  Goal-oriented communication (GoC) is a form of semantic communication where
the effectiveness of information transmission is measured by its impact on
achieving the desired goal. In the context of the Internet of Things (IoT), GoC
can make IoT sensors to selectively transmit data pertinent to the intended
goals of the receiver. Therefore, GoC holds significant value for IoT networks
as it facilitates timely decision-making at the receiver, reduces network
congestion, and enhances spectral efficiency. In this paper, we consider a
scenario where an edge node polls sensors monitoring the state of a non-linear
dynamic system (NLDS) to respond to the queries of several clients. Our work
delves into the foregoing GoC problem, which we term goal-oriented scheduling
(GoS). Our proposed GoS utilizes deep reinforcement learning (DRL) with
meticulously devised action space, state space, and reward function. The
devised action space and reward function play a pivotal role in reducing the
number of sensor transmissions. Meanwhile, the devised state space empowers our
DRL scheduler to poll the sensor whose observation is expected to minimize the
mean square error (MSE) of the query responses. Our numerical analysis
demonstrates that the proposed GoS can either effectively minimize the query
response MSE further or obtain a resembling MSE compared to benchmark
scheduling methods, depending on the type of query. Furthermore, the proposed
GoS proves to be energy-efficient for the sensors and of lower complexity
compared to benchmark scheduling methods.
