OSSR: Online Scalable Scheduling and Routing for Industrial Time-Sensitive Networking

As an emerging communication technology, time-sensitive networking (TSN) promises the real time and deterministic interaction of massive data in Industrial Internet of Things. However, it is challenging to schedule the time-sensitive flows timely and superiorly through the mechanism analysis for current TSN scheduling models, especially in complex industrial scenarios. In this article, we propose an analysis approach of flow sequences based on divisibility theory to characterize the flow conflicts and dependencies, which derives the scheduling flexibility based on flow position diversity (PD) and the equivalent flow judgment conditions for slot occupancy. Integrating the abovementioned derivation, a parallel computing framework with the generalized slot length is established to lower the scheduling complexity. Within each computing unit, an incremental scheduling algorithm with the flow judgment conditions and PD-based search boundary is proposed. It reduces the scheduling complexity further while maintaining load balance for the mixed transmission of periodic and aperiodic flows. To achieve the optimality of runtime and load balance, two PD-based flow sorting strategies are designed, respectively. The evaluation results show that compared with the existing works, the runtime efficiency of scheduling at scale is increased by at least 1500 times in complex traffic scenarios while the load balance on the network links is also improved.

More data and information is being captured from systems, machines, and devices and made available to industrial information technology (IT) systems. The information is processed on-the-fly, enabling IT-based management systems to generate updated information for real-time control of the manufacturing processes. This data capturing and collection for IT systems is often referred to as the Internet of Things (IoT). When adopted to the industrial requirements, such as robustness, reliability, timeliness, and security, it is often termed as the industrial IoT (IIoT) [A1]. IIoT has attracted the attention of both the industry and academia since it is expected to enhance day-to-day activities, create new business models, products, and services, and as a broad source of research topics and ideas. Meanwhile, it is envisioned that the fifth-generation (5G) networks will be a cornerstone in future wireless industrial connectivity, and currently, there are multitude of ongoing research efforts in their design and optimization. Future industries willembrace use cases with numerous wireless-connected sensors and devices, and judging by the demand, massive machine-type communication and ultra-reliable low-latency communication (URLLC) in the literature and standardization activities, have been identified as two of the three main communication scenarios for 5G. These scenarios demand intelligent, scalable, and robust radio access techniques, network architectures, and deployment options to meet industrial demands [A2]. Therefore, more in-depth research is needed for IIoT and sensor networks in 5G-and-beyond wireless communication systems to address various challenges, including the following. 1)Transmit power control policy should be judiciously designed to improve both the spectrum efficiency and energy efficiency effectively; higher transmit powers can improve reliability but increase the interference and battery consumption. 2)Low-latency communication and computing is one of the significant challenges in 5G-and-beyond IIoT; uploading the device data to the cloud computing centers has high latency and resources waste issues in sensor networks. 3)Addressing privacy and security problems [A3] in the 5G-IIoT is fundamental to the further development and spread of 5G-IIoT. 4)Reliability and latency requirements of URLLC services, requiring less than 1-ms user plane latency and higher than 99.999% reliability, are demanding to meet, especially in time-varying industrial wireless channels. 5)Radio resource allocation, sharing, and isolation with performance guarantees under dynamic traffic conditions are critical issues for emerging IIoT applications requiring real-time support of massive connected devices. 6)5G-and-beyond IIoT networks must satisfy industrial-grade coverage, capacity, time-sensitive networking, and over-the-air time synchronization requirements [A4].

The Industrial Internet of things (IIoT), the implementation of IoT in the industrial sector, requires a deterministic, real-time, and low-latency communication response for its time-critical applications. A delayed response in such applications could be life-threatening or result in significant losses for manufacturing plants. Although several measures in the likes of predictive maintenance are being put in place to prevent errors and guarantee high network availability, unforeseen failures of physical components are almost inevitable. Our research contribution is to design an efficient communication prototype, entirely based on internet protocol (IP) that combines state-of-the-art communication computing technologies principles to deliver a more stable industrial communication network. We use time-sensitive networking (TSN) and edge computing to increase the determinism of IIoT networks, and we reduce latency with zero-loss redundancy protocols that ensure the sustainability of IIoT networks with smooth recovery in case of unplanned outages. Combining these technologies altogether brings more effectiveness to communication networks than implementing standalone systems. Our study results develop two experimental IP-based industrial network communication prototypes in an intra-domain transmission scenario: the first one is based on the parallel zero-loss redundancy protocol (PRP) and the second one using the high-availability seamless zero-loss redundancy protocol (HSR). We also highlight the benefits of utilizing our communication prototypes to build robust industrial IP communication networks with high network availability and low latency as opposed to conventional communication networks running on seldom redundancy protocols such as Media Redundancy Protocol (MRP) or Rapid Spanning Tree Protocol (RSTP) with single-point of failure and delayed recovery time. While our two network communication prototypes—HSR and PRP—offer zero-loss recovery time in case of a single network failure, our PRP communication prototype goes a step further by providing an effective redundancy scheme against multiple link failures.

