In industrial wireless networks with resource-constrained and densely deployed devices, link scheduling is a challenging task. Traditional optimization methods have high computational complexity and low scalability. Graph learning offers a promising approach, yet it also comes with limitations of capturing multivariate relationships from interference, leading to ineffective link scheduling. In this article, hypergraphs are additionally introduced to model cumulative interference from concurrent transmissions. Considering the constructed comprehensive interference model, we propose a Lightweight link scheduling algorithm based on hybrid binary Graph and HyperGraph representation learning (L-GHG) in an unsupervised manner. Thereinto, the L-GHG algorithm extracts and fuses features from various interference relationships for link scheduling decisions. A graph partitioning based on channel state information is proposed to reduce redundant search space arising from cumulative interference. Simulations demonstrate that our proposed algorithm outperforms benchmark schemes regarding generalizability and scalability. We also validate the availability of the proposed algorithm in practical experiments.

Performing unstructured grasping tasks in cluttered or obstacle-rich food processing environments is a key challenge in robotic systems. This work presents a task-adaptive grasping approach for a dual-arm anthropomorphic robot, named Herdsman, developed for the food industry. With an articulated torso, Herdsman is able to perform human-like reaching motions for more flexible grasping operations. To recognize the target object and extract the features for grasping, a vision pipeline, including a lightweight network GDC-YOLO for real-time object detection and a U-ReSENet network for grasping detection enhancement, is designed based on convolutional neural networks. After the detection comes the grasp execution, where a task-adaptive grasping strategy that works with the articulated torso is put forward to carry out grasping tasks in unstructured environments. Comparative experiments are designed to evaluate the detection performance between the proposed network and other popular networks for object detection and grasping detection. In addition, the task-adaptive grasp strategy for the Herdsman robot is experimentally validated by grasping the objects at different heights. The results have shown that the task-adaptive grasping solution exhibits robustness against variations in the target object position, which could be a promising approach for its application in unstructured environments requiring autonomous grasping.

Industrial Control Systems (ICSs) are the core of industrial production. Wireless technology, with its flexibility and adaptability, is catalyzing a transformative shift from traditional ICS to the advanced Industrial Wireless Control Systems (IWCSs). However, the openness of wireless media, high dynamics of the environment, and resource scarcity present unprecedented security challenges of high security defense costs and low detection inaccuracy for IWCS. State-of-the-art methods primarily treat ICS as a typical cyber-physical system, which focuses on security issues from the cyber and control domains, rather than the physical domain. As a result, they are unable to fully address the high dynamics of wireless channels and unknown attacks, ultimately failing to meet the stringent security requirements of industrial systems. To this end, this paper proposes a physical-domain whitelist as the final line of security defense leveraging the finite nature of the physical behavior space in industrial production systems. Moreover, a holistic cross-domain security-safety architecture is introduced, drawing inspiration from the integrated cyber-control-physical collaboration. In the proposed architecture, the top-down inherent security-safety defense and bottom-up risk backtracking form a close loop, which not only prevents unknown attacks but also facilitates rapid localization and response to attacks. In the experiment, the composite AGV scheduling control has been developed to verify the effectiveness of the architecture. Ultimately, the potential challenges of the cross-domain architecture for IWCS safety-security defense have been summarized.

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot.

Physical layer authentication (PLA) is a promising approach to ensure wireless network security. However, existing PLA algorithms require channel state information calibration and model training at each location, which limits the device's mobile ranges. To enable zero training deployment in uncalibrated scenarios, we propose a transferable PLA (TPLA) algorithm. It exploits the channel time-varying patterns as the scenario-independent features to avoid PLA failure in uncalibrated scenarios. To extract transferable time-varying patterns from dynamic industrial mobile environments, a neural network feature extractor is designed by a multibranch parallel architecture with multiscale channel receptive fields. Furthermore, the global and component features are fused by the model-level and decision-level fusion methods to accommodate different transferability and computational cost requirements. In experiments, TPLA achieves below 2.5% authentication error in a new scenario, which proves that TPLA is an important step toward zero training deployment of the PLA algorithm.

