Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This article presents a comprehensive analysis of various prediction models, with a focus on achieving accurate and efficient Wi-Fi link quality forecasts using machine learning techniques. Specifically, the article evaluates the performance of data-driven models based on the linear combination of exponential moving averages, which are designed for low-complexity implementations and are then suitable for hardware platforms with limited processing resources. Accuracy of the proposed approaches was assessed using experimental data from a real-world Wi-Fi testbed, considering both channel-dependent and channel-independent training data. Remarkably, channel-independent models, which allow for generalized training by equipment manufacturers, demonstrated competitive performance. Overall, this study provides insights into the practical deployment of machine learning-based prediction models for enhancing Wi-Fi dependability in industrial environments.

Ensuring high-quality data is essential for effective air quality monitoring, especially in smart urban environments, where real-time insights drive health and policy decisions. This paper presents a structured, step-by-step approach for developing FIWARE-based air quality monitoring systems with a strong focus on data quality. Addressing challenges, such as sensor heterogeneity, calibration, real-time data processing, and semantic interoperability, the paper outlines best practices for achieving accurate and reliable environmental data. The system design leverages open-source FIWARE components integrated with NGSI-LD Smart Data Models to ensure standardized data flows. Furthermore, the TwinAIR project is explored as a real-world implementation that enhances data quality through validation and digital twins for air quality trend prediction. The proposed architecture demonstrates how high data integrity can be maintained across the entire data life-cycle — from sensor deployment to actionable analytics. This work is a practical guide for system designers and a benchmark to achieve scalable, interoperable, and reliable air quality monitoring solutions.

The deployment of autonomous mobile robots in Industry 4.0 environments requires reliable and adaptable SLAM (Simultaneous Localization and Mapping) techniques. This paper presents a comparative evaluation of multiple SLAM algorithms that take advantage of both LiDAR and visual inputs on a Clearpath Jackal robot using ROS2 Humble. Unlike existing studies limited to sensor-specific SLAM approaches, our work systematically benchmarks algorithm performance in a testbed at SmartFactoryOWL, Lemgo, under varying sensor configurations and environmental conditions to assess their applicability in real-world industrial scenarios. We evaluated traditional algorithms such as GMapping, Hector, and Cartographer, along with Visual SLAM frameworks such as ORB-SLAM3 and RTAB-Map. Experiments are conducted in both Gazebo simulation and real-world environments modeled on SmartFactoryOWL. Key performance indicators including mapping accuracy, localization accuracy, robustness to dynamic changes, and real-time performance are systematically analyzed. This study aims to identify suitable SLAM solutions tailored for smart factory applications fostering better human-robot collaboration, thereby contributing towards robust indoor autonomy for mobile robot navigation.

Executable QR codes, or sQRy, is a technology dated 2022 that permits to include a runnable program inside a QR code, enabling interaction with the user even in the absence of an Internet connection. sQRy are enablers for different practical applications, including network equipment configuration, diagnostics, and enhanced smart manuals in industrial contexts. Many other non-industry-related fields can also benefit from this technology. Regardless of where SQRy are used, text strings are among the most commonly embedded data. However, due to strict limitations on the available payload, the occupancy of strings limits the length of the programs that can be embedded. In this work, we propose a simple yet effective strategy that can reduce the space taken by strings, hence broadening sQRy applicability.

This research presents the solution for building a Data Management platform with FIWARE framework and other open-source components to monitor and manage indoor air quality. FIWARE components are used to collect, store, and analyze data related to indoor air quality, such as particulate matter (PM2.5), volatile organic compounds (VOCs), and carbon dioxide (CO2) levels. Other components are used to provide a data registry and data visualizations. To ensure timeliness, the platform enables real-time data collection and processing through FIWARE's Context Broker, allowing immediate updates and access to current IAQ data. The paper presents how such a setup could look like and discusses some open issues related to the data management issue in the indoor air quality context.