In the past decades, the evolution of Industrial Inter-net of Things (IIoT) and Industry 4.0 faces several challenges, and one of them is the proprietary network which causes a rising cost due to the interoperability, compatibility, and strict requirements. To deal with the challenge, industry advocates an open Ethernet-based standard under the IEEE 802.1 family, named Time-Sensitive Networking (TSN), to support real-time and critical applications, where the routing with TSN is still an open and ongoing research problem. Although there are some existing routing approaches, they are offline algorithms and thus not suitable for future industrial automation that needs to support dynamic reconfiguration. Also, they consider either only TSN traffic or only AVB traffic to determine the routing paths, which limits the overall solution space and fails to make the best use of TSN features. To address the issues above, we aim to explore an online routing problem considering both TSN and AVB traffic in a TSN network. It is very first to investigate the online routing problem considering both TSN and AVB traffic. We propose an Ant Colony Optimization (ACO) based approach with a low time complexity to solve the problem. Experimental results show that the proposed algorithm outperforms a well-known solution in terms of the schedulability, performance, number of rerouted streams, and computational efficiency.

Scalable routing in wireless sensor network (WSN) is required for future applications to provide high-quality and robust transmission. In this paper we propose a Polychromatic Sets based scalable Routing (PSR) scheme for WSNs. PSR is a hierarchical routing scheme designed to scale to the network size. This scheme firstly partitions the network into multiple clusters which are organized as a multilevel hierarchical model. Then intra-cluster or inter-cluster routing is applied to increase the scalability. The clustering techniques and routing scheme are validated by comparing with existing protocols. Simulation results clearly show the superior throughput and considerable low packets delay of PSR against AODV and FSR in terms of scalability and robustness to network size.

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

With the development of industrial Internet of Things, many new industrial networks are emerging. Among these networks, the standard of Wireless Networks for Industrial Automation-Process Automation (WIA-PA) is one typical specification for industrial wireless networking, while Time-Sensitive Network (TSN) is another typical specification for industrial wired networking. A factory network often consists of multiple kinds of networks, including wireless media and wired media. Therefore, effective protocol conversion among different industrial networks becomes more and more significant. To address this issue, this paper presents a conversion scheme between WIA-PA and TSN based on data flow mapping. The proposed scheme is composed of a set of conversion methods, including priority conversion method, flow identification conversion method, address conversion method, cross-network clock synchronization method, and bandwidth reservation method. The conversion mechanism is designed in detail and implemented on experimental platform. Test results show that the mutual conversion of data streams of the two networks is achieved effectively by the proposed scheme, while the key network characteristics such as the data stream priorities are maintained.

In the emerging industrial Internet of Things (IIoT) scenario, machine-to-machine communication is a key technology to set up environments, wherein sensors, actuators, and controllers can exchange information autonomously. However, many current communication frameworks do not provide enough dynamic interoperability and security. Hence, in this article, we propose a novel communication framework based on Message Queuing Telemetry Transport (MQTT) broker bridging, which, in an IIoT scenario, can foster dynamic interoperability across different production lines or industrial sites, guaranteeing, at the same time, a higher degree of isolation and control over the information flows, thereby increasing the overall security of the whole scenario. The solution we propose also supports dynamic authentication and authorization and has been practically implemented and evaluated in a proper small-scale IIoT testbed, encompassing PLCs, IIoT gateways, and MQTT brokers with novel and extended capabilities. The evaluation results demonstrate a linear time complexity for all the considered implementations and bridging modes of the extended brokers. Moreover, all considered access token encapsulation techniques demonstrate a minimum overhead in comparison with standard MQTT brokers.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

With the rapid development of the Industrial Internet of Things (IIoT) and intelligent manufacturing, massive amounts of heterogeneous and non-independent, identically distributed (non-IID) data are continuously generated in industrial environments. Large models have demonstrated strong generalization and transfer capabilities, offering new possibilities for predictive maintenance, anomaly detection, and intelligent decision-making in IIoT scenarios. However, the deployment of such models in industrial environments faces challenges due to resource constraints in communication and computation. To address this problem, this paper proposes a collaborative optimization framework that integrates client-side feature learning, a hierarchical client–edge–cloud federated aggregation, and network-computing resource scheduling for efficient large-model embedding training. A parameter search method based on the Kepler Optimization Algorithm (PSKOA) is introduced to jointly optimize the three interdependent dimensions: client-side model structure parameter, federated aggregation parameters, and scheduling strategy. Evaluations demonstrate that the proposed method significantly reduces model loss by 41.7% and shortens training time by 13.4% compared to the traditional Genetic Algorithm-based method. Additionally, the proposed method achieves 12.5% lower model loss and 3.1% faster training time compared to the Particle Swarm Optimization-based method. These results highlight that the proposed method effectively enhances both training efficiency and convergence performance by jointly optimizing communication, computation, and model structure, making it a practical and scalable solution for large-model embedding training in resource-constrained IIoT environments.

The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.

