Physical layer security (PLS) is a fundamental challenge for sixth-generation (6G) wireless networks, particularly in integrated sensing and communication (ISAC) systems, where sensing targets may simultaneously act as potential eavesdroppers. In this paper, we investigate PLS in a reconfigurable intelligent surface (RIS)-assisted cell-free ISAC system, where distributed access points collaboratively serve users and actively sense potential eavesdroppers. We formulate a weighted sum secrecy rate maximization problem through joint ISAC beamforming design. The resulting non-convex problem is first transformed into a semidefinite programming (SDP) formulation and then solved via convex optimization techniques. To further enhance secure communication performance, we extend the framework by incorporating RIS phase shift optimization and propose an alternating optimization algorithm that jointly optimizes active ISAC beamforming and passive RIS configurations. This joint design exploits the controllable wireless propagation environment provided by RISs to enhance legitimate links while suppressing eavesdropping channels. Extensive simulation results demonstrate that the proposed approach significantly outperforms baseline approaches. Specifically, the proposed joint ISAC method improves the communication signal to interference plus noise ratio (SINR) by approximately 1.8 dB and the sensing signal-noise ratio (SNR) by 4.8 dB compared to sensing-priority and communication-priority baselines, respectively. Furthermore, the RIS-assisted framework improves a weighted sum secrecy rate gain of approximately 2.2 dB compared to the frameworks without RIS, validating the proposed framework as a promising solution for secure and spectrum-efficient cell-free ISAC systems in future 6G networks.

The advent of Industry 4.0 is leading to a radical restructuring of industrial control architectures,transitioning from the rigid,hierarchical models defined by the ISA-95 pyramid toward agile,decentralized cloud-edge-terminal architectures.Leveraging high bandwidth and low latency,5G technology is poised to function as the critical enabler of this evolution,facilitating the migration of industrial controllers,such as programmable logic controllers(PLCs),from the factory floor to cloud or edge servers to support large-scale,collaborative control via wireless networks.However,establishing direct controller-to-device(C2D)links over 5G for high-precision applications,such as motion control,faces two fundamental challenges.The first is protocol incompatibility:high-performance standards like Ethernet for Control Automation Technology(EtherCAT)operate at Layer 2 of the OSI model using custom Ethernet frames,rendering them strictly incompatible with standard,IP-based(Layer 3)5G routing.The second,and more profound challenge,is a fundamental performance mismatch.While EtherCAT necessitates deterministic,microsecond-level synchronization via mechanisms s uch as distributed clocks(DCs),5G networks remain subject to inherent latency jitter stemming from radio channel fluctuations,interference,and resource scheduling.This non-deterministic jitter compromises the sensitive timing of EtherCAT,potentially leading to synchronization loss,control-loop instability,and system failure.To overcome these limitations,this study proposes a comprehensive strategy for integrating 5G and EtherCAT within cloud-based motion control systems,offering a two-fold contribution:(1)to resolve protocol disparities,we design and implement an integration architecture based on the Virtual eXtensible Local Area Network(VxLAN).By employing network virtualization to construct a Layer 2 overlay atop the 5G IP underlay,this mechanism encapsulates entire EtherCAT frames within standard User Datagram Protocol(UDP)packets;this ensures transparent transmission across the infrastructure,effectively rendering the 5G network a virtual ethernet segment from the perspective of the control system;(2)Concurrently,to address the stability challenges posed by jitter,we introduce a novel quantitative performance evaluation framework.Recognizing that establishing connectivity is distinct from ensuring operational stability,this framework analyzes the impact of network jitter on EtherCAT's periodic data exchange.Subsequently,we derive a mathematical constraint model that explicitly correlates the minimum stable control period with key network performance indicators.This model serves as a robust predictive tool,allowing engineers to assess the feasibility of motion-control applications before physical deployment.The efficacy of both the VxLAN-based integration architecture and the analytical model is validated through extensive experimentation on a physical testbed using a commercial 5G network.By benchmarking the system performance,we demonstrate the solution's practical feasibility and confirm the accuracy of the constraint model's predictions.Finally,this research extends beyond a functional integration scheme to provide a theoretic methodology for assessing the viability of deterministic,real-time industrial applications over non-deterministic wireless channels.Leveraging the proposed 5G-based motion control performance analytical methodology,this work seeks to promote the future deployment of 5G and its evolutions such as 5G-Advanced and 6G,in the most demanding sectors of industrial automation.

