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.

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.

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.

Location-based service (LBS) is the core of internet of things (IoTs), which serves tracking, navigation and monitoring. The ubiquitous magnetic signals are temporally stable and spatially distinguishable, and can achieve high-precision and ubiquitous positioning results without additional infrastructure, which is favored by researchers and has become a major research hotspot. Although there has been extensive research in the field of indoor magnetic positioning, there is still room for optimization in terms of positioning accuracy and robustness. Aiming at the problem that the magnetometer is offset and susceptible to environmental interference, we propose an online magnetometer calibration algorithm without user perception. Aiming at the inconsistency of magnetic data spatial scale problem caused by differences in device sampling frequency and user walking speed, we leverage different scales to segment the magnetic data, extract the magnetic sequence features of the corresponding scales through Transformer, utilize the attention mechanism to score the weights of the different scale features, and finally fuse the multiple scale features for positioning. We conduct extensive and well-designed experiments on public datasets and self-collected datasets. The experimental results indicate that the proposed method effectively solves the magnetic spatial scale problem and improves indoor magnetic positioning accuracy.

This paper investigates the joint scheduling of cross-channel traffic resources in 5G-Time-Sensitive Networks (TSN) and proposes an adaptive prediction method suitable for cross-domain Channel State Information (CSI). Firstly, we analyze the 5G-TSN cross-domain data forwarding mechanism by leveraging the architecture of the 5G-TSN bridging network and combining the functions of 5G and TSN network elements. Secondly, we propose a representation method for the 5G-TSN cross-network wireless CSI, specifically the data transmission delay information, as a dataset for channel quality prediction. This serves as a data foundation for subsequent intelligent prediction. Next, to make better use of the local information of channel state and achieve fast convergence, we employ an Extreme Learning Machine Auto-Encoder (ELM-AE) prediction logic based on Deep Extreme Learning Machine (DELM) and introduce the Dung Beetle Optimizer (DBO) algorithm to improve the DELM regression prediction. We perform prediction and analysis of the 5G channel delay and TSN domain data transmission delay. Then, we use the 5G-TSN CSI, collected in practice, as the data source to train and test the wireless channel delay indicators, which helps form the 5G-TSN channel model. Finally, we build a laboratory transmission prototype test bed for 5G-TSN cross-network transmission and conduct end-to-end transmission delay testing based on the proposed offline-generated channel model. The results demonstrate that the channel prediction model enables the end-to-end delay to decrease to less than 5 ms, the cross-network time synchronization accuracy to reduce to less than 100 ns, and the relevant performance indicators to reach industry-leading levels.

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.

Signal detectors are quite important to attain the diversity of doubly-dispersive wireless channels. Detectors based on message-passing (MP) of factor graphs have been regarded as the way to achieve the near-optimal performance for OTFS. In this paper, by deriving the pattern of the multipath vectorized channel matrix of the orthogonal time frequency space (OTFS) system, it is shown that short girth (i.e. girth-4) may exist in the Tanner graphs, which will degrade the performance of MP detectors, especially with high modulation orders. By introducing interleavers at the transmitter and receiver, the vectorized channel matrix turns out to be a sparse upper block Heisenberg matrix, whose structure is beneficial for the computation of matrix QR decomposition (QRD). Successive interference canceling (SIC) detectors based on QRD and sorted QRD are constructed to eliminate the cross-symbol interference and improve the reliability of the symbol-level channel. Simulation results show that for 4QAM, the QRD-based SIC detectors can achieve about 4dB gain at 10−2 over the non-SIC detectors, while the sorted QRD-based SIC detectors can bring an additional 2dB at 10−3, which is only 1dB gap from the MP. For 16QAM, the sorted SIC detectors show superior BER performance than the MP method, and for 64QAM, the MP detector reaches the error floor while SIC detectors show their excellent performance in all configurations.

The use of cell-free massive multiple-input–multiple-output (MIMO) is regarded as a novel technique in the Industrial Internet of Things (IIoT) networks, and many studies have been reported on its cross-layer optimization, including random access and power allocation. Nevertheless, the cooperation of deep reinforcement learning (DRL) and cell-free massive lacks of deep study. In this article, a primal–dual deep deterministic policy gradient (DDPG) algorithm is designed to obtain cross-layer radio resource management, including power allocation in the physical layer and random access in the medium access layer. Different from the current studies, the random access and power allocation is formulated in cell-free massive MIMO IIoT networks, utilized by the stochastic ergodic optimization. In contrast to the stochastic policy gradient algorithm, a primal–dual DDPG algorithm is designed for the cross-layer optimization. Moreover, a multiagent primal–dual DDPG algorithm is proposed to different scenarios in the cell-free massive MIMO IIoT networks. Simulations are presented to verify the effectiveness of the primal–dual DDPG algorithm for random access and power allocation in the cell-free massive MIMO IIoT networks.

Affective services will become a research hotspot in artificial intelligence (AI) in the next decade. In this paper, a novel service paradigm combined with wireless communication in integrated satellite-terrestrial network (ISTN) is proposed. On this basis, an affective services computing offloading and transmission network (ASCTN) with a three-tier computation architecture is proposed, which is able to assist users to obtain affective computing services and regulate emotions. The optimization problem is investigated in the ASCTN, which is a discrete, non-linear, and non-convex problem with the limitation of computation ability of satellite and transmit power. Specifically, with the objective to minimize the cost utility related to latency and energy consumption, a joint affective services tasks computing offloading strategy, sub-channel, and power allocation algorithm based on dueling deep Q-network (Dueling-DQN) is proposed, which is in possession of better stability. The simulation results reveal the effectiveness of the optimization algorithm in terms of the cost utility in the ASCTN system.