Channel-state information (CSI)-based physical-layer authentication (PLA) has gained significant attention. However, in industrial mobile scenarios, the time-varying channels and changing device locations limit the reliability of CSI-based PLA algorithms. Furthermore, the performance of existing PLA algorithms degrades sharply at uncalibrated locations. To improve the reliability and robustness of authentication, we propose a new spatiotemporal gradient-based-PLA (STG-PLA) algorithm enhanced by CSI-to-image transformation. We first extract correlation and scattering features to depict the multidimensional channel properties, including selectivity and dispersion. We then convert several individual CSI sequences to a CSI-image. Therefore, the spatiotemporal correlation gradient of the CSI-sequences is reflected in one CSI-image. Both simulations and experiments show that STG-PLA reduces the authentication error rate from$>10{\%}$(in existing studies) to$< 1{\%}$, which signifies considerable progress toward the practical applicability. Furthermore, with no model retraining, STG-PLA exhibits the robust performance when the device moves to uncalibrated locations.

The Industrial Internet of Things (IIoTs) plays an important role in various industrial applications, which require multiple time-critical networks to be deployed in the same region. The limited communication resources inevitably incur network coexistence problems. For scenarios where coexisting networks cannot coordinate effectively, the centralized or partial-information-based decentralized resource allocation methods cannot be implemented. To address this concern, we propose a Cooperation-Free Reinforcement Learning (CF-RL) algorithm for the fully distributed resource allocation problem in coexisting IIoT systems. Each network adopts the proposed algorithm to minimize collisions through a trial-and-error approach without any information interaction. To resist the influence of environmental dynamics, each coexisting network learns the state transition probability of the resource block instead of the resource block's position. Moreover, to potentially ensure the overall system performance, each network additionally considers the period offset in the initialization phase and action selection phase, so that the coexisting networks have different preferences for different state transitions. We conduct extensive simulations to verify the convergence performance. Evaluation results show that the CF-RL algorithm almost achieves (more than 99.88%) the effect of centralized resource allocation and has obvious superiorities over other cooperation-free algorithms in terms of the convergence rate, the number of collisions, and the resource utilization ratio.

Conventional wireless automated guided vehicle (AGV) systems based on WiFi or ZigBee suffer random network performance and fail to guarantee the ordered and reliable transmission in AGV applications. In this article, we study the transmission scheduling with order constraints in WIA-FA-based AGV systems. We first present the transmission process of data packets in the WIA-FA-based AGV system, and design a novel superframe structure to support the ordered data exchange in the AGV system. Then, we design three heuristic rules for timeslot allocation to regulate the device transmissions subject to order constraints. Finally, inspired by the designed rules, we propose a dynamic expected packet loss rate-based timeslot allocation (DELTA) algorithm and prove its time complexity rigorously. The simulation results show that the proposed DELTA algorithm outperforms the existing works in terms of transmission reliability for different channel conditions and network scales.

WIA-FA and IEEE 802.11 are two most widely adopted industrial wireless standards in discrete manufacturing. However, comprehensive performance comparisons between WIA-FA and IEEE 802.11 are still missing and industrial applications urgently need experimental methods to guide the selection of appropriate wireless technologies. To this end, this article performs extensive experiments between WIA-FA and IEEE 802.11 in two practical industrial scenarios, with one ordered scenario defining the transmission order of devices and the other order-free scenario imposing no order constraints to the transmission order of devices. Network performance indices of the WIA-FA and IEEE 802.11 networks, including reliability, delay, jitter, and disorder rate, are compared for different network sizes and data generation periods. Experimental results show that the WIA-FA protocol provides stable network performance, while the network performance of the IEEE 802.11 protocol is random and uncontrollable. Additionally, we perform preliminary comparisons of WIA-FA with IEEE 802.11ax and 5G New Radio.

