Integration of Edge Computing Architectures for Real-Time Healthcare Monitoring

Abstract

The demand for continuous and intelligent healthcare monitoring has intensified with the rise of chronic illnesses, aging populations, and the increasing need for real-time physiological assessment. Edge computing has emerged as a critical enabler of next-generation healthcare systems by bringing computational power closer to patient-side devices and reducing latency inherent in cloud-based processing. This review explores the architectures, models, and implementation strategies that enable edge-driven healthcare monitoring. The paper evaluates sensor integration methods, distributed analytics, adaptive learning algorithms, and communication topologies that support low-latency and energy efficient monitoring. It also discusses hybrid models in which cloud and edge systems collaborate to enhance inference accuracy, optimize network traffic, and provide scalable health analytics. Major challenges including data heterogeneity, interoperability, patient privacy, and cybersecurity are examined alongside ongoing advancements such as federated learning, intelligent gateways, and AI-powered edge nodes. Case studies from telemedicine,  cardiac monitoring, and emergency response systems illustrate the advantages of edge computing in delivering timely decision support. The review concludes by identifying future directions related to predictive diagnostics, autonomous care systems, and edge-integrated biosensor networks that will shape the future of personalized digital healthcare.