Time-Sensitive Networking Over 5G for Industrial Control Systems

The benefits of the currently evolving IEEE Time-Sensitive Networking (TSN) standard have already been globally recognized. Whereas the application of TSN in a LAN is currently widely and globally tested, TSN in a Metropolitan Area Network (MAN) has not been a major focus until now. The possible benefits of utilizing co-located Edge Clouds in order to support multiple urban production sites with industrial realtime applications open a wide range of new business models. Therefore, we have analyzed the feasibility of transparently using PROFINET over TSN via a Dense Wavelength Division Multiplex (DWDM) link, where a machine park is controlled remotely by an Edge-based virtual Programmable Logic Controller (vPLC). As a result, we are able to setup a TSN connection over a MAN with a one-way delay of about 156.5 J.μs and a jitter of about 12 ns. This work can be extended to allow for dynamically provisioned TSN flows and multi-path Frame Replication and Elimination (FRER) for distributed hard real-time machine control and adoption to Ultra-Reliable Low-Latency Communication (URLLC) 5G campus networks.

Ultra-Reliable Low Latency Communication (URLLC) is the current requirement of today's vertical industry as per 3GPP 5G standard such as smart or automotive industry, smart vehicle, smart home, remote surgery for eHealth and cloud robotics for Industry 4.0. These systems are based upon network infrastructure which will handle time critical data traffic. In Release 16, 3GPP has made an integration of the 5G system with IEEE 802.1 working group specifications covering Time Sensitive Networking (TSN). Organizations are presently utilizing this standard in their applications over different wired and wireless networks like ethernet, fiber and 5G networks. In this paper the authors have presented their successful utilization of TSN in their 5G 28 GHz radio link and tested the radio link with different use cases. They are also exploring the different systems available with them starting with PC processor and bluestacks/chromecast software to launch their applications over 5G link. Further, they are exploring the use of multiprocessor based equipments for the upgradation of their system. The inclusion of NPN(Non Public Network) in Release 16 encourages the developers/users to implement their smart 5G network without taking any resources from MNOs (Mobile Network Operator). The authors are emulating NPN and TSN assisted 5G catering for smart system like smart home and vehicle.

Extended-Reality Technologies: An Overview of Emerging Applications in Medical Education and Clinical Care.

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.

Many industries, including the automotive industry and industrial automation, have a need for reliable real-time communication. To satisfy this need, industry-specific, often proprietary, solutions have been developed in recent decades. Time-sensitive networking (TSN) is addressing a grand unification of these existing technologies to leverage cross-industry cost efficiency as well as to guarantee a stable growth path of communication capabilities. While the functionality of time-sensitive networking is exhaustively standardized in IEEE 802.1, configuration standards for TSN are largely missing. Thus, in this article we review the IEEE TSN standards, in particular 802.1Qbv, and continue to illustrate a reference model for traffic planning for time-sensitive communication. Furthermore, the article references a detailed technical report describing the complete model, which is ideally suited as a starting point for standardization and ensures the interoperability of future TSN traffic planning tools.

Time-Sensitive Networking (TSN) encompasses standards to facilitate near-deterministic performance in wired networks and the integration of TSN with modern wireless systems, such as Fifth Generation (5G) systems, is intensely pursued. Consequently, TSN is examined by numerous research groups. Given the limited and costly access to TSN hardware, a reliable simulator becomes crucial. OMNeT++ is a modular simulator that can be expanded with network simulation models, such as INET, which can simulate TSN functionalities. We evaluate the accuracy of the OMNeT++ INET simulator in mirroring the physical (hardware-based) reality by comparing OMNeT++ INET simulations with measurements of two commercial TSN switches. Our evaluations encompass the generalized Precision Time Protocol (gPTP) accuracy, the Store-and-Forward (SF) and Cut-Through (CT) switching forwarding latencies, the Time Aware Shaper (TAS), and Frame Preemption (FP), including combinations of CT, TAS, and FP. We find that compared to hardware measurements, INET simulations exhibit different clock synchronization dynamics, underestimate the switching latencies, and do not support CT with FP. We enhance the alignment of INET simulations with authentic hardware behaviors by modifying INET modules. We modify the INET gPTP synchronization model and the INET simulation models for the SF and CT forwarding latencies. Also, we modify the INET simulation modules to support the combined operation of CT and FP. We demonstrate how our INET modifications, which we make publicly available, accurately simulate the behaviors of real TSN hardware.

