A Novel layers-based Framework for Edge Computing in Healthcare Applications

New technologies have greatly impacted the healthcare sector and increased the need for vital problem-solving solutions like edge computing. Edge computing processes data near the source to avoid high latency, band- width constraints, and inefficient systems that are unsuitable for real-time health care. In this paper, a novel framework is proposed that introduces edge computing layers for health care data management, privacy, and system reliability. The proposed framework has five layers: device, edge, fog, cloud, and application layers. Every system layer does data mining, cleansing, real-time processing, analysis, and decision making. A detailed simulation assessed the framework’s efficacy. Signal filtering, averaging, feature extraction, and classification were done using the edge layer approach before and after physiological signal simulation. Performance was compared to a noise-free reference technique. The simulation results showed that the suggested edge layer technique improved data quality and classification accuracy for real-time healthcare applications. This study discusses edge computing’s minimal delay, privacy, expandability, and reliability benefits in healthcare. Simulations showed that the framework improved data processing and decision-making over conventional methods. This research work is aligned with the SDG 3 – Good Health and Wellbeing. The study advises healthcare practitioners and policymakers to use edge computing for improved management, diagnosis, and treatment to im- prove healthcare delivery.