TY - JOUR
T1 - EdgeLaaS
T2 - Edge learning as a service for knowledge-centric connected healthcare
AU - Li, Gaolei
AU - Xu, Guangquan
AU - Sangaiah, Arun Kumar
AU - Wu, Jun
AU - Li, Jianhua
N1 - Funding Information:
AcKnowledgMents This work is sponsored by the National Natural Science Foundation of China (Grant Nos. 61572355, U1736115, and 61431008).
Publisher Copyright:
© 2019 IEEE
PY - 2019/11/1
Y1 - 2019/11/1
N2 - 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.
AB - 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.
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U2 - 10.1109/MNET.001.1900019
DO - 10.1109/MNET.001.1900019
M3 - Article
AN - SCOPUS:85096972077
SN - 0890-8044
VL - 33
SP - 37
EP - 43
JO - IEEE Network
JF - IEEE Network
IS - 6
M1 - 1900019
ER -