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.

Cyber-physical systems are realized as those systems where the computation, communication and physical processes interact with each other (e.g., robotic systems, automotives, etc). A large class of cyber-physical systems are associated with strict timeliness requirements. They are known as ‘real-time systems’ (e.g., smart grids, avionics, etc). Industrial control systems (ICS) are real-time systems that consist of several closed-loop controls. The main components of a control loop are — a physical plant, sensors, actuators and a controller. The sensors transmit the measurement data to the controller that generates appropriate control signals to actuate the physical plant. Large-scale wireless sensor actuator networks form the main communication framework among the network devices in ICS. Most of the communications between each of these devices are periodic real-time flows with hard deadlines. To ensure reliability, the communication in these systems are time-division multiple access (TDMA) based. Dedicated network resources (time-slots and frequencies) are allocated to these devices in advance and the communication schedule is pre-computed to satisfy the hard deadlines of the real-time flows. The same schedule is repeated over time which makes the schedule predictable in nature. However, a malicious attacker can exploit this predictable time-slots in the schedules to launch timing attacks. Since these applications are time-critical, timing attacks can completely undermine the system performance leading the system to an unsafe state. Schedule randomization is a popular defense mechanism to mitigate timing attacks. Although a few works exist in the literature that use schedule randomization as a countermeasure against timing attacks in real-time uniprocessor systems, none of them are applicable for real-time wireless networks. Schedule randomization as a countermeasure against timing attacks in real-time wireless networks remained largely unexplored. With this objective in focus, this thesis addresses some schedule randomization techniques to mitigate timing attacks in real-time wireless networks. Among the existing wireless network protocols that are in use, the WirelessHART is the most suitable and widely adopted protocol in ICS. A WirelessHART protocol supports centralized architecture, TDMA based communication, multiple channels, etc. All of these characteristics of a WirelessHART network facilitate reliable and predictable communication with real-time flow guarantees in ICS. However, the predictable communication exposes the system to timing attacks, such as selective jamming attacks. Selective jamming attacks are stealthy attacks, however, it can lead the system to an unstable state and can disrupt the safety of the system. As a countermeasure against such attack in WirelessHART network, we propose a centralized schedule randomization technique that randomizes the time-slots and channels in the schedule over every hyperperiod (least common multiple of the periods of the real-time flows) without violating the hard deadlines of the real-time flows, while still satisfying the feasibility constraints of a schedule in a WirelessHART network. The centralized technique generates the schedules offline and distributes the schedules online, hence, cannot support any change in topology in the network. Further, this technique has energy overheads in distributing the schedules to all the network devices at runtime. Hence, we propose a distributed online schedule randomization technique that can generate random feasible schedules at runtime in each network device without affecting the closed-loop control stability. To increase the extent of randomization of time-slots in the schedules, this online distributed technique adopts a period adaptation strategy that can adjust the transmission periods of the real-time flows at runtime depending on the stability of the closed-loop controls. To support an ever-growing number of devices and dense connectivity, the fifth generation (5G) cellular networks is expected to serve as the main communication standard in the future wireless sensor networks. Among the different service categories supported by 5G, the ultra-reliable low-latency communication (URLLC) is mostly suitable for time-critical applications such as the ICS. The communication in 5G is organized into slots over multiple frequencies. To satisfy the hard deadlines of the URLLC flows in ICS, a part of the resources (slots and frequencies) in 5G are reserved for URLLC traffic and the same schedule is repeated over time. The repetition of the same schedule over time makes the slots in the schedules predictable. This, in turn, makes the 5G networks vulnerable to timing attacks. However, the proposed schedule randomization techniques for WirelessHART networks are not applicable for dynamic networks like 5G where the number of flows and the amount of available network resources for URLLC flows vary at runtime. Moreover, the feasibility of the real-time flows need to be guaranteed at runtime. Hence, we propose an online schedule randomization technique that randomizes the slots and the frequencies of the periodic URLLC flows while guaranteeing the feasibility of the flows at runtime.

