Goal-Oriented Sensor Reporting Scheduling for Non-linear Dynamic System Monitoring

Abstract

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 Internet-of-Things (IoT) networks, GoC can enable sensors to selectively transmit data relevant to the intended goals of the receiver, thereby facilitating timely decision-making, reducing network congestion, and enhancing spectral efficiency. In this paper, we consider an IoT 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. This work delves into the foregoing GoC problem and solution, which we termed goal-oriented scheduling (GoS). The latter utilizes deep reinforcement learning (DRL) with meticulously devised action space, state space, and reward function. A long short-term memory network is used to estimate the inter-query duration and the corresponding estimation standard deviation. This empowers the proposed DRL scheduler to make judicious decisions, even when no queries are posed. Numerical analysis demonstrates that the proposed GoS reduces the mean square error (MSE) of the query responses compared to the benchmark scheduling methods even as the number of clients and DRL action space increase, which proves its scalability. Moreover, this is attained without polling sensors during $70\% - 87\%$ of the testing phase, thus promoting energy efficiency. Lastly, the complexity of the proposed GoS is relatively lower than the benchmark scheduling methods.