As manufacturing moves toward flexibility, intelligence, and autonomy, industrial intelligence is shifting from data-centric processing to industrial embodied intelligence with real-time physical closed loops. Addressing the lack of focus on the synergistic coupling of communication, sensing, computing, and control in such scenarios, this paper takes their co-evolution as the main line. It abstracts industrial embodied intelligence as a closed-loop system of information transmission, state sensing, environmental cognition, and physical execution, systematically reviewing key enabling technologies. It reveals how deterministic networking supports distributed sensing and real-time control, how integrated sensing and communication enables continuous semantic mapping, how sensing–computing fusion allows near-source multimodal cognition, and how the computing–control loop ensures stable mapping from digital decisions to physical execution. Key challenges in cross-layer standardization, data alignment, multi-agent collaboration, and security are further summarized, and future research directions are outlined.

The development of multi-access edge computing and ultrareliable low-latency wireless technology has prompted the evolution of the traditional tightly coupled controller-equipment system into the industrial wireless control system. Such wireless control systems enable on-demand deployment of control tasks in edge or local controllers and are appropriate for flexible intelligent manufacturing. However, unlike public data services, control tasks have strong spatiotemporal correlation features, and traditional offloading schemes are not applicable for such space-time-dependent control tasks. Therefore, a novel double deep graph convolutional Q-network (DDGCQ) model is proposed to optimize the control task scheduling scheme and dynamically allocate networking and computing resources for minimizing the weighted sum of task processing delay and energy consumption. In the proposed model, the graph convolutional and directed graph convolutional networks are integrated with the Q-network to extract the spatiotemporal correlation features of control tasks. The simulation results showed that the proposed DDGCQ model has excellent learning capability for spatiotemporal control tasks. Furthermore, compared with baseline methods, the proposed DDGCQ-based task-scheduling method achieved the minimum weighted cost of latency-energy consumption while maintaining a task execution success rate exceeding 95

For more accessible and advanced health monitoring, the Body Area Network (BAN) design with semantic technologies offers efficient information sensing and communication in smart healthcare Artificial Intelligence of Things (AIoT). To address the critical challenges of effective communication and reduction of data transmission pressure in AIoT-BAN, a hybrid BAN system is proposed which enhances information processing and communication capabilities by leveraging semantic understanding and multimodal processing. It incorporates a semantic communication and sensing fusion framework, offloading based on the human Body Coupled Communication (BCC) channel, and multimodal semantic information integration to reduce data transmission pressure. The proposed method offers effective inclusive smart healthcare and daily health maintenance for the general public.

Traffic scheduling plays a critical role in Time-Sensitive Networking (TSN) for ensuring high reliability and deterministic latency. In this paper, we propose a novel window-based scheduling approach for the Time-Aware Shaper (TAS). By allowing packets to wait in egress queues before forwarding, our approach relaxes the strict timing constraints imposed by existing packet-based schedulers. We employ a generalized Network Calculus (NC) framework built on an End-to-End (E2E) network model, to analyze the upper-bound latency, which is then used to assess the schedulability of Time-Critical (TC) traffic. Inspired by the Proportional-Integral-Derivative (PID) closed-loop control architecture, we introduce an Incremental PID-based Search (IPS) algorithm to optimize schedulability, where the P, I, and D terms are leveraged to scale update steps, maintain search momentum, and dampen the oscillations, respectively. To accommodate various traffic classes, throughput constraints for non-TC traffic are incorporated as bounds on window lengths. Simulation experiments were performed on a multi-node network topology carrying large traffic volumes. Under optimal PID settings, the proposed IPS algorithm was evaluated against the well-validated Simulated Annealing (SA) method under a unified scheduling framework with identical decision variables and constraints to ensure a fair comparison. Results show that IPS consistently achieves higher schedulability and requires fewer iterations for flow counts ranging from 100 to 600. Furthermore, a real-time simulation platform based on OMNeT++ was developed, and the effectiveness of the proposed wait-allowed scheduling model was validated through optimized GCL configurations.

