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.

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 multi-crane collaborative sorting in wireless time sensitive networking (TSN) networks. The design primarily incorporates field devices, data transmission, artificial intelligence (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 ultra-reliable, 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 ultra-reliable 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 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 Stackelberg game solution.

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.

IEEE 802.1 Time-Sensitive Networking (TSN) is an emerging and promising communication solution offering benefits for Industrial Internet. TSN can provide deterministic latency and ultra-reliability guarantee for automation control information in multi-traffics scenario. However, the routing and scheduling methods, which have important effects on system performance, are not covered by TSN standards. Therefore, in this paper, ajoint routing and scheduling model based on K-shortest-path (KSP) and swarm intelligence is proposed. The model effectually reduces end- to-end latency caused by link congestion and improves scheduling feasibility for multiple traffics in TSN domain. By contrast experiments under different circumstances, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are analyzed and compared. The simulation results indicate that the proposed model based on improved ACO achieves better performance on scheduling success rate and end-to-end latency guarantee, while PSO has higher scheduling efficiency in terms of optimization results and iterative convergence rate.

We develop a deep-learning-based multicrane visual sorting system with virtualized programmable logic controllers (PLCs) in intelligent manufacturing, which enables the accurate location and suction of the materials on the conveyor belt. First, virtualized PLCs are deployed in the field and the cloud to break data islands for efficient communication between low-level devices. Second, artificial intelligence algorithms are integrated into the physical industrial control system in which cooperation between virtualized PLCs and the visual recognition model is developed to complete the industrial control closed loop. Third, we establish a visual recognition model in which object detection algorithms are used to process the original image and then obtain the position and type of the object in the pixel coordinate system. In addition, a new linear interpolation-based backpropagation neural network is presented to provide the transform relation between the pixel coordinate system and the world coordinate system that the crane needs to precisely suck the material. The whole system is applied in a time-sensitive network environment in a highly reliable and stable manner. The experimental prototype system demonstrates that high recognition accuracy can be achieved for the visual sorting system within an acceptable time frame. The accuracy of the sorting task reaches 96.5% and the average consumption time of each object is approximately 2.317 s when the speed of the conveyor belt is 5.2 m/min.

Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.

In 3GPP LTE-Advanced networks deployed with type I relay nodes (RNs), resource partition is required to support in-band relaying. This paper focuses on how to partition system resources in order to attain improved fairness and efficiency. We first formulate the generalized proportional fair (GPF) resource allocation problem to provide fairness for all users served by the evolved node B (eNB) and its subordinate RNs. Assuming traditional proportional fair scheduling is executed independently at the eNB and each RN to achieve local fairness, we propose the proportional fair resource partition algorithm to tackle the GPF problem and ensure global fairness. Through system level simulations, the proposed algorithm is evaluated and compared with both non-relaying and relaying systems with the fixed resource partition approach. Simulation results demonstrate that the proposed algorithm can achieve a good trade-off between system throughput and fairness performance.

This paper focuses on how to associate users with serving nodes in order to achieve improved fairness and efficiency for the cellular networks enhanced with multiple relay nodes (RNs). We first formulate the generalized proportional fair (GPF) problem with the aim of providing fairness for all users served by the evolved node B (eNB) and its subordinate RNs. Routing is incorporated into the GPF problem as part of the resource allocation strategy. Assuming traditional proportional fair (PF) scheduling algorithm is executed independently at the eNB and each RN, we propose efficient PF-based routing algorithm aiming at optimizing the GPF objective, which also takes into account the impact of relay link resource allocation. Through system-level simulations, the proposed algorithm is evaluated and compared with both non-relaying and relaying systems with benchmark routing algorithms. Simulation results demonstrate that the proposed algorithm can achieve better system throughput and fairness performance.