Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of
  Things Devices   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.

21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol