Analysis Of Network Resource Management Techniques for Enhanced 5G-Iot Smart Healthcare Solutions

The Internet of Things (IoT) is all the rage these days; it links people’s daily lives to small devices like sensors and offers smart applications. In order to analyse the wellbeing of patients in real time, sophisticated applications for healthcare need remote surveillance of patients. In order for IoT devices to transmit data in real-time, they need highpower radio connection, including Fifth Generation (5G). The healthcare industry has been transformed by the growing engagement of 5G-enabled IoT devices, which has provided effective and real-time tracking and diagnosing abilities. Nevertheless, as 5G-IoT becomes more widely used in healthcare applications, certain issues have been determined. These include a lack of uniform guidelines for managing programs, inefficient use of network capacity (resulting in wasted bandwidth), insufficient environmentally friendly resources, and the most pressing concern-the security of private medical information or malicious information. An improved system architecture consisting of four separate modules is suggested as a solution to this issue. A sophisticated medical infrastructure based on 5G-IoT may be optimized and analysed using an intelligent methodology. An automated model called Fast Library for Automated Machine Learning (FLAML) may be used to analyse the system’s effectiveness. The suggested FLAML-based approach outperforms state-of-the-art models like Enhanced Spectrum Sensing, Reinforcement Learning, CNN, and POSENS, reaching a total accuracy of $99.35 \%$. This impressive level of precision proves that FLAML is a powerful tool for improving model efficiency.