Deterministic transmission guarantee in time-sensitive networks (TSN) relies on queue models (such as CQF, TAS, ATS) and resource scheduling algorithms. Thanks to its ease of use, the CQF queue model has been widely adopted. However, the existing resource scheduling algorithms of CQF model only focus on periodic time-triggered (TT) flows without consideration of bursting flows. Considering that the bursting flows often carry high-priority data in real systems, in this paper we investigate the mixed-flow (i.e., TT and bursting flows) scheduling problem in CQF-based TSN aiming to maximize the number of schedulable flows and system load balance while satisfying the deterministic demands of delay, jitter, and reliability for both TT and bursting flows. Unfortunately, it is challenging to schedule the mixed flows with the original CQF model because of the huge difference between TT and bursting flows. To resolve this problem, we firstly design an enhanced Multi-CQF model to satisfy the basic demands of bursting flows sent at any time without affecting the deterministic transmission of TT flows. Given the complexity of mixed-flow scheduling and the proposed queue model, it is difficult for traditional algorithms to fully utilize network resources. Thus, we further propose a time-correlated DRL resource scheduling (TimeDRS) algorithm to optimize the resource allocation. TimeDRS can be extended to other time-related resource scheduling scenarios, such as TDMA-based scheduling. Experimental results demonstrate that our proposed approaches can greatly reduce frame loss and end-to-end latency for bursting flows, and well balance runtime and schedulability compared with state-of-the-art benchmarks. 

Burst-Aware Time-Triggered Flow Scheduling With Enhanced Multi-CQF in Time-Sensitive Networks

The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things (IIoT) environments. Most of the industrial automation systems today are based on the traditional International Society of Automation (ISA)-95 model, with some recently transitioned to Cloud Automation systems. Latest developments in network connectivity technologies, Artificial Intelligence (AI), and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this paper, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative “Cloud-Fog Automation” paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic Connectivity, Deterministic Connected Intelligence, and Deterministic Networked Computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision. 

Cloud-Fog Automation: Vision, Enabling Technologies, and Future Research Directions

New applications are emerging that bring new and stricter requirements for WiFi. Smart factories, mobile and collaborative robots, and Extended Reality (XR) demand deterministic wireless connectivity with ultra-low latency. This article focuses on the challenges and enhancements supporting Time-Sensitive Networking (TSN) that enable WiFi to support usages that require deterministic operation. The article reviews existing and future time-sensitive applications and their connectivity requirements. It discusses the evolution of the TSN capabilities supported by various WiFi generations, from legacy standards to the latest 802.11ax specification and the ongoing progress in the 802.11be Task Group specific to deterministic operation. The article also discusses the open challenges for next generation WiFi, including ultra-low latency (sub-millisecond) with higher efficiency. The article concludes with a review of the TSN ecosystem activities toward interoperability testing and certification of WiFi TSN and discusses testbed results demonstrating the 802.11ax and 802.11be tools to achieve deterministic operation. 

WiFi TSN: Enabling Deterministic Wireless Connectivity over 802.11

Wireless Industry 4.0 applications typically have stringent latency and reliability requirements. Even though state-of-the-art Wi-Fi networks can reliably achieve single digit milliseconds latency, new emerging time-critical applications have requirements that current Wi-Fi cannot meet. In this paper, we present next generation Wi-Fi technologies and describe how they can be leveraged to enable three time-critical Industry 4.0 use cases: wireless industrial automation control, remote rendering in extended reality applications and cooperative simultaneous localization and mapping using autonomous mobile robots in a factory plant. 

Time-Critical IoT Applications Enabled by Wi-Fi 6 and Beyond

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time-Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications. 

Time-Sensitive Networking (TSN) for Industrial Automation: Current Advances and Future Directions

Mobile multi-robot systems are an integral component of highly automated factories of the future. Since mobile robots have limited on-board computing capability and battery capacity, there is increasing interest in exploring approaches that enable robots to effectively leverage wireless communications and Edge Computing solutions for perception, navigation, planning, coordination, and control. It is, however, a major challenge achieving precision, high-speed, co-ordinated actions between robots due to tight end-to-end latency, and safety requirements, especially while enabling time-sensitive data exchange over wireless networks and execution of computing workloads distributed across robots and the Edge system. The traditional approach of designing compute, communications, and control components in an Edge system as independent components, limits the capacity and scalability of computing and wireless resources and is therefore unsuitable to meet performance guarantees for energy and resource-efficient time-sensitive robotic applications. In this article, we discuss technical challenges in the context of two Edge Robotics use cases such as conveyer object pick-up and robot navigation, which are representative of time-critical control in IoT applications. We propose research directions grounded in an end-to-end system co-design paradigm and describe technology components such as virtualized robot functions, compute-communications-control co-design, Edge system co-simulation, safety and security aspects that are core to Edge Robotics. We also briefly outline future research directions that are necessary to pave the path toward factory-scale Edge Robotics systems.

