  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.