Modern Artificial Intelligence (AI) research is having a huge impact in many technological domains. As in many other research areas, the application of AI in smart factories has been a key factor in the contribution to the “smartness”. Every aspect of industrial automation has been affected by the introduction of AI: the usage of AI solutions allows to introduce advanced capabilities for optimizing processes, increasing efficiency, and reducing costs. This paper analyzes some relevant aspects of the application and the impact of AI solutions on the current scenario of smart factories and industrial automation. We identify a list of significant topics related to this domain, and we report the main aspects related to them. The dissertation includes an initial quantitative analysis of the relevance of these topics in the scientific publications, a detailed description of the characteristics of the topics, and a discussion of the related challenges.

Mobile robots are essential in various industries, with Simultaneous Localization and Mapping (SLAM) technology playing a crucial role in their autonomy. This work-in-progress paper lays the foundation for evaluating 2D LiDAR-based SLAM algorithms for implementation on a Clearpath Jackal robot in a smart factory environment. The study focuses on three SLAM algorithms: GMapping, Cartographer, and Hector SLAM. A real-world smart factory, serving as a case-study location, is modelled in the Gazebo simulator to evaluate the selected algorithms according to mapping quality, location accuracy, and performance consistency. The simulation uses a hardware-in-the-loop approach, where LiDAR data is processed by the physical Jackal robot, ensuring realistic testing conditions. The findings from the simulation, including the key factors influencing the performance metrics, are validated through real-world testing. This paper outlines the methodology for both simulation and real-world deployment, setting the stage for determining the most suitable SLAM algorithm for efficient and accurate mapping and localization within the operational constraints and requirements of a smart factory environment. Additionally, preliminary insights into factors affecting SLAM performance in the real-world and the relative strengths and weaknesses of each framework are discussed.

Executable QR codes, also known as eQR codes or just sQRy, are a special kind of QR codes that embed programs conceived to run on mobile devices like smartphones. Since the program is directly encoded in binary form within the QR code, it can be executed even when the reading device is not provided with Internet access. The applications of this technology are manifold, and range from smart user guides to advisory systems. The first programming language made available for eQR is QRtree, which enables the implementation of decision trees aimed, for example, at guiding the user in operating/maintaining a complex machinery or for reaching a specific location. In this work, an additional language is proposed, we term QRind, which was specifically devised for Industry. It permits to integrate distinct computational blocks into the QR code, e.g., machine learning models to enable predictive maintenance and algorithms to ease machinery usage. QRind permits the Industry 4.0/5.0 paradigms to be implemented, in part, also in those cases where Internet is unavailable.

Integrity as one property of trustworthiness is an important aspect for the adoption of the Asset Administration Shell (AAS) as a data exchange format and source for data driven services. Until now, the AAS offers an access control solution as a preventive integrity measure. Nevertheless, preventive methods lack in detecting integrity violations due to accidental or malicious modifications from access permitted endpoints. This work in progress paper proposes a concept to complement the access control with a detective integrity measure. By signing submodels of the AAS, business partners along the life cycle can verify the integrity of the data inside the AAS. Therefore, a Certificates Submodel and a Signature Submodel are proposed in the concept to enable a flexible and interoperable integrity verification. In future work, the concept will be implemented and evaluated.

Technologies such as TSN and OPC UA are enablers for converged networks in industrial applications. TSN enables the coexistence of network traffic with real-time requirements and best-effort requirements. On the other hand, OPC UA defines a common information model in addition to secure communication, scalability, and platform independence. The new specification of OPC UA PubSub enables the possibility of implementing OPC UA on the lower levels of the automation pyramid, the field level. Therefore, combining these two technologies can in principle be implemented for every vertical and horizontal communication. This document analyzes the state of the art of OPC UA over TSN and explains the first steps towards an easy to use proof-of-concept for a converged network that is also designed to be a testbed. The testbed will be used to evaluate the interoperability of TSN-capable devices from different vendors. In addition to that, different OPC UA PubSub implementations will be used and benchmarked. These future results will present an overview of the currently available products and provides hands-on experiences for practical implementations of converged networks.

