Edge Computing Architectures integrated with Internet of Things (IoT) healthcare monitoring platforms have emerged as a revolutionary approach for delivering real-time medical services and intelligent patient care. Traditional cloud-based healthcare systems often experience high latency, bandwidth limitations, and delayed decision-making due to centralized data processing. Edge computing addresses these limitations by processing healthcare data closer to the source devices, enabling faster response times, reduced network congestion, and improved reliability. In healthcare environments, IoT devices such as wearable sensors, smart medical equipment, and remote patient monitoring systems continuously generate massive volumes of physiological data including heart rate, blood pressure, oxygen saturation, glucose levels, and electrocardiogram signals. Edge computing architectures facilitate real-time analytics, anomaly detection, and emergency response mechanisms without depending entirely on remote cloud infrastructures. This technology supports critical healthcare applications such as telemedicine, elderly care, chronic disease management, emergency monitoring, and intelligent hospital systems. Furthermore, edge-enabled healthcare platforms improve data privacy, enhance security, and optimize resource utilization through localized processing. Despite numerous advantages, challenges such as device heterogeneity, limited edge resource capacity, interoperability issues, cybersecurity risks, and system scalability remain significant concerns. This study explores the architecture, applications, methodologies, benefits, and limitations of edge computing frameworks for IoT-based healthcare monitoring systems, emphasizing their role in developing efficient, secure, and intelligent healthcare infrastructures.