Time-sensitive networking (TSN) is emerged as a prominent solution for in-vehicle network (IVN) because of the deterministic communication capability in multi-traffics transmission. However, it is complicated to configure the gate control lists (GCLs) among multiple TSN switches under massive heterogeneous traffics, especially in IVN where communication and computing resources are seriously limited. Therefore, a new algebraic deterministic network calculus (DNC) algorithm named as fine-grained left-over service curve for time-aware shaper (FLSC-TAS) is proposed to evaluate more accurate delay upper bounds for time-critical traffics, aiming to simplify the GCL configurations for in-vehicle TSN. The proposed FLSC-TAS enhances the accuracy of service curve and traffic arrival curve considering the characteristics of priority queues in TAS mechanism. Compared with two commonly used baseline methods, the results demonstrate that the proposed FLSC-TAS algorithm achieves higher accuracy in delay upper bound evaluation. Furthermore, the average runtime is reduced by 14.04% compared to the most commonly used method.

Accurately predicting molten steel carbon content plays a crucial role in improving productivity and energy efficiency during the Basic Oxygen Furnace (BOF) steelmaking process. However, current data-driven methods primarily focus on endpoint carbon content prediction, while lacking sufficient investigation into real-time curve forecasting during the blowing process, which hinders real-time closed-loop BOF control. In this article, a novel Transformer-based framework is presented for real-time carbon content prediction. The contributions include three main aspects. First, the prediction paradigm is reconstructed by converting the regression task into a sequence classification task, which demonstrates superior robustness and accuracy compared to traditional regression methods. Second, the focus is shifted from traditional endpoint-only forecasting to long-term prediction by introducing a Transformer-based model for continuous, real-time prediction of carbon content. Last, spatial–temporal feature representation is enhanced by integrating an optical flow channel with the original RGB channels, and the resulting four-channel input tensor effectively captures the dynamic characteristics of the converter mouth flame. Experimental results on an independent test dataset demonstrate favorable performance of the proposed framework in predicting carbon content trajectories. The model achieves high accuracy, reaching 84% during the critical decarburization endpoint phase where carbon content decreases from 0.0829 to 0.0440, and delivers predictions with approximately 75% of errors within ±0.05. Such performance demonstrates the practical potential for supporting intelligent BOF steelmaking.

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks.

Smart factories are evolving into agentic control systems powered by wireless connectivity, edge computing, and artificial intelligence. This evolution alleviates computational limits and enhances production efficiency. However, the heterogeneity of spatiotemporal control logic, coupled with indeterminate wireless conditions, makes it challenging to coordinate control tasks and radio resources. To overcome these challenges, this paper presents a mixed graph-driven model to characterize spatiotemporal dependencies among control tasks and proposes a semi-centralized multi-agent collaborative framework. This paper employs an improved heterogeneous twin delayed deep deterministic policy gradient algorithm to jointly optimize task scheduling and radio resource allocation, thereby minimizing the average processing delay of industrial control processes. Simulation results demonstrate that the proposed algorithm achieves outstanding performance compared to benchmarks, improving execution success rate and data processing rate, as well as reducing model training time.

Optimizing task allocation and scheduling to minimize latency becomes increasingly critical for production efficiency and stability, as Industry 4.0 drives industrial systems to be more distributed and event-driven. However, conventional operations research and scheduling models often fail to capture the complex, event-driven characteristics and critical end-to-end process flows inherent to these decentralized automation systems. To address this gap, we propose the Triggered Task Flow Model (TTFM), a novel computational framework inspired by the IEC 61499 standard. TTFM provides a more realistic representation by explicitly modeling trigger sources, sinks, critical end-to-end task flows, and their quantifiable costs. To ground the model in practice, we establish a methodology for representing industrial applications and standard benchmarks within the TTFM framework. Analysis of latency composition verifies the model’s validity in capturing shifting physical bottlenecks. We then formulate the task allocation and scheduling optimization problem to minimize the latency of critical flows, which directly impacts production stability and responsiveness. A comparative evaluation of four heuristic algorithms identifies Tabu Search (TS) as providing a superior balance of solution quality and convergence speed for this NP-hard problem. Scalability analysis confirms the viability of TS for practical industrial scales. Our work provides industrial engineers and operations managers with a high-fidelity task model and its application methodology, validating its use for designing and managing next-generation, high-performance distributed industrial systems.

