THE COMPARATIVE ANALYSIS OF THE EFFECTIVENESS OF EDGE COMPUTING AND FOG COMPUTING IN MEDICAL MONITORING SYSTEMS
DOI:
https://doi.org/10.32689/maup.it.2024.3.6Keywords:
data processing, distributed systems, signal latency, real-time processing, cloud technologiesAbstract
This article is dedicated to a comparative analysis of the effectiveness of Edge Computing and Fog Computing technologies in medical monitoring systems. The aim of the study is to examine the advantages and disadvantages of each technology to determine the most optimal approach for ensuring stable and efficient operation of medical monitoring systems. The methodology of the research involved analyzing scientific publications, evaluating architectural features, data processing speed, fault tolerance, scalability, and energy consumption of Edge and Fog Computing technologies. To achieve this aim, methods of systems analysis, load modeling, and integration analysis were used to assess the performance and feasibility of implementing the selected technologies in medical systems. The scientific novelty of the work lies in developing a methodology for selecting the optimal technology based on specific requirements for performance, reliability, and energy efficiency in real-time conditions. The results of the study showed that Edge Computing technology provides minimal latency and high-speed response to changes in patient conditions, which is critical in intensive care units. Meanwhile, Fog Computing offers more flexible scalability and high fault tolerance, making it effective for large networks or remote sites where large volumes of data need to be processed. The conclusions emphasize that the choice between these technologies depends on the specific tasks of the medical infrastructure: Edge Computing is optimal for rapid local processing, while Fog Computing is more suitable for distributed systems with high demands for reliability and security. A combined use of both technologies is recommended to create a flexible and adaptive medical infrastructure.
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