Poster

Industrial automation environments are undergoing profound changes due to the high level of digitization in the industry. This means that teaching methods and software tools need to be updated. Teaching automation and control concepts to students involves the use of many different software tools that are not easily accessible or configurable. In addition, the new teaching methods lead to delocalized and remote teaching. Therefore, this paper proposes the development of a remote access platform that uses pre-configured virtual machines with all the necessary software for teaching industrial automation, control and monitoring concepts. This platform allows students not only to access a real physical system and work remotely with all the software already configured, but also to download the virtual machine to run it on their own computers and easily switch between a connection to the real system and a realistic simulation of it. The platform also allows the management of different hands-on tasks for subjects of Master's degree related to industrial process automation.

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.

With the interconnection of distributed devices from different industrial automation networks, heterogeneous traffics with different Quality of Service (QoS) requirements increase dramatically. Shapers in Time-Sensitive Networking (TSN) enable the coexistence of different industrial traffics within a single network infrastructure. In this paper, with full consideration of the time-criticality of different industrial traffics and shaping mechanisms, we propose a network calculus-based modeling scheme for the widely-used Credit Based Shaper (CBS) and Time Aware Shaper (TAS). Then, we analyze the QoS of industrial traffics with different real-time characteristics by CBS and TAS, respectively. To evaluate the accuracy of network calculus-based modeling scheme, we compare our theoretical analysis with simulations, wherein a compactness function is defined to describe QoS compactness. Simulation results verify the effectiveness of proposed modeling scheme, by which we further validate both CBS and TAS can satisfy the QoS of traffics for different industrial automation scenarios.

The basic function of the Internet is to delivery data (what) to serve the needs of all applications. IP names the attachment points (where) to facilitate ubiquitous interconnectivity as the current way to deliver data. The fundamental mismatch between data delivery and naming attachment points leads to a lot of challenges, e.g., mapping from data name to IP address, handling dynamics of underlying topology, scaling up the data distribution, and securing communication, etc. Information-centric networking (ICN) is proposed to shift the focus of communication paradigm from where to what, by making the named data the first-class citizen in the network, The basic consensus of ICN is to name the data independent from its container (space dimension) and session (time dimension), which breaks the limitation of point-to-point IP semantic. It scales up data distribution by utilizing available resources, and facilitates communication to fit diverse connectivity and heterogeneous networks. However, there are only a few consensuses on the detailed design of ICN, and quite a few different ICN architectures are proposed. This paper reveals the rationales of ICN from the perspective of the Internet evolution, surveys different design choices, and discusses on two debatable topics in ICN, i.e., self-certifying versus hierarchical names, and edge versus pervasive caching. We hope this survey helps clarify some mis-understandings on ICN and achieve more consensuses.

Predictable and low-latency communication timing is one of the major challenges for employing Ethernet-based networks in industrial automation. The evolving Ethernet AVB standard appears to be a promising architecture, as it provides mechanisms for predictable timing with standard Ethernet hardware. However, the worst-case timing of Ethernet AVB still has to be evaluated. In this paper, we analyze the timing of Ethernet AVB using both simulation and a formal worst-case analysis based on Compositional Performance Analysis known from embedded computing systems. We investigate two industrial scenarios, a typical line topology and a more complex two-level network, and compare the results from analysis and simulation. This allows us get a good indication of the applicability of the current Ethernet AVB with respect to predictable low-latency timing in industrial automation networks. We also gain an understanding of the benefits and limitations of formal Compositional Performance Analysis compared to simulation in this context.

Development of Advanced Fermentor Control Applications for Use in an Industrial Automation Environment

Industrial control systems are foreseen to operate over hybrid wired/wireless networks. While the controller will be deployed in the wired network, sensors and actuators will be deployed in a wireless sensor network (WSN). To support QoS for control systems, an end-to-end delay bound and a target reliability must be provided in both wireless and wired domains. However, for industrial WSNs, guaranteeing reliability is a challenging task because of low-power communications and the harsh wireless environment. In this work, we present the first QoS framework for arbitrary hybrid wired/wireless networks, which guarantees that the delay bound and the target reliability of each application are provided. As part of this framework, we propose the first reliability-based scheduler for WSN able to achieve a target reliability in the presence of dynamic interference. Simulations of the proposed scheduler prove its suitability in different interference scenarios and motivate further work.

