OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical Systems

TL;DR

This paper introduces OIDM, a novel observability-based intelligent distributed sensing method utilizing deep reinforcement learning to optimize sensor scheduling in industrial cyber-physical systems, enhancing accuracy and efficiency.

Contribution

It presents a new approach linking observability with sensor transmission success, including linear observability approximations and probabilistic bounds to guide stochastic scheduling.

Findings

Improved sensing accuracy and power efficiency demonstrated in simulations.

Probabilistic bounds effectively guide sensor scheduling for observability.

Method validated on industrial hot rolling process temperature estimation.

Abstract

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness.