EdgeLaaS: Edge Learning as a Service for Knowledge-Centric Connected Healthcare

TL;DR

EdgeLaaS introduces a framework for real-time, knowledge-centric healthcare data processing at the network edge, enhancing emergency response and personalized care in connected healthcare systems.

Contribution

The paper proposes a novel EdgeLaaS framework that enables local processing of healthcare data for timely, knowledge-based decision-making in connected healthcare environments.

Findings

EdgeLaaS improves emergency response times.

KCCH enhances personalized healthcare management.

Performance evaluations show superior efficiency of the proposed framework.

Abstract

By introducing networking technologies and services into healthcare infrastructures (e.g., multimodal sensors and smart devices) that are deployed to supervise a person's health condition, the traditional healthcare system is being revolutionized toward knowledge-centric connected healthcare (KCCH), where persons will take their own responsibility for their healthcare in a knowledge-centric way. Due to the volume, velocity, and variety of healthcare supervision data generated by these healthcare infrastructures, an urgent and strategic issue is how to efficiently process a person's healthcare supervision data with the right knowledge of the right guardians (e.g., relatives, nurses, and doctors) at the right time. To solve this issue, the naming and routing criterion of medical knowledge is studied. With this offloaded medical knowledge, we propose an edge learning as a service (EdgeLaaS) framework for KCCH to locally process health supervision data. In this framework, edge learning nodes can help the patient choose better advice from the right guardians in real time when some emergencies occur. Two application cases: 1) fast self-help and 2) mobile help pre-calling are studied. Performance evaluations demonstrate the superiority of KCCH and EdgeLaaS, respectively.