This paper presents a comprehensive survey on the applications of sensor networks in industrial automation. Sensor networks are integral to modern industrial processes, enabling real-time monitoring, predictive maintenance, and enhanced operational efficiency. The survey focuses on the various types of sensor networks employed, their applications in different industrial sectors, and the challenges associated with their integration. It also highlights the future directions of sensor network technologies in the context of Industry 4.0.

Adopting the Fog computing paradigm in traditional Cloud infrastructures reduces latency and optimizes network consumption between data providers and data consumers because the system processes the data closer to its source. However, interoperability issues remain as heterogeneous and distributed data sources with different information models must be accessed for performing Cloud-level processing and analysis. This paper describes an integration architecture based on the OPC UA Aggregation Server paradigm, which introduces Fog-level data processing in industrial automation environments. The proposed architecture allows the creation of a configurable aggregated address space at Fog-level where information from data sources at Edge-level is integrated and served to consumers at Cloud-level. The architecture was tested both to verify that the functional requirements are met, and to analyse the time costs involved in the aggregation of Edge-level servers

Industrial wireless sensor networks for factory automation are globally considered as an integral part of smart manufacturing. Wireless networks for industrial automation-factory automation (WIA-FA) is the first and only international standard specifying industrial wireless sensor networks for time-sensitive automation applications. In this article, a channel-hopping-based retransmission (CHR) scheme for WIA-FA networks is proposed. To take advantage of strictly limited communication resources, we design CHR as a hybrid scheduling-based retransmission scheme that is composed of a cyclic reservation diversity retransmission (CRDR) scheme and an on-demand retransmission (ODR) scheme. For device fairness and channel diversity, we propose to combine time-slotted channel hopping with retransmission schemes. Furthermore, we perform the reliability analysis to CHR, guiding the dynamic selection of retransmission schemes according to the actual traffic and available communication resources. Simulation results demonstrate that CHR outperforms existing retransmission schemes in transmission reliability and worst case latency.

We design a content-centric privacy scheme for Information-Centric Networking (ICN). We enhance ICN's ability to support data confidentiality by introducing attribute-based encryption into ICN and making it specific to the data attributes. Our approach is unusual in that it preserves ICN's goal to decouple publishers and subscribers for greater data accessibility, scalable multiparty communication and efficient data distribution. Inspired by application-layer publish-subscribe, we enable fine-grained access control with more expressive policies. Moreover, we propose an attribute-based routing scheme that offers interest confidentiality. A prototype system is implemented based on CCNx, a popular open source version of ICN, to showcase privacy preservation in Smart Neighborhood and Smart City applications.

Intelligent factory automation systems strongly rely on industrial wireless control networks which have to ensure timely and reliable data exchange among their components. This paper presents a comprehensive survey on recently approved International Electrotechnical Commission standard Wireless networks for Industrial Automation-Factory Automation (WIA-FA). This paper first introduces the system architecture of WIA-FA including network device, network topology, and system management, and then illustrates WIA-FA protocol stack and key technologies. Furthermore, two WIA-FA testbeds are described to demonstrate the high performance of WIA-FA. After that, three examples of practical applications are provided in this paper. One application deploys a WIA-FA network to monitor and control industrial robots in a digital workshop. The second application adopts the deployment of WIA-FA as a real-time wireless network that connects automated guided vehicles (AGVs) in a logistic sorting system. The last application coordinates multiple cooperative AGVs via the WIA-FA network to carry large and complex components. Finally, the open issues and future directions for WIA-FA networks are presented.

Industrial automation systems are showing a strong trend towards convergence and networking. Infrastructures and devices with standardized implementations are increasingly being used in the industrial environment where traditionally isolated solutions have been used before. Information security management has become an important concern in the field. We are investigating the challenges of information security management in a modern industrial automation environment.

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.

Time sensitive networking (TSN) is gaining attention in industrial automation networks since it brings essential real-time capabilities to the Ethernet layer. Safety-critical realtime applications based on TSN require both timeliness as well as fault-tolerance guarantees. The TSN standard 802.1CB introduces seamless redundancy mechanisms for time-sensitive data whereby each data frame is sequenced and duplicated across a redundant link to prevent single points of failure (most commonly, link failures). However, a major shortcoming of 802.1CB is the lack of fault detection mechanisms which can result in unnecessary replications even under good link conditions - clearly inefficient in terms of bandwidth use. This paper proposes a machine learning-based intelligent configuration synthesis mechanism that enhances bandwidth utilization by replicating frames only when a link has a higher propensity for failure.