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