Time-sensitive networking, ultra-reliable low latency communication, and industrial 5G

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.

Time-Sensitive Networking (TSN) is an emerging technology, which enables advancements in applications like industrial automation, automatic vehicle-to-vehicle communication, etc. which hosts various time-critical applications, ensuring bounded latency. The novel idea of this paper is to present OMNET++ simulation-based complex multi-hop TSN network using the native VLAN concept to bring out a cost-effective model for inter-TSN and Intra-TSN domains. This paper investigates the performance of hybrid IEEE standards, ie.IEEE 802.1Qbu and IEEE 802.1Qbv standards. The simulation results show that the combination of these standards, when effectively scheduled in switches will reduce the latency by 3.3 µseconds in time-critical applications. Further, it is observed that in Best effort traffic, frame loss is also very less in the range of 2-5 frames out of 1385 frames. These results certainly will be of great value in more complex TSN deployments.

As Industry 5.0 progresses, the demand for zero-touch configuration in industrial automation and smart manufacturing is increasing. This paper proposes a dynamic configuration management framework for Time-Sensitive Networking (TSN), aiming to address the challenges of flexibility and adaptability in dynamic network environments. A zero-touch configuration model is presented for TSN by incorporating a Delay-Aware Shortest Path Search (DASPS) algorithm to improve scheduling success rates. Simulation results demonstrate the ability of the framework to reconfigure networks within 2.67 milliseconds. The DASPS algorithm achieves a scheduling success rate of 70.22% for 1000 TSN flows, in contrast to only 22.23% achieved by the Shortest Path Search (SPS) algorithm. The proposed model effectively adapts to dynamic network changes, guaranteeing real-time data transmission. To further evaluate system adaptability, path entropy is introduced as a metric to quantitatively assess the balance of scheduling outcomes under topological changes. In the event of link failures, path entropy experiences a sharp decline but rapidly recovers after reconfiguration, demonstrating the system’s strong self-healing capability.

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].

Frame Replication and Elimination for Reliability (FRER) in Time-Sensitive Networking (TSN) enhances fault tolerance by duplicating critical traffic across disjoint paths. However, always-on FRER configurations introduce persistent redundancy overhead, even under nominal network conditions. This paper proposes a predictive FRER activation framework that anticipates faults using a Key Performance Indicator (KPI)-driven bidirectional Long Short-Term Memory (BiLSTM) model. By continuously analyzing multivariate KPIs—such as latency, jitter, and retransmission rates—the model forecasts potential faults and proactively activates FRER. Redundancy is deactivated upon KPI recovery or after a defined minimum protection window, thereby reducing bandwidth usage without compromising reliability. The framework includes a Python-based simulation environment, a real-time visualization dashboard built with Streamlit, and a fully integrated runtime controller. The experimental results demonstrate substantial improvements in link utilization while preserving fault protection, highlighting the effectiveness of anticipatory redundancy strategies in industrial TSN environments.

The convergence of 5G and Time-Sensitive Networking (TSN) offers a powerful foundation for enabling ultra-reliable, low-latency, and deterministic communication across a wide spectrum of emerging applications. While prior surveys primarily concentrate on industrial automation, this work presents, to the best of our knowledge, the first comprehensive survey that systematically investigates the potential of integrated 5G-TSN in four critical domains: industrial automation, intelligent transportation systems, smart energy systems, and digital health. We begin by providing a detailed background and comparative analysis of 5G and TSN technologies, highlighting recent advancements and complementary capabilities. The paper then presents a deep dive into an integrated 5G-TSN system architecture, with particular focus on time synchronization, traffic scheduling, QoS mapping, and cross-domain resource coordination. Building on this technical foundation, we introduce a structured, application-specific analysis that maps the communication requirements, challenges, and domain-specific integration strategies to the corresponding enabling 5G-TSN mechanisms. Finally, we synthesize key technical challenges such as interoperability, end-to-end synchronization, heterogeneous QoS alignment, and unified security, and propose targeted research directions to support the practical, scalable deployment of integrated 5G-TSN for emerging applications.