| Popis: |
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. |