Over the past decades industrial communication networks have evolved into highly diverse and heterogeneous environments, with a variety of different technologies being deployed to address the diverse requirements of manufacturing-and automation-specific use cases. These include stringent latency limits, high availability and reliability, as well as deterministic communication behavior. To assure the necessary allocation of re-sources and provisioning of required Quality-of-Service in highly diverse communication systems, a holistic network management approach is needed that can serve all cornerstones of modern industrial networks. More recently, this lead to the development of new adaptive and agile management approaches that imple-ment autonomous and self-organizing manufacturing networks, whereby Machine Learning (ML) methods started to become an integral part for overcoming the limiting factors of practically deploying such systems. Due to the growing complexity of today's networking environments, defining network management policies based on expert knowledge becomes increasingly difficult. ML has evolved as a promising technique to extract knowledge from collected data to enable cognitive network management approaches. This paper reviews past advances in ML applications for zero touch management of heterogeneous industrial communication networks. It illustrates how a network's management life-cycle that is based on digital twin technology can harnesses the potentials of ML to bring the concepts of organic computing and zero-touch cognitive manufacturing within industrial networks closer to reality. Lastly, recent papers that discuss the use of ML approaches for self-x features in Zero-Touch Management (ZTM) network environments are surveyed and relevant open issues are discussed.

The manufacturing domain is exposed to a continu-ous change of the requirements towards the IT infrastructure and the flexibility in Industry 4.0. In order to achieve a highly reliable production system, predictive maintenance and additive sensing have been implemented and will be complemented by further applications such as Augmented Reality. As the applications may be required ad-hoc at any time, the dynamic resource utilization of networking and computational resources needs to be managed. In the long-term, the planning of the infrastructure affects the available resources and thus the efficiency and reliability of the short-term resource management. This paper suggests an architecture that combines short- and long-term aspects of the resource utilization and previews how the infrastructure and opportunity costs can be optimized by the joint approach.

The data driven services in industrial automation systems are transforming the world of automation industry by optimizing industrial processes and providing Value Added Services (VASs) with the grace of Industry 4.0, Big Data and Artificial Intelligence (AI). A demand driven data pipeline is essential to connect different industrial data sources in a shop floor with different data storage systems for service provisioning. This paper analyzes an experimental approach and corresponding challenges to optimize computing resource allocation in industrial applications to construct such demand driven data pipeline to provide data driven services through an open source, flexible and extensible distributed query engine known as Presto, which can perform interactive analytical queries for different purposes such as condition monitoring, asset management or many others.

The advances in industrial automation systems rise more stringent demands for reliable and flexible communication. 5G and TSN have been discussed widely as promising candidates that fulfil these requirements. To take advantage of the combination of both of these technologies, an integration of 5G with TSN is part of the current work on the 5G specifications. In this paper, a simulation approach for OMNeT++ based on the existing TSN model NeSTiNg and a novel lightweight 5G user plane model that is still under development is presented. By means of a relevant closed loop control simulation, the known issue of different scheduling periods is reproduced in a minimal simulation scenario. Furthermore, crucial further steps to optimize the model towards an IEEE 802.1Qbv TSN bridge are identified as future tasks.

Real-time behaviour is one of the most important aspects of field level automation networks in factory communication. Various Real-Time Ethernet (RTE) technologies have been developed over the years to cope with the network requirements like latency, Quality-of-Service (QoS), reliability etc. of the field level networks. Because of their nature of vendor-dependency, these technologies have been incompatible with one another. In the era of Industry 4.0, an innovative approach is required to bring together this technology in order to solve the interoperability issue. Open Platform Communication Unified Architecture (OPC UA) is a vendor-neutral communication protocol which can be an answer to this incompatibility problem of field level factory networks. This paper aims to use OPC UA as a field level communication protocol in combination with the Time-sensitive networking (TSN) standards to simulate the real-time behaviour of field network.

Increasing heterogeneity of industrial network systems is a fact and the chances that in the future one communication standard will be able to fulfill the requirements of all possible applications are utopian. With the increasing number of communication systems, their management, configuration, and maintenance become a significant issue. Additionally, due to the increasing amount of network services and traffic, the management of available network resources and the possibility of delivering certain levels of communication quality of service, especially across different network solutions becomes a big challenge. Therefore, in this paper a Controller of Controllers (CoC) concept for management of heterogeneous industrial networks is proposed. The concept is designed to support still widely spread legacy fieldbus systems, different Ethernetbased industrial solutions, and potentially upcoming network technologies. The goal is achieved by leveraging state-of-theart architectural concepts such as software-defined networks, which allows for integration of abstract models in network management. The concept is described and discussed by way of a demonstrator concept for a heterogeneous system of fieldbus and Ethernet-based time-sensitive networks.

