  We study the problem of optimal sampling in an edge-based video analytics
system (VAS), where sensor samples collected at a terminal device are offloaded
to a back-end server that processes them and generates feedback for a user.
Sampling the system with the maximum allowed frequency results in the timely
detection of relevant events with minimum delay. However, it incurs high energy
costs and causes unnecessary usage of network and compute resources via
communication and processing of redundant samples. On the other hand, an
infrequent sampling result in a higher delay in detecting the relevant event,
thus increasing the idle energy usage and degrading the quality of experience
in terms of responsiveness of the system. We quantify this sampling frequency
trade-off as a weighted function between the number of samples and the
responsiveness. We propose an energy-optimal aperiodic sampling policy that
improves over the state-of-the-art optimal periodic sampling policy.
Numerically, we show the proposed policy provides a consistent improvement of
more than 10$\mathbf{\%}$ over the state-of-the-art.