Mold level fluctuation significantly affects the stability and quality of the slab during the continuous casting process. However, traditional mechanism models are insufficient for providing accurate time-series predictions under complex and multivariable operating conditions. Additionally, the dynamic interdependencies between process variables and transient abnormal fluctuation events have been largely overlooked in existing studies. To address these limitations, we propose an integrated time–frequency characterization and prediction framework that combines multi-domain feature extraction with a long-sequence Informer model. First, the preprocessing pipeline transforms heterogeneous sensor data into standardized time series through normalization and standardization, thereby establishing a robust foundation for subsequent feature extraction and predictive modeling. Second, the time–domain and frequency–domain feature extraction methods are integrated to capture essential patterns in casting signals with improved resolution and interpretability. Third, the fusion features are embedded into a time-series prediction model, which performs robust forecasting of mold level behavior and enhances the identification of root causes behind fluctuation anomalies. Compared with conventional LSTM and Transformer models, the proposed framework achieves over 90% reduction in prediction error and provides interpretable insights into the correlations between casting parameters and mold level variations. Finally, real industrial experimental results demonstrate the performance of the proposed framework in enhancing prediction reliability and providing insight into fluctuations with scalable implementation.

Integrated Sensing and Communication (ISAC) systems represent a transformative paradigm for next-generation wireless networks by enabling dual-functional efficiency through simultaneous information transmission and environmental sensing. This paper investigates the critical challenge of robust beamforming design for monostatic ISAC systems operating in the near-field (NF) regime, where conventional far-field channel assumptions become fundamentally invalid. We develop a novel robust beamforming framework that optimizes minimum mean squared error (MMSE) estimation for sensing performance while guaranteeing stringent communication quality-of-service requirements. A distinctive feature of our approach lies in the proposed spherical wavefront-based channel model that incorporates both distance and angular response, providing superior accuracy compared to conventional planar wavefront approximations in NF scenarios. To resolve the inherent non-convex optimization problem with coupled sensing-communication constraints, we devise an efficient semidefinite relaxation (SDR)-based algorithm with guaranteed convergence properties. Comprehensive simulations demonstrate significant improvements in both sensing and communication performance, even under imperfect channel state information.

Green network aims to promote the sustainable development of communication systems, and base station (BS) and cells sleeping has been proven effective in reducing the power consumption of these systems. However, the current Reinforcement Learning (RL) based methods for multi-cells collaborative sleeping face significant challenges in real-world applications due to the complex users-to-cells connection relationships, and have been rarely researched in city-scale deployments. In this article, a robust RL-based multi-cells sleeping model called Graph Deep Deterministic Policy Gradient (GDDPG) is developed for handling highly complex communication scenarios. Besides, we first propose a framework for deploying multi-cells sleeping models at the city scale. Then two algorithms are put forward for determining the essential cells needed to maintain basic radio coverage and for effectively grouping these cells, which are two crucial works in the framework. Additionally, to address the temporal variation of traffic patterns, transfer learning is employed to fine-tune the pre-trained RL model periodically. Finally, we validate the feasibility of city-scale deployment algorithms and demonstrate the effectiveness of GDDPG by leveraging a computational platform and real-collected cells data from a telecom operator in China. Experimental results show that GDDPG effectively manages the sleeping states of up to 72 cells in a real-world environment. The experimental scenario is much more complex than those in other studies.

An improved network calculus (NC)-based model is proposed to analyze the upper-bound latency for time-sensitive networking (TSN). It aims to address two key issues existing in the benchmark NC model: on the one hand, the inaccurate description of the arrival curve (AC) for simultaneously arriving data packets; on the other hand, the design of the network service curve (SC) does not consider the interdependencies between nodes. In the simulation experiment section, the pessimism of theoretical analysis is reduced by comparing the two models, and the validity of the improved NC-based model is verified using the OMNeT++ platform.