Towards Factory-Scale Edge Robotic Systems: Challenges and Research Directions

Time-sensitive networking (TSN) is an emerging topic for the advancement of wireless networking for industrial applications. TSN, as defined under the umbrella of IEEE 802.1 working group standards, addresses issues related to providing deterministic communications over IEEE 802-based Local Area Networks (LANs). TSN was originally designed to support real-time audio/video applications over Ethernet providing better reliability and lower, more deterministic latency with traffic shaping capabilities. TSN has since expanded its scope and applicability to other applications such as those in industrial environments and automotive applications. Industrial examples include machine-machine communications for robot control, end-effector actuation, real-time sensing, and safety integrated systems. Applications utilizing a wireless local area network (WLAN) can also benefit from scheduling and traffic shaping as defined in the 802.1Qbv standard; however, factors such as clock stability, synchronization, resource requirements and protocol options come into play when selecting a schedule to support multiple application types on the same network. In this article, we present a scenario for a collaborative robot heavy lift operation, in which, two robots communicate over an IEEE 802.11 WLAN with TSN capabilities to lift a rigid body in three dimensions. Scheduling is performed using 802.1Qbv over WLAN with the robot operating system (ROS) used as the software middleware utilizing the transport control protocol (TCP). As a part of the research, we describe our process for schedule selection to accommodate the time-sensitive traffic of the robotic scenario while allowing an industrial internet of things (IIoT) high data rate traffic to coexist. We then provide an analysis of the impacts of TSN schedule selection on the operational performance of the collaborative robot application.

Operational Impacts of IEEE 802.1Qbv Scheduling on a Collaborative Robotic Scenario

The introduction of Time-Sensitive Networking (TSN) is revolutionizing real-time networks and time-critical applications. Recent advancements in this field extended the TSN capabilities to wireless technologies, giving rise to the concept of wireless TSN (WTSN). This paper focuses on the integration of wireless TSN with Software-Defined Networking (SDN) to enable dynamic network configuration and improve the performance of time-sensitive applications. We present a practical test environment that uses a hybrid network configuration consisting of wireless and wired TSN links. The primary objective is to evaluate the effectiveness of combining a TSN-capable network with an SDN controller. This setup enables dynamic configuration and routing within the system, allowing for prompt actions to address network issues, such as seamlessly re-routing data paths between the two links due to an increase in latency or packet loss. A measurement setup using OpenVSwitch in the wireless TSN domain is presented, along with the evaluation of time synchronization and dynamic route selection capabilities.

Evaluating the Integration of Wireless Time-Sensitive Networking with Software-Defined Networking for Dynamic Network Configuration

The future of autonomous systems will rely on the usage of wireless time-sensitive networks to connect mobile cyberphysical systems, such as Autonomous Mobile Robots (AMRs), to Edge compute platforms to offload computationally intensive workloads necessary to complete tasks. In the case of AMRs, due to their mobility, the offloading of expensive processes such as localization and tracking methods to the Edge computing infrastructure must also be done over dynamic wireless networks. In larger scale systems, the network and compute resource requirements can quickly become prohibitively large due to network traffic and heavy workloads and tight deadline requirements for proper execution of time-critical tasks. In this paper, we formulate the problem of jointly allocating network and compute resources for time sensitive systems as the state of the wireless channel changes over time. By characterizing a compute model for AMR workloads, we further demonstrate how the network and compute scheduling decisions can be serialized, thus making the optimal scheduling problem significantly more tractable, via the incorporation of a compute-utility aware network cost function. Simulation results of AMR systems in a Wi-Fi network demonstrate substantial gains over baseline scheduling methods in total resource efficiency.

https://ieeexplore.ieee.org/ielx7/8782711/8889399/09933616.pdf

Joint Resource Scheduling for AMR Navigation Over Wireless Edge Networks

Achieving time-critical control in Edge Robotics Systems over 5G

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