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Battery-powered sensors deployed in the Internet of Things (IoT) requireenergy-efficient solutions to prolong their lifetime. When these sensorsobserve a physical phenomenon distributed in space and evolving in time, thecollected observations are expected to be correlated. We take advantage of theexhibited correlation and propose an updating mechanism that employs deepQ-learning. Our mechanism is capable of determining the frequency with whichsensors should transmit their updates while taking into the consideration anever-changing environment. We evaluate our solution using observations obtainedin a real deployment, and show that our proposed mechanism is capable ofsignificantly extending battery-powered sensors' lifetime without compromisingthe accuracy of the observations provided to the IoT service.

Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices