Investigation of the Efficiency of an Internet of Healthcare Things for Healthcare Monitoring Using M/M/C/K Queuing Models

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Dr. K. Sai Manoj

Abstract

Modern computing facilities for medical surveillance, which appear to be the essential computing keystone that aided access and processing of health data of each patient at the very edge of the healthcare system to combat regional or global virus pestilence, appear to be the essential computing keystone. Many computing system architectures for medical surveillance have been given in past studies, but only a few researchers have focused on the pure effectiveness of healthcare data transport in depth. To test the efficacy of an Internet of Healthcare Things (IoHT) architecture, researchers used an M/M/c/K queuing network strategy in conjunction with a three-layer cloud computing continuum. Medical data from body-attached IoT devices at the network edge to local consumers in the fog layer and faraway customers in the fog layer is taken into account in the model. In addition, in two situations, researchers examine how changes in setup and computer layer processing capacity affect key performance indicators. The results of the analysis and modeling reveal that the proposed model can accurately forecast the system response time and the number of computing resources required for healthcare data services in a variety of workload scenarios to reach the goal of efficiency. As a consequence, the study's findings can be applied to better clinical administration in hospitals and medical centers, and also the development of computing structures that are suitable for medical monitoring in the case of a virus epidemic.

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