  Battery-powered sensors deployed in the Internet of Things (IoT) require
energy-efficient solutions to prolong their lifetime. When these sensors
observe a physical phenomenon distributed in space and evolving in time, the
collected observations are expected to be correlated. We take advantage of the
exhibited correlation and propose an updating mechanism that employs deep
Q-learning. Our mechanism is capable of determining the frequency with which
sensors should transmit their updates while taking into the consideration an
ever-changing environment. We evaluate our solution using observations obtained
in a real deployment, and show that our proposed mechanism is capable of
significantly extending battery-powered sensors' lifetime without compromising
the accuracy of the observations provided to the IoT service.
