The ongoing transition in industrial automation towards a highly interconnected architecture underscores the importance of time-sensitive networking (TSN). This paper addresses the implementation of a time-predictable software-based TSN-enabled network stack for mixed criticality traffic in TSN end stations. The findings demonstrate the network stack’s worst-case execution time (WCET) analysability, effective management of mixed critical traffic, and real-time transmission of UDP frames. Additionally, the stack offers modularity, configurability, and TSN support for transferring messages across different priorities using either UDP or TCP. Evaluation on the time-predictable open-source T-CREST platform yields representative WCET estimates comparable to related research. The paper concludes by outlining further research.

The change of industrial automation towards a highly interconnected architecture is still ongoing. Time-sensitive networking (TSN) is an essential element for this transition. This paper focuses on TSN end stations, mainly answering how to implement a software-based worst-case execution time (WCET) analyzable buffer management. The two-level segregated fit (TLSF) algorithm was chosen, implemented, and evaluated based on an asymptotic complexity analysis of dynamic storage allocation algorithms. The implemented and improved TLSF algorithm showed similar timing behavior as calculated when executed on a time-predictable platform. The paper concludes by outlining further research.

Ongoing changes in industrial automation aim to-wards a flat and highly interconnected architecture that includes end-to-end real-time enabled machine-to-machine communications. Several technologies, such as OPC Unified Architecture (OPC UA) publish-subscribe and time-sensitive networking (TSN), facilitate that change. While OPC UA PubSub and TSN can already provide real-time capabilities, the parallel operation with standard OPC UA client-server remains an open challenge. This article presents preliminary results for solving concurrent information model access between OPC UA PubSub and client-server. The results include the overall RT-TSN-OPC UA concept, an analysis of common concurrent data access mechanisms for their suitability, and identifying critical code segments in the open62541 OPC UA stack. The paper concludes by outlining further research focusing on implementing and evaluating a wait-and obstruction-free mechanism into open62541.

Cyber-physical systems are highly distributed, flexible, and closely connected to the physical world. State preservation is an essential aspect of operating cyber-physical systems. If stateful applications fail, the current state needs to be restored so that the system can operate again. With the entry of edge computing, applications can now be containerized close to the equipment and allow new use cases. The lack of persistent storage in containers to ensure statefulness requires external, centralized, or distributed solutions. This paper explores this spectrum by comparing three experimental implementations and indicates possible causes for state loss. The underlying aim is to provide a starting point for choosing which state preservation approach is most suitable for which application type. The paper concludes with future research steps.

Flexibility and reconfiguration of manufacturing systems have been subjects of research for almost half a century. In recent years, with the emergence of customized mass production as a goal of Industry 4.0 (I4.0), Reconfigurable Manufacturing Systems (RMSs) have experienced a renaissance. The objective of rapid reconfiguration of production facilities is a fundamental condition for the realization of customized mass production. Besides the configuration of machinery and their programs, this also affects the configuration of the safety system. In this paper, core characteristics of Reconfigurable Safety Systems (RSSs) are proposed, which extend the characteristics of RMSs. Furthermore, we explore use cases of discrete manufacturing and identify service groups and services that are needed for the technical implementation of rapid safety reconfiguration within RMSs in order to allow flexible, reliable, and safe operation of machinery.

Neural Networks are the state-of-the-art technology for environmental perception in applications such as autonomous driving. However, they require a large amount of training data in order to perform well, making the selection and annotation of sensor data a time-consuming and expensive task. Active learning is a promising approach to reduce the required amount of training data by selecting samples for annotation that are expected to improve the neural network the most. In this work, we propose a sequence-based active learning approach that selects sequences of consecutive frames instead of individual images. This allows to evaluate tracking algorithms and to reduce the annotation effort by interpolating labels between frames. Our approach is compared to a random sampling strategy as baseline. Over 15 iterations, both approaches select 1000 additional images for training in each iteration. The performance of the neural network trained on the data selected by our sequence-based active learning approach is compared to the performance of the network trained on the data select by the baseline approach. The results show that sequence-based active learning can reduce the required amount of training data by up to 25% while reaching a similar performance. Furthermore, sequence-based active learning can improve the neural network's overall performance by 2 % compared to a random sampling strategy. In this work, the proposed method was evaluated with a new dataset consisting of 15 scenes in railway environments. The dataset has 45,888 frames in total, 14,513 frames contain persons on railway stations or close to tracks.

Current industrial trends require flexible and reconfigurable manufacturing systems, with Cyber-Physical Production Systems (CPPS) playing a crucial role. However, the increased flexibility and decentralisation increase the risk of unexpected or emerging system behaviour. Message sequence charts (MSCs) have proven helpful in the early stages of system design to capture intended scenarios. This paper introduces a process that utilises MSCs to identify unexpected system behaviour. After synthesising the MSCs into finite state machines, the process suggests applying Kronecker algebra to verify intended and identify unintended scenarios. The process is illustrated by an example depicting an auto-lock mechanism. The paper concludes by outlining further research focusing on implementing the process and identifying emergence at runtime.

Industrial automation is changing towards a flat and highly interconnected architecture, with requirements for end-to-end real-time enabled machine-to-machine communications. Technologies such as time-sensitive networking (TSN) and OPC Unified Architecture (OPC UA) publish-subscribe provide the necessary features. While TSN is well explored, OPC UA's execution time behavior remains unknown. This article presents findings made while extending the open62541 OPC UA Pub Sub stack with the 802.1q VLAN tag to enable IEEE 802.1Qbv time-aware scheduling. The results include end-to-end timing measures and worst-case execution time analyses considering various payloads. Time-predictable T-CREST platforms host the publisher and subscriber, and a TSN network handles message transmission. The paper concludes by outlining further research focusing on dynamic memory access, buffer management, and the inclusion of non-priority access to the Ethernet port.

The Fourth Industrial Revolution, or Industry 4.0, aims to advance flexibility and reconfigurability in current production systems. This paper sets cyber-physical production systems in context with Industry 4.0 concepts and architectures. The combination points out the importance of the interplay between communication and component reconfiguration when changes occur. A solution that utilizes fog computing, container-based deployment and Kubernetes functionality is presented and evaluated in simplified reconfiguration scenarios. The findings show the feasibility and the challenges of the solution and point towards further research to successfully create reconfigurable cyber-physical production systems.

Worst-case execution time (WCET) analysis is a prevalent way to ensure the timely execution of programs in time-critical systems. With the advent of new technologies such as fog computing and time-sensitive networking (TSN), the interest in timing analysis has increased in industrial communication. This paper highlights experiences made while adjusting the publisher of the open62541 OPC UA stack to enable WCET analysis, following a simple process combined with the open-source platform T-CREST. The main challenges are the required knowledge about the code and the specific communication software characteristics like variable message sizes. Other findings indicate the need for other types of annotation for indirect recursion or callback functions. The paper provides the foundation for further research on adjusting the implementation of existing industrial communication protocols for WCET analysis.

Industrial automation is changing towards higher integration and seamless communication. A stepping stone is end-to-end real-time machine-to-machine communication, now becoming feasible with technologies such as time-sensitive networking (TSN) and OPC Unified Architecture (OPC UA) publish-subscribe. While TSN takes care of communication, the OPC UA stack's execution time behavior remains unknown. This paper highlights experiences made while adjusting the OPC UA subscriber of the open62541 stack for worst-case execution time (WCET) analysis. Two directly connected time-predictable T-CREST platforms hosting the publisher and subscriber delivered end-to-end timing measures validating the WCET estimates. The paper concludes by outlining further research with several time-predictable publishers and subscribers.

Converging Information Technology (IT) and Operations Technology (OT) in modern factories remains a challenging task. Several approaches such as Cloud, Fog or Edge computing aim to provide possible solutions for bridging OT that requires strict real-time processing with IT that targets computing functionality. In this context, this paper contributes to ongoing Fog computing research by presenting three industrial use cases with a specific focus on consolidation of functionality. Each use case exemplifies scenarios on how to use the computational resources closer to the edge of the network provided by a Fog Computing Platform (FCP). All use-cases utilize the same proposed FCP, which allows drawing a set of requirements on future FCPs, e.g. hardware, virtualization, security, communication and resource management. The central element of the FCP is the Fog Node (FN), built upon commercial off-the-shelf (COTS) multicore processors (MCPs) and virtualization support. Resource management tools, advanced security features and state of the art communication protocols complete the FCP. The paper concludes by outlining future research challenges by comparing the proposed FCP with the identified requirements.