Concordant operations among automatic industrial devices (i.e., agents) play a significant role in achieving efficient smart manufacturing. Existing multiagent cooperation methods focus on centralized training with decentralized execution, which is unsuitable for the edge-based distributed industrial scenarios. To harmonize distributed devices' actions and enhance the production efficiency, in this article, we propose an Interactive-Imitation-based Distributed Coordination (IIDC) algorithm. Specifically, we leverage the generative adversarial imitation learning (GAIL) model to direct one agent's actions by following external professional demonstrations. We also adopt the self-imitation learning (SIL) model to direct one agent's potential actions by following its own previous good experiences. As the imperfect demonstrations in the GAIL-based interagent imitation process may degrade the imitation accuracy, we present a confidence-based matching method to reduce the gap between the professional and imitative behaviors. Furthermore, during the SIL-based intra-agent imitation process, the rewardless explorations also lead to nonoptimal imitation policy. We then present a Stein variational policy gradient based self-imitation method to learn an expected optimal policy. We validate the IIDC algorithm's effectiveness via the sequential assembly task. Evaluation results demonstrate that the IIDC algorithm can enhance the production efficiency evidently.

In industrial wireless sensor networks (IWSNs), monitoring data generated by field devices are supposed to be delivered to the gateway with low latency and high reliability. However, most of industrial wireless standards are based on IEEE 802.15.4 and offer limited data rates, which prevents their adoption in critical scenarios. Based on IEEE 802.11, WIA-FA is proposed to address higher communication requirements in factory automation. In this paper, we first analyze the drawbacks of the default NACK-based retransmission scheme of WIA-FA, and then propose an automatic on-demand retransmission (AODR) scheme. Finally, we give a detailed reliability analysis of the proposed AODR scheme. Simulation results show that the proposed AODR scheme outperforms existing works in terms of reliability for different scenarios.

The limited spectrum resources inevitably incur the spectrum sharing among coexisting industrial wireless networks (IWNs), and multiple coexistence IWNs form a heterogeneous environment. An effective resource allocation thus plays a crucial role in coordinating the efficient operations of multiple IWNs. Existing works only study the constrained coexistence problem among specified types of networks with a limited number of nodes over one single channel. In this paper, we investigate a general coexistence problem over multiple channels among arbitrary types of networks with line topologies, and the number of nodes in each network is also arbitrary. We rigorously analyze theoretical scheduling latency of this general coexistence problem, then we propose an algorithm to attain the optimal result. The presented Coexisting Line topology Networks Resource Allocation (CLNRA) algorithm consists of two phases. In the inter-network resource allocation phase, non-overlapped channels are allocated to each network according to the corresponding transmission priority. While in the intra-network resource allocation phase, we filter out the nodes that may generate continuous empty buffers so as to enhance the resource utilization ratio. We also verify the effectiveness of the CLNRA algorithm through extensive simulations. Evaluation results show that the CLNRA algorithm can attain the theoretical optimal result in 99:3% cases, and it has obvious superiorities on resource utilization ratio and scheduling latency.

In industrial wireless sensor networks (IWSNs), monitoring data generated by field devices should be delivered to the gateway prior to deadlines. Traditional field devices with one radio interface can only work in the half-duplex mode, which may cause severe degradation of the network real- timeliness. Considering the scenarios where each field device is with multiple radio interfaces, we study the joint scheduling of slots, channels and radio interfaces in IWSNs with mesh topologies. Specifically, a new method to calculate the total and remaining resource blocks of each transmission is first given. Then, a two-level priority assignment rule is designed by jointly considering remaining resource blocks and deadlines. Finally, a remaining resource blocks based least laxity first (RRBs-LLF) algorithm based on the above rule is proposed. Simulation results show that the proposed RRBs-LLF algorithm outperforms existing works in terms of schedulable ratio.

Intelligent factory automation systems strongly rely on industrial wireless control networks which have to ensure timely and reliable data exchange among their components. This paper presents a comprehensive survey on recently approved International Electrotechnical Commission standard Wireless networks for Industrial Automation-Factory Automation (WIA-FA). This paper first introduces the system architecture of WIA-FA including network device, network topology, and system management, and then illustrates WIA-FA protocol stack and key technologies. Furthermore, two WIA-FA testbeds are described to demonstrate the high performance of WIA-FA. After that, three examples of practical applications are provided in this paper. One application deploys a WIA-FA network to monitor and control industrial robots in a digital workshop. The second application adopts the deployment of WIA-FA as a real-time wireless network that connects automated guided vehicles (AGVs) in a logistic sorting system. The last application coordinates multiple cooperative AGVs via the WIA-FA network to carry large and complex components. Finally, the open issues and future directions for WIA-FA networks are presented.