  In order to make better use of deep reinforcement learning in the creation of
sensing policies for resource-constrained IoT devices, we present and study a
novel reward function based on the Fisher information value. This reward
function enables IoT sensor devices to learn to spend available energy on
measurements at otherwise unpredictable moments, while conserving energy at
times when measurements would provide little new information. This is a highly
general approach, which allows for a wide range of use cases without
significant human design effort or hyper-parameter tuning. We illustrate the
approach in a scenario of workplace noise monitoring, where results show that
the learned behavior outperforms a uniform sampling strategy and comes close to
a near-optimal oracle solution.
