During continuous physiological monitoring, which can play a crucial role in finding and treating asymptomatic pathologies in patients, useful physiological data is collected and analyzed.  Examples of collected data include electrocardiograms (EKG), blood oxygen levels, weight, blood pressure and many others. 
In such a setting, patients wear collecting devices.  Collecting devices transmit data to an aggregator when the devices are within transmission range.  The aggregator, in turn, transmits the data to a remote archival and analysis platform.  Care providers are given secure access to the back end system so that they can monitor their patients, receive notifications and/or alerts, and possibly provide feedback to the patients based on the analysis and their own expertise. 
When the capacity of the system and of its components for handling and processing a stream of data is exceeded, load-shedding is triggered, whereby a portion of the data stream signal is discarded without processing. 
Existing systems that implement load-shedding techniques either assume a priori knowledge of the value of incoming data or revert to dropping data in a random manner.  The assumption of a priori knowledge is hard to enforce in practice (especially in the medical domain), and random shedding is far from optimal for many analysis algorithms.