Edge computing empowers the new generation of industrial cyber-physical systems to perform efficient distributed sensing, even under high data loads and frequent transmission demands. In the sensing process, observability is essential for complete state estimation and subsequent precise control. However, observability guarantee has become increasingly challenging due to the growing scale of sensing networks and the deployment constraints of sensing devices in industrial environments. For this problem, an observability guaranteed hybrid wired/wireless distributed edge sensing method is proposed, which optimizes accuracy and efficiency while guaranteeing observability. The dynamics-aware structural observability is proposed to bridge dynamics and observability under sensor scheduling. The capability of the system to achieve observability is quantitatively analyzed, and a novel necessary and sufficient condition for observability guarantee is derived. Furthermore, based on observability analysis and topology of networks, an energy-efficient heuristic algorithm is developed, which assigns wired transmissions between selected sensor–edge computing unit pairs for observability guarantee. Besides, deep reinforcement learning methods are adopt to improve sensing performance in the sense of expectation for wireless sensor scheduling, overcoming the difficulty of analytically expressing the objective function. Finally, our proposed method is applied to slab temperature estimation in the industrial hot rolling process, and its effectiveness is fully verified by simulation results.

The steel hot rolling production line is a representative Industrial Cyber-Physical System (ICPS), where accurate temperature estimation of the moving steel strip is crucial for ensuring product quality. Existing models of temperature dynamics in hot rolling face challenges due to the strip’s continuous motion and complex thermo-mechanical interactions. Many rely on continuous mathematical models that are difficult to apply for real-time estimation, or they neglect key factors. To address these limitations, we propose a discrete-time state-space model that incorporates air cooling, contact heat transfer, and deformation-induced heating. This formulation more accurately captures the actual rolling process and enables efficient temperature sensor scheduling for improved estimation accuracy. Building upon this model, we develop an Integrated Model Based Method (IMBM) that employs deep reinforcement learning for real-time perception and estimation of the temperature field in the continuously moving and deforming strip. Simulation results demonstrate that IMBM achieves accurate temperature estimation with low computational cost, exhibiting superior performance and strong practical applicability.

Joint estimation is crucial in the industrial Internet of Things (IIoT) by integrating data from diverse devices to improve monitoring accuracy. Network slicing can meet the heterogeneous needs of devices through logical isolation. However, existing methods often overlook the interaction of multiple slices on estimation performance, leading to potential estimation bias and ineffective resource costs. To address this, we propose an Age of Task (AoT)-driven associated network slicing method tailored for joint estimation scenarios. Specifically, we design an association-oriented slicing architecture for joint estimation that considers both the heterogeneous requirements of individual slices and the interactive effects of multiple slices. We define slice association based on the AoT to quantify the coupling relationship between slicing strategies and estimated performances. Moreover, we develop a dynamic-fitness multivariable particle swarm optimization algorithm to achieve associated slicing. Simulation results show that the associated slicing scheme achieves a flexible balance between timeliness and accuracy.

During the flight control system testing (FCST) process, multiple testing tasks should be completed. Battery-powered wireless sensors are used to measure the motion state of each flight control surface. In this paper, we investigate a multi-task scheduling problem to enhance overall monitoring accuracy during the FCST process. However, the decline in sensor battery levels, along with limited time slot resources, impacts the transmission quality of measurement data, leading to reduction in monitoring accuracy. Thus, we analyze the relationship among battery levels, transmission power, and monitoring accuracy to transform the original problem into an expectation probability maximization problem. Three important factors of monitoring accuracy are identified, based on which, we present the accuracy-oriented testing task scheduling (AOTS) algorithm. To validate the effectiveness of AOTS algorithm, we compare its performance among three different scheduling orders. Simulation results demonstrate that AOTS algorithm can not only improve the testing accuracy, but also reduce the fluctuation in accuracy among all testing tasks. Additionally, there are various elements in the FCST process that need to be uniformly managed to enhance the level of digitization. To address this issue, we design a digital twin enabled FCST (DT-FCST) system to manage data, models and algorithms in the FCST process. Finally, we implement the AOTS algorithm into developed DT-FCST system.

The booming development of 5G and Internet of Things (IoT) technologies significantly promotes the revolution of industrial IoT systems. Age of Information (AoI) is expected to play a critical role in industrial IoT systems, especially for time-sensitive monitoring and control applications. In addition, edge computing (EC) will be leveraged to effectively support industrial tasks in the limited communication and computing resources environment, bringing threefold benefits of shorter end-to-end delay, improving information timeliness, and reduced communication burden. Thus, we propose an edge-assisted co-design architecture of sampling-scheduling-control to improve the overall system performance. Under this architecture, a new definition, Age of Task (AoT), is proposed first to measure the timeliness of multielement and compute-intensive monitoring tasks in the industry. By analyzing the coupling relationship between AoT and control performance, an analytical expression of estimation error based on AoT is derived. Furthermore, we prove that the optimal control law could be expressed in a certain equivalent form, making it possible to decompose the design of control and network resource allocation (sampling, scheduling). According to the relation between AoT and estimation error, a co-design method, event-triggered sampling and max-age-reduce-first scheduling (ETMA), is proposed to minimize the system cost, including control cost and network energy consumption. The simulation results show that our co-design scheme has the optimal system cost among the four state-of-the-art schemes.