It’s essential to achieve reliable transmission by accurately characterizing the delay upper bound in space-air-ground integrated network (SAGIN). Based on deterministic network calculus and time-aware shaping (TAS) mechanism in time-sensitive networking (TSN), a delay upper bound analysis model for low earth orbit (LEO) satellites was constructed. Firstly, a delay upper bound guarantee architecture for LEO satellite network (DGA-LEO) was developed using network calculus within the context of SAGIN. Next, a traffic model was formulated based on leaky bucket model, considering the worst-case impact of interference from different priorities, and then the service curve for a satellite node was established. For the same-priority interference, a new left-over service curve calculation method was proposed, and based on the convolution theory in min-plus algebra, an improved network calculus algorithm was developed. Finally, the proposed algorithm was tested under a defined LEO satellite network and traffic model, and the comparative analysis was conducted with three traditional algebraic network calculus algorithms. Experimental results demonstrate that the proposed algorithm achieves more accurate delay upper bounds while significantly reducing computation time.

The demands for industrial ubiquitous communications promote the development of real-time and high-reliability wireless communication techniques. Accurate time synchronization is a critical foundation for deterministic communications. However, many wireless time synchronization methods achieve poor accuracy, while others take the high hardware costs and can not be used in practice. How to design high precision wireless time synchronization method with reasonable hardware costs is still a big challenge. Therefore, without affecting Wi-Fi protocol stack, a new medium access control (MAC) layer-based approach is proposed in this article to implement precision time synchronization with an open-source Wi-Fi design. The software protocol stack only needs to send handshake messages carrying identifiers, and timestamps are inserted and extracted from handshake messages as they pass through the MAC synchronization architecture designed in field programmable gate array. In the single-hop synchronization experiment, the synchronization accuracy is tested with and without network load. Comparing with other methods in several literatures, the results of the proposed solution unequivocally demonstrate the effectiveness and excellent wireless time synchronization precision, with 99% absolute time synchronization errors under 50% and 100% loads within 200 ns and 1 mu s, respectively.

The digitization and intellectualization have been envisioned as the fundamental basis for future Industrial Internet of Things, which integrates sensor technology, industrial control technology, communication technology, and artificial intelligence (AI). Specifically, the collaboration among these above techniques is crucial for the successful implementation of intelligent applications. This article develops an end-to-end visual control framework to accomplish multicrane collaborative sorting in wireless time sensitive networking (TSN) networks. The design primarily incorporates field devices, data transmission, AI, and industrial control. An advanced binocular stereo visual recognition model based on deep learning is investigated to accurately obtain the world coordinates and types. A cooperative control scheduling model that combines a scheduling strategy with an anti-collision strategy is presented to effectively control multiple cranes for sorting tasks. The device data and commands are transmitted through industrial 5G-TSN integrated network for ultrareliable, low-latency, and deterministic transmission. The proposed visual sorting system is further validated through the establishment of an experimental prototype, demonstrating its exceptional real-time performance while enabling flexible intelligent manufacturing.

5G delivers ultrareliable low-latency communications and mobile edge computing (MEC) for industrial applications, enabling distributed computing resource collaboration and promoting traditional automation systems toward networked control paradigms. However, industrial networked automation systems face significant challenges in optimally allocating limited computing and communication resources to meet strict QoS requirements for massive control tasks while maintaining system stability and efficiency. Thus, we propose a computing-communication-control collaboration mechanism to enhance coordination between MEC and local controllers, enabling complex control tasks to be processed despite limited local computing resources. To jointly optimize hybrid control tasks migration with communication constraints and computing resources allocation with incomplete information, we design a dual double deep Q-network embedded with the Stackelberg game (SG) model. Simulation results demonstrate that the proposed mechanism achieves better performance compared with other benchmarks, and extremely decreases the convergence time compared with the classical SG solution.

Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios. We compare synchronous, asynchronous, hierarchical, and heterogeneous FedSL frameworks in terms of workflow, scalability, adaptability, and limitations under dynamic industrial conditions. Furthermore, we systematically categorize token fusion strategies into three paradigms: input-level (pre-fusion), intermediate-level (intra-fusion), and output-level (post-fusion), and summarize their respective strengths in industrial applications. We also provide adaptive optimization techniques to enhance the efficiency and feasibility of FedSL implementation, including model compression, split layer selection, computing frequency allocation, and wireless resource management. Simulation results validate the performance of these frameworks under industrial detection scenarios. Finally, we outline open issues and research directions of FedSL in future smart manufacturing systems.