  Millions of battery-powered sensors deployed for monitoring purposes in a
multitude of scenarios, e.g., agriculture, smart cities, industry, etc.,
require energy-efficient solutions to prolong their lifetime. When these
sensors observe a phenomenon distributed in space and evolving in time, it is
expected that collected observations will be correlated in time and space. In
this paper, we propose a Deep Reinforcement Learning (DRL) based scheduling
mechanism capable of taking advantage of correlated information. We design our
solution using the Deep Deterministic Policy Gradient (DDPG) algorithm. The
proposed mechanism is capable of determining the frequency with which sensors
should transmit their updates, to ensure accurate collection of observations,
while simultaneously considering the energy available. To evaluate our
scheduling mechanism, we use multiple datasets containing environmental
observations obtained in multiple real deployments. The real observations
enable us to model the environment with which the mechanism interacts as
realistically as possible. We show that our solution can significantly extend
the sensors' lifetime. We compare our mechanism to an idealized, all-knowing
scheduler to demonstrate that its performance is near-optimal. Additionally, we
highlight the unique feature of our design, energy-awareness, by displaying the
impact of sensors' energy levels on the frequency of updates.
