  As the advancement of deep learning (DL), the Internet of Things and cloud
computing techniques for biomedical and healthcare problems, mobile healthcare
systems have received unprecedented attention. Since DL techniques usually
require enormous amount of computation, most of them cannot be directly
deployed on the resource-constrained mobile and IoT devices. Hence, most of the
mobile healthcare systems leverage the cloud computing infrastructure, where
the data collected by the mobile and IoT devices would be transmitted to the
cloud computing platforms for analysis. However, in the contested environments,
relying on the cloud might not be practical at all times. For instance, the
satellite communication might be denied or disrupted. We propose SAIA, a Split
Artificial Intelligence Architecture for mobile healthcare systems. Unlike
traditional approaches for artificial intelligence (AI) which solely exploits
the computational power of the cloud server, SAIA could not only relies on the
cloud computing infrastructure while the wireless communication is available,
but also utilizes the lightweight AI solutions that work locally on the client
side, hence, it can work even when the communication is impeded. In SAIA, we
propose a meta-information based decision unit, that could tune whether a
sample captured by the client should be operated by the embedded AI (i.e.,
keeping on the client) or the networked AI (i.e., sending to the server), under
different conditions. In our experimental evaluation, extensive experiments
have been conducted on two popular healthcare datasets. Our results show that
SAIA consistently outperforms its baselines in terms of both effectiveness and
efficiency.
