology has revolutionized the healthcare sector, providing efficient and real-time monitoring and diagnosis capabilities. However, with the increasing adoption of 5G-IoT in healthcare applications, distinct problems are identified with the applications of 5G-IoT such as there is no standard framework for handling the applications, lack of network resource optimization leads to the wastage of network bandwidth, lack of energy-efficient resources, and malicious data or confidentiality of sensitive medical data are the major concern. To solve this problem, an enhanced system framework is proposed that is integrated in four distinct modules. newlineThe first module is proposed a system framework that provides the overall methodology used for the enhancement of the 5G-IoT smart healthcare application. The proposed 5G-IoT smart healthcare system (SHS) is explained for the effective handling of the 5G-IoT applications. A customized architecture is proposed for a 5G-IoT based smart healthcare system that incorporate different sensors, technology standards and communication protocols. The proposed architecture is validated using a software called Cisco Packet tracer, demonstrating the effectiveness and dependability of the suggested system. newlineThe second module is proposed an intelligent approach for network resource optimization and analysis of a 5G-IoT based smart healthcare network. The performance of the system can be examined by using an automatic model, named Fast Library for Automated Machine Learning (FLAML).