Modern building management systems (BMS) require scalable and flexible solutions to integrate new devices and functionalities with minimal manual effort. This paper addresses this challenge by proposing a multi-agent system (MAS) architecture that leverages the KNX IoT and Web of Things (WoT) specification. The proposed skill-based MAS approach enables agents to interact autonomously through a semantically enriched communication layer. This reduces configuration efforts and commissioning, which typically pose a great challenge for the deployment and integration of new devices within a BMS. The MAS is virtually deployed and evaluated for a single-family residence. Two scenarios are analyzed: (1) Addition of new equipment to an existing setup, and (2) extending functionality of an existing setup. The results demonstrate the seamless device integration process and validate the applicability of KNX IoT and WoT for the developed MAS. Nevertheless, the study also identified limitations in current KNX modeling capabilities, that could be improved in future versions.

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 poses fresh challenges, demanding innovative strategies to ensure the safe execution of production processes. With the increasing significance of production system integrity, any security breaches can lead to severe consequences like production downtime, equipment damage, or human harm. Our prior research on Austrian industrial automation stakeholders highlighted the necessity for a cost-effective, all-encompassing approach to the integrated safety and security. We introduced an extensive ontology for safety, security, and operational requirements in IT/OT convergence. This paper presents an approach of Model-Based Systems Engineering (MBSE) for the integrated safety and security by design of industrial systems. We employ the Systems Modeling