The Internet of Things is a key enabler of mobile
health-care applications. However, the inherent constraints of
mobile devices, such as limited availability of energy, can impair
their ability to produce accurate data and, in turn, degrade the
output of algorithms processing them in real-time to evaluate the
patient’s state. This paper presents an edge-assisted framework,
where models and control generated by an edge server inform
the sensing parameters of mobile sensors. The objective is to
maximize the probability that anomalies in the collected signals
are detected over extensive periods of time under battery-imposed
constraints. Although the proposed concept is general, the control
framework is made specific to a use-case where vital signs –
heart rate, respiration rate and oxygen saturation – are extracted
from a Photoplethysmogram (PPG) signal to detect anomalies
in real-time. Experimental results show a 16.9% reduction in
sensing energy consumption in comparison to a constant energy
consumption with the maximum misdetection probability of 0.17
in a 24-hour health monitoring system.