Joint monitoring, by integrating observations from multiple types of equipment, is essential for a thorough understanding of physical processes in the Industrial Internet of Things (IIoT). However, it does demand sufficient resources to ensure reliable and timely delivery of such observations. Although network slicing is widely used to meet such heterogeneous requirements, it falls short in this system, because it causes interconnected impacts on system performance across multiple slices. In this paper, we introduce an innovative associated network slicing framework for joint monitoring, which focuses on system cost minimization while accounting for slice associations. Particularly, to better understand the characteristics, we introduce a new concept, Age of Inexact Task (AoIT), to capture inter-slice associations. We then decompose the optimization variables to facilitate efficient Associated Network Slicing (ANS) algorithmic design, leading to a closed-form solution for intra-slice small-timescale resource allocation and an iterative block coordinate gradient descent algorithm for inter-slice large-timescale resource allocation. Simulation results demonstrate that our proposed ANS balances heterogeneous requirements and associations, showing significant reductions in system costs compared to existing solutions.

With the rapid evolution of industrial architecture, industrial edge computing is one of the most popular architectures at present. However, backup methods designed for traditional industrial architectures are not satisfactory in terms of device cost and applicability when they are applied in this new architecture. Against this background, this work presents a new backup method for industrial edge applications with better performance on resource consumption and high stability. The Function Block of IEC 61499 is used as the standard industrial edge application in this work. By carrying out experiments and employing multiple backup approaches, the resource occupancy rate and recovery time can be reduced simultaneously. The innovative methods and resource allocation models tackle the limitation of traditional backup methods effectively.

Aircraft final assembly line (AFAL) is a typical complex manufacturing system with multiple installation and test processes operating simultaneously at each workstation. Lots of robots and sensors are connected and operated for heterogeneous processes by sharing limited communication and computing resources. How to manage devices and resources in a coordinated and efficient way is thus very challenging. Digital twin (DT) is a powerful technology for multiple objects management in the complex assembly system. It enables us to coordinate various devices and allocate communication and computing resources at workstations. In this article, two main processes, i.e., vision-assisted installation and flight control system test, are considered in the AFAL. We introduce a DT-enabled AFAL system and propose a DT-assisted heterogeneous processes coordinated (DT-HPC) framework to coordinate various devices and resources at each workstation. The wirelessly connected robots and sensors are applied for perception and information fusion. In order to minimize the total energy consumption and computing resources of all the wireless devices, joint design of the wireless channel allocation, transmission power, and computing resource allocation are proposed to satisfy the diverse Quality-of-Service (QoS) requirements. First, we propose a priority-aware channel assignment (PACA) algorithm to allocate channels for sensors and robots. Then, the optimal computing resource allocation strategy for two processes is derived while guaranteeing the processing latency requirements. Next, we derive the minimum transmission power of wireless sensors to guarantee the monitoring accuracy and calculate the transmission power of robots to obtain the satisfied transmission rate. Finally, we apply the DT-HPC framework in the DT-enabled AFAL system. The simulation results prove that our proposed algorithms can save energy while guaranteeing different QoS requirements.

Multivariate-information and computation-intensive tasks play an important role in Wireless Sensor Network Systems (WSNSs), where information freshness has an important impact on the system performance of state analysis. Recently, the Age of Information (AoI) has been studied extensively as a promising metric to evaluate the freshness of state packets. However, most of the existing research focuses on optimizing the average AoI of a single information source, which can not be directly applied to the scenario with multi-source tasks. In this paper, we firstly present a novel definition of the Age of Task (AoT) for edge-assisted industrial WSNSs. Furthermore, the expressions of the sensing time and arrival time that determine the AoT tail distribution are given in detail. Then, we propose an AoT tail violation probability minimization problem to find the optimal sampling rate and give the feasible region of the sampling rate. Since it is difficult to obtain the exacted expression of the formulated problem, the Upper Bound Minimization Problem (UBMP) and the more tractable α-relaxed UBMP are proposed to obtain the near-optimal sampling rate. Finally, simulation results show that the sampling rate obtained by α-relaxed UBMP is nearly optimal for the AoT tail violation probability minimization problem.

State estimation plays an important role for the situation monitoring in industrial network systems, where multiple sensors observe a dynamical process and deliver state information to the remote center over wireless channels. However, the lossy wireless channels make the state information received by remote center be intermittent. Moreover, the scarcity of radio resources makes it challenging to simultaneously schedule a large number of sensors. In practice, different sensors usually have distinct contributions on state estimation, thus this paper firstly characterizes the integrated impact of sensing ability and transmission capacity on the state estimation performance, based on which a sensing aware opportunistic transmission scheme is then proposed. At each discrete time instant, the remote center determines which sensors to schedule based on the estimation demand and radio resources, and each sensor decides whether to participate the data transmission according to its residual energy. In order to further enhance the estimation performance and resource efficiency, the transmission scheduling and the sensor participation are jointly optimized by formulating a network-wide revenue maximization problem. This mix-integer nonlinear programming problem is effectively solved with the Dinkelbach method and heuristic algorithm. Finally, numerical simulation results verify the scheme efficiency.