The advent of Industry 4.0 brings the Internet of Things (IoT) and Cyber-Physical Systems (CPSs) inside the factory premise and made the boundaries between the information and operational technologies (IT & OT) obsolete. This, in turn, introduces the problem of interoperability due to the integration of multiple industrial protocols and technologies from various automation networks. The aim of this paper is to investigate this interoperability problem of heterogeneous network management and propose a semantic network knowledge base as a solution to it. It also describes a set of generic interface functions for data acquisition.

Driven by rapid digitalisation, production systems are becoming more flexible and adaptable with the help of emerging concepts like the Internet of Things (IoT) and Industry 4.0. Often, these transformations are not fully implemented in Small and Medium-sized Enterprises (SMEs) due to the replacement cost of existing machines. This paper aims to develop a Plug & Play retrofitting platform, where Industry 4.0 compliant sensor systems can be attached, detected, and configured automatically to the existing production environment. The purpose of the retrofitting is to integrate the sensor system with a cloud platform that would provide persistent storage for sensor data as well as the functionalities to perform monitoring, analysis, and predictive learning.

This special section on heterogeneous industrial networks of the current and next-generation factories tackles a wide range of the issues mentioned above. Its main objective is both bringing together ideas from the worldwide research community about the directions in which the industrial networks’ development is headed and presenting the latest advances in the works related to industrial communication technologies to be potentially used in the future. All these concepts have to be evaluated keeping in mind that the evolution of these technologies is a fluid and continuous process. Probably, in the near future, industrial networks will be more and more heterogeneous for the introduction of new technologies with even more variegated characteristics, and application boundaries will be able to space from a single room to the whole world. Only a few articles are currently addressing intelligent and self-adaptive networks, and some of these works are included in this Special Section. Certainly, the introduction of relatively new paradigms for industrial communication such as SDN is the first important step for a network of the future on which to lay intelligent algorithms. These paradigms will have to also be extended to the wireless counterpart, bearing in mind that the adaptability to the intrinsic variability of the wireless medium and nodes mobility will remain research challenges for a long time. The eight articles selected for publication in this special section are briefly summarized here.

The emergence of the Internet of Things (IoT) and Cyber-Physical Systems (CPSs) has revolutionized the industrial sectors around the world. Industry 4.0, a digitalization endeavour by the government of Germany for the factories of the future, aims to create intelligent, self-organized, and self-optimized smart factories by combining the IoT, cloud computing and artificial intelligence together. Interoperability is one of the crucial aspects of Industry 4.0 which requires standardized information models and exchange mechanisms for heterogeneous platform integration. Despite the development of various industry standards, this still remains the biggest burden due to multiple protocols, technologies, and policies involved. A common understanding of data semantics can be helpful in mitigating such problem. This paper provides a detailed comparison between the modelling approaches of Open Platform Communication Unified Architecture (OPC UA) and Resource Description Framework (RDF) and its extension languages. The aim is to garner a common understanding between these two modelling frameworks in order to generate a standardized mapping between them for the purpose of interoperable industrial applications.

The upcoming fifth generation of mobile networks (5G) is supposed to enable innovation in industries like factory and process automation by addressing machine-type communication and its requirements of reliability and latency. However, 5G will not immediately fulfill all the requirements raising from industrial applications at reasonable costs. The integration of 5G as a network entity into an industrial communication system is therefore considered as an applicable migration path. In this paper, a flexible and adaptive integration of a 5G mobile network into the industrial Ethernet protocol Profinet utilizing the network slicing concept will be proposed.

Industrial food production is one of the biggest businesses that supplies most of the food consumed by the world. Despite being one of the largest industries, the lack of appropriate control and quality due to inefficient food management results in a lot of waste and cost ineffectiveness. With the emergence of Internet of Things (IoT) and Industry 4.0, the food industry can be benefited by adapting to these concepts and enhance the desirable quality of the products. In this paper, an advanced quality check method has been proposed by identifying influencing process parameters and proposes a retrofitting architecture for existing machines by implementing a hardware device capable of collecting vast amount of process data and integrating them with a cloud platform for further analysis.

