Year: 2025
Type: article
Abstract: The proliferation of Internet of Things (IoT) devices in healthcare settings has generated unprecedented volumes of patient data that require efficient processing mechanisms. Edge computing has emerged as a paradigm that allows data processing closer to the source, reducing latency and enabling real-time analytics critical for patient monitoring. This research explores the implementation of edge computing architectures for real-time patient monitoring systems, evaluating their performance across multiple healthcare scenarios. Through experimental deployments in both simulated and real clinical environments, we demonstrate that edge-based monitoring systems reduce data transmission latency by 68% compared to cloud-centric approaches while maintaining 99.7% accuracy in critical parameter monitoring. Our findings indicate that strategic placement of computing resources at the network edge significantly enhances the responsiveness of patient monitoring systems, particularly in time-sensitive medical scenarios. The proposed framework incorporates multi-level data processing with automated triage capabilities, addressing key challenges in contemporary healthcare monitoring including privacy preservation, resource optimization, and reliable operation during network degradation.
Author: Praggnya Kanungo
Institution: University of Virginia
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409134038