In this paper, we investigate the link between the rate at which
events are observed by a monitoring system and the ability of the
system to effectively perform its tracking and surveillance tasks. In
general, higher sampling rates provide better performance, but they
also require more resources, both computationally and from the sensing
We have used Hidden Markov Models to describe the dynamic processes to
be monitored and (alpha,beta)-currency as a performance measure for the monitoring system. Our ultimate goal is to be able to determine the minimum sampling rate at which we can still fulfill the performance requirements of our system.
Along with the theoretical work, we have performed simulation-based
tests to examine the validity of our approach; we compare performance
results obtained by simulation with the theoretical value obtained
a priori from the scenario parameters and illustrate with a
simple example a technique for estimating the required sampling rate
to achieve a given level of performance.