EdgeLaaS: Edge learning as a service for knowledge-centric connected healthcare

Gaolei Li, Guangquan Xu*, Arun Kumar Sangaiah, Jun Wu, Jianhua Li

*この研究の対応する著者

研究成果: Article査読

18 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号1900019
ページ(範囲)37-43
ページ数7
ジャーナルIEEE Network
33
6
DOI
出版ステータスPublished - 2019 11月 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信

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