Connectivity is the one word summary for Industry 4.0 revolution. The importance of Internet of Things (IoT) and Industrial IoT (IIoT) have been increased dramatically with the rise of industrialization and industry 4.0. As new opportunities bring their own challenges, with the massive interconnected devices of the IIoT, cyber security of those networks and privacy of their users have become an important aspect. Specifically, intrusion detection for industrial networks (IIoT) has great importance. For instance, it is a key factor in improving the safe operation of the smart grid systems yet protecting the privacy of the consumers at the same time. In the same manner, data streaming is a valid option when the analysis is to be pushed from the cloud to the fog for industrial networks to provide agile response, since it brings the advantage of fast action on intrusion detection and also can buy time for intrusion mitigation. In order to dive deep in industrial networks, basic ground needs to be settled. Hence, this chapter serves in this manner, by presenting basic and emerging technologies along with ideas and discussions: First, an introduction of semiconductor evolution is provided along with the up-to-date hi-tech wired/wireless communication solutions for industrial networks. This is followed by a thorough representation of future trends in industrial environments. More importantly, enabling technologies for industrial networks is also presented. Finally, the chapter is concluded with a summary of the presentations along with future projections of IIoT networks.

5th-Generation (5G) and Time-Sensitive Networking (TSN) are regarded as competitive new technologies for future industrial networks; 5G-TSN collaboration transmission has drawn more attention because it can provide a guarantee of low-latency, ultra-reliable and deterministic transmission for time-critical automation applications. However, the methodologies of resource scheduling mechanisms in 5G and Time-Sensitive Networking (TSN) are quite different, which may lead to an inefficient Quality of Service (QoS) guarantee for deterministic transmission across 5G and TSN. Therefore, an efficient 5G-TSN joint scheduling algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed and analyzed in this article. The proposed algorithm takes both 5G radio channel information and the Gate Control List (GCL) state in the TSN domain into consideration, aiming to provide a latency guarantee for time-triggered applications across 5G and TSN as well as a throughput guarantee for video applications in 5G systems. The simulation results compare the latency and throughput performance of the proposed joint scheduling algorithm with several traditional 5G scheduling algorithms; meanwhile, several GCL setting methods are given to verify the impacts on latency and throughput performance within the proposed algorithm. The simulation results demonstrate that the proposed DDPG-based joint scheduling algorithm can significantly enhance the multi-application-carrying capability of 5G-TSN collaboration architecture.

Driven by the rapid technological advances, industrial Internet of Things (IIoT) has recently been embraced to enhance autonomous industrial processes. Since a huge diverse traffic would be generated by IIoT, the industrial processes would meet the challenges of spectrum scarcity and on-demand service requirements. Millimeter wave (mmW) and pervasive edge computing (PEC) technologies in 5G communication are available to deal with these requirements. In this article, a novel dual-band framework that integrates both mmW and microwave (μW) networks in PEC environment has been proposed, which locally performs joint resource allocation and power assignment over mmW and μW to meet IIoT devices' specific requirements. To consider the new prominent figure of merit in IIoT scenario, the scheduling problem is formulated as an optimization problem to minimize the IIoT energy consumption in real-time environment. A Lyapunov optimization technique has been applied for the objective function with low complexity and rapid convergence. To solve the NP-hard Lyapunov algorithm, we introduce a block coordinate descent method that decompose the Lyapunov problem into two nested subproblems over the mmW and μW networks. An initialization-free semidistributed scheme is proposed in mmW PECs, which not only requires little information exchange via the μW network but also achieves the global optimal solution. Numerical results are shown to demonstrate the effectiveness of our proposed algorithms and confirm our theoretical analyses.

Introduction. Industry 4.0 is a concept that is considered a new phase in the Industrial Revolution, closely related to the application of information technologies and the digital transformation of manufacturing. The main purpose is to be created a more holistic and more connected ecosystem, focused on supply chain management in industrial companies. Implementation of solutions in Industry 4.0 is mostly related to the concept of the Internet of Things (IoT). Mass deployment of this type of technology in industrial enterprises is the basis of the so-called Industrial Internet of Things (IIoT). Achieving interoperability in the IIoT requires the combination of two technologies: the Internet of Things and the Internet of People.
 Aim and tasks. This article describes the implementation of the concept of the Internet of Things in industrial enterprises, as a key technology factor for developing Industry 4.0.
 Results. A brief overview of the evolution of industrial production - from the beginning of the Industrial Revolution to the emergence of Industry 4.0 is made. The main principles for implementing Industry 4.0 solutions ensure that the entire production process is computerized. Industry 4.0 solutions are mostly associated with the concept of the Internet of Things (IoT) whose definition and essence are obtained in this article. Based on the various concepts of the IoT are presented solutions that can be used in the industry, namely: in consumer devices in technology used in public organizations in infrastructure applications in industrial applications, also called the Industrial Internet of Things (IIoT). Therefore, we can say that there is a significant potential for improving production processes as regards: optimization of operations, forecasting equipment support, inventory optimization, improving workers' security, shipping chain optimization, etc.
 Conclusions. The application of the Internet of Things in enterprises is an important and decisive step in the process of their digital transformation and transition to Industry 4.0. The interaction between humans and machines, carried out through Internet technologies, leads to the emergence of the Internet of Everything, which will be a basic concept in industrial production in the coming years. However, the role of man in the production process should not be completely eliminated, but solutions should be sought that support and intellectualize his work.