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

TL;DR

This paper introduces a goal-oriented scheduling approach using deep reinforcement learning for sensor data collection in IoT systems monitoring non-linear dynamic processes, improving accuracy and energy efficiency.

Contribution

It develops a novel DRL-based scheduler that minimizes response error and reduces sensor polling, enhancing IoT monitoring efficiency for non-linear systems.

Findings

Achieves lower mean square error than benchmark methods.

Reduces sensor polling by 77%-88%, saving energy.

Demonstrates effectiveness through numerical analysis.

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 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, which would later lead to the minimization of the mean square error (MSE) of the query responses. Numerical analysis demonstrates that the proposed GoS obtains a smaller MSE compared to the benchmark scheduling methods while being of lower complexity. Moreover, this is attained without polling sensors during 77%-88% of the testing phase, thus, resulting beneficial in terms of energy efficiency.