Due to the dynamic nature of the Industrie 4.0 developments, future production systems will be reconfigured more frequently and new system configurations will be deployed automatically. In order to keep pace with this development, it will be required to observe and ensure the needed security functionalities and corresponding certifications in an automated way. This implies an improvement of today's static procedures and manual efforts as much as possible in favor of a dynamic standard establishment. Therefore, this paper evaluates the state of the art of the industrial security standardization landscape and proposes a concept for the automated support of certification processes with information from industrial communication networks in order to enhance the usability of the standards establishments and the certifications within organizations and companies, especially small and medium-sized enterprises.

Maintenance is an important set of various activities related to preserving from failure or decline. Improper or lack of maintenance may result in excessive component wear, production quality deterioration, or even longer downtime. However, today's production facilities strive to devote the least amount of necessary maintenance time in order to maximize production time. Therefore, new solutions for deliberate and efficient maintenance are needed. The solution proposed in this paper benefits from the newest trends and innovations in industry, namely the Asset Administration Shell (AAS) which is part of the Industrie 4.0 (I4.0) concept. The AAS shall contain the maintenance submodel which shall be used for supporting humans during the maintenance process. The submodel provides a standardized description of required tools and parts as well as step-by-step instructions which also include safety concerns and multimedia files, such as pictures and videos. In this way, maintenance can be carried out more reliably, resulting in reduced downtime. In addition, feedback from the maintenance process shall be stored in the submodel and fed through an I4.0-compliant network to other processes from different phases of the life cycle in order to improve them.

In this paper, an alternative and new approach to embed sophisticated Internet of Things (IoT) functionality into consumer electronic devices has been proposed. The approach has been evaluated by implementing it into an electric kettle. The proposed concept includes a new communication architecture, data models as well as the possibility to create user profiles. Moreover, it is compatible with the building automation and control systems (BACS) and other available IoT solutions. The presented evaluation shows that by careful consideration of the power consumption, there is a high potential to a safe significant amount of energy, especially if it is scaled up to the number of potential users. Furthermore, it is shown that the proposed architecture is very flexible and can be extended with several other functionalities or profiles, depended on the requirements and addressed device type.

Current manufacturing and production systems are becoming more flexible and adaptable. Such systems demand rapid changeover and short configuration time of machines. With the adaptation of IT systems in the automation industry, machines and devices will have self-descriptive semantic device data description to represent their data elements and services. This paper proposes an approach to integrate new machines and devices into an existing production system without any manual intervention. The proposed concept provides a Plug & Produce system architecture and evaluates its capabilities based on OPC UA. The concept also describes an easy and fast integration of the digital representation of new field devices into an existing production system.

The Plug-and-Produce concept requires that after connecting a new module to a system, the exchange of the configuration data takes place. As further operation of the system depends on this initialization procedure, it is necessary to ensure that the data presented by the system and the newly attached component is authentic. Therefore, we propose a new concept for secure Plug-and-Produce functionality, which exploits the combination of the Asset Administration Shell (AAS) and Blockchain technology. On the one hand, the AAS shall be responsible for presenting uniform and standardized configuration data as well as for storing and managing Blockchain. On the other, Blockchain shall ensure authenticity and integrity of the configuration data.

The process of digitalisation has an advanced impact on social lives, state affairs, and the industrial automation domain. Ubiquitous networks and the increased requirements in terms of Quality of Service (QoS) create the demand for future-proof network management. Therefore, new technological approaches, such as Software-Defined Networks (SDN) or the 5G Network Slicing concept, are considered. However, the important topic of cyber security has mainly been ignored in the past. Recently, this topic has gained a lot of attention due to frequently reported security related incidents, such as industrial espionage, or production system manipulations. Hence, this work proposes a concept for adding cyber security requirements to future network management paradigms. For this purpose, various security related standards and guidelines are available. However, these approaches are mainly static, require a high amount of manual efforts by experts, and need to be performed in a steady manner. Therefore, the proposed solution contains a dynamic, machine-readable, automatic, continuous, and future-proof approach to model and describe cyber security QoS requirements for the next generation network management.

In Germany, as of 2017, a new smart metering infrastructure based on high security and privacy requirements will be deployed. It provides interfaces to connect meters for different commodities, to allow end users to retrieve the collected measurement data, to connect to the metering operators, and to connect Controllable Local Systems (CLSs) that establish a TLS secured connection to third parties in order to exchange data or for remote controlling of energy devices. This paper aims to connect industrial machines as CLS devices since it shows that the demands and main ideas of remotely controlled devices in the Smart Grid context and Industrial Cloud Applications match on the communication level. It describes the general architecture of the Smart Metering infrastructure in Germany, introduces the defined roles, depicts the configuration process on the different organizational levels, demonstrates the connection establishment and the initiating partners, concludes on the potential industrial use cases of this infrastructure, and provides open questions and room for further research.

Recent developments under the term Smart Grid change how users consume electricity and interact with the power grid. Smart metering and energy management are developments that transform the yet passive energy consumer to a participant that is actively involved in the energy market by using variable energy tariffs or by demand-response services. But such functionality demands a platform that integrates all smart devices in the users property, connects to external services and electricity providers, and has interfaces that provide information and control to the user. AnyPLACE will develop such platform. Based on the latest legislation in the European member states, it will incorporate smart meters and create links to external service providers. Furthermore, it connects the devices in the property of the end-user in order to be able to fully monitor and control the energy consumption. This paper presents the AnyPLACE idea and the problems that are solved on the communications aspect. It provides an in-depth analysis of current European legislation in the context of smart metering and provides the requirements that need to be realized by the platform. Additionally, it proposes a strategy to create a solution that can be used in any place of Europe. The paper also incorporates the security and privacy requirements in different domains and sketches a solution and architecture to fulfill these by incorporating existing open source implementations as provided by the openHAB project.

Software-Defined Networking (SDN) is a promising approach to build future industrial networks. The disruptive change of network architecture and management opens new possibilities in various application domains. While the approach is already deployed in several domains and was also initially examined to be used in the industrial area, many open questions arise when assessing the risks and improvements to be expected. The following work presents features of a potential future industrial network infrastructure and sets up requirements that are important for future communications. It reviews basics of SDN and correlates existing work to the specified requirements. Finally, it presents open questions and room for future research.

Scheduling of time triggered communication systems belongs to the well known hard mathematical optimization problems. This paper tackles the communication scheduling problem in redundant network topologies, where the real-time process data has to be duplicated and sent on two different paths to its destination. This paper proposes an algorithm that is able to find a feasible schedule in a short time, while keeping the makespan of the schedule in average 10% shorter than the makespan of the schedule produced by a commercial tool, which is the current benchmark.

In the industrial automation a paradigm shift from centralized, static automation structures to reconfigurable manufacturing systems (RMS) might be lie ahead. RMS are seen as a key enabler for the required changeability of future production companies since they can reduce the engineering effort needed for the reconfiguration of existing or the construction of new production systems. However, it is not clear how the companies can benefit from RMS in detail. For example, the reduction of engineering effort cannot be expressed in figures by today. But such information is necessary to convince the industry of the advantages of the new production principle. Indeed, existing RMS paradigms like Service-Oriented Architectures are rarely used in the practice of automation. The basis for analyzing the advantages of RMS is an analysis of the status quo in the industrial automation. Regarding the engineering effort of current automation systems, this paper will present a case study considering the effort occurring during the commissioning process of a production system constructed by state-of-the-art components. The evaluation of the study can serve as a reference when comparing the engineering effort of RMS with today's systems.

Many aspects have to be considered while achieving flexibility of production systems. One of them is flexible communication system that on one hand fulfils requirements of a particular industrial application and on the other hand allows integration of new network components optimally in a seamless way. In this paper, an approach to increase, rather static, Time Triggered Ethernet based systems is proposed. It is based on a greedy scheduling approach that can be implemented directly in a processing limited hardware such as IO Controller. This, together with the discovery and auto configuration mechanism proposed in [1] allows to shift the engineering process typically performed offline using engineering tool to the field level. Furthermore, it has been proven that the proposed scheduling approach is not only simple but also powerful. It is able to calculate an optimal schedule for any kind of tree graph and the complexity of the algorithm is defined by the complexity of the used sorting algorithm.

One of the key aspects to consider increasing flexibility of production plants is the industrial communication system. In case of time triggered Ethernet (TTE) systems, the flexibility is typically limited. In this paper an approach for seamless reconfiguration of TTE system is proposed. It has been shown that the reconfiguration can be performed without influencing currently running system.

One of the most important requirements for the industrial networks, apart from the real-time capability, is the reliability. There are several methods that can be used to increase the communication reliability. One of them is data duplication, which is afterwards transmitted using two different (disjoint) paths. Since, such path search for time triggered Ethernet based systems is typically done during engineering, it is important to keep the time needed for that as short as possible. Especially, if some topology changes are foreseen causing system re-engineering. Therefore in this paper an algorithm is proposed that has linear time complexity and is able to find two possibly disjoint paths for an arbitrary network topology in a short time.

In the future, production systems will consist of modular and flexible production components, being able to adapt to completely new manufacturing processes. This requirement arises from market turbulences caused by customer demands, i. e. highly customized goods in smaller production batches, or phenomenon like commercial crisis. In order to achieve adaptable production systems, one of the major challenges is to develop suitable autoconfiguration mechanisms for industrial automation systems. This paper presents a two-step architecture for the autoconfiguration of real-time Ethernet (RTE) systems. As a first step, an RTE-independent device discovery mechanism is introduced. Afterwards, it is shown how the parameters of an RTE can be configured automatically using Profinet IO as an exemplary RTE system. In contrast to the existing approaches, the proposed discovery mechanism is based on the OPC Unified Architecture (OPC-UA). In addition, a procedure to autoconfigure modular IO-Devices is introduced.

Nowadays the deployment and commissioning of complex production processes or Internet-enabled applications interacting with production systems requires a time consuming and error-prone manual system configuration process. This is due to the need to maintain a high level of determinism, safety, and security of the production process itself and avoiding both safety-critical failures and costly production interruptions. The project “IoT@Work - Internet of Things at Work” aims at delivering tools and runtime mechanisms to significantly simplify commissioning, operating, and maintaining complex production processes, thus enabling Plug-and-Produce for automation systems. This paper presents a novel approach for the autoconfiguration of real-time Ethernet systems, and all relevant mechanisms. A case study, based on Profinet IO, evaluates the approach and shows its feasibility.

Due to several advantages, such as flexibility, increased mobility, and lower costs, the deployment of wireless technologies in factory automation systems is rapidly growing. However, current wireless technologies, e. g., IEEE802.11 Wireless Local Area Networks (WLANs), are not able to satisfy the requirements of industrial applications in terms of real-time communication. In this paper medium access control mechanisms in IEEE 802.11 are investigated and a new isochronous MAC is proposed. In combination with a traffic scheduler specifically designed for it, typical constraints of soft real-time flows found on the factory floor can be satisfied. A preliminary analysis shows the effectiveness of the proposed combination.

Ethernet-based fieldbus protocols, are rapidly becoming the preferred choice for networked control systems (NCS) in Factory Automation. Identification of real-time Ethernet (RTE) traffic flows (TFs) is needed for networks that support parameterized QoS services. In this paper, we present a method based on cross-layer packet inspection to identify TFs of RTEs. The method is tested on hardware that uses some of the most popular RTEs used in industrial automation: PROFINET RT, EtherNet/IP and Modbus TCP/IP. The set of parameters chosen to generate the TF signature lead to a correct identification of all cyclic flows in all tested scenarios.

The use of wireless technologies in Factory Automation is attractive due to several advantages (mobility, cost, etc.); however, to satisfy the requirements of industrial applications, they have to be improved in terms of real-time performance. Handover is a particular weakness in cellular wireless systems, e. g., in IEEE 802.11, since it may introduce delay beyond acceptable bounds. The project “flexWARE - Flexible Wireless Automation in Real-Time Environments” aims at implementing such an infrastructure based on IEEE 802.11. To enhance overall system performance, it offers a localisation service. In this paper we present theflexWARE handover mechanism which exploits localisation to reduce the discovery phase. A performance evaluation, based on simulation and empirical measurements, shows that the mechanism results in a seamless handover for a class of industrial applications.