Transforming Public Health Data Management in IoMT Networks Based on Innovative Offloading Scheme

Healthcare platform monitoring IoT-oriented technologies constitute the idea of Internet of Medical Things (IoMT) in public health and medical services. The amount and quality of created data have a substantial influence on data management and privacy compute offloading solutions are unable to keep up with the growing needs of the health industry, especially when fast and dependable communication was needed. The study suggests a unique approach called Mutated Barnacles Mating Optimization (MBMO) for assisting the data management issues in IoMT systems. The suggested MBMO framework successfully addresses problems that are common in the medical industry by using a data offloading technique. To overcome the issues of discrete tasks and resource allocation, guarantees the needs of dependable and efficient communication. We implemented Java software. The evaluation of the performance step encompasses several measures, such as energy consumption (J/ms) and Time delay (ms)model to assess the efficiency of the suggested forecasting algorithm. We performed an assessment of comparison with other established approaches results indicate that the suggested model produces superior results for assisting the data management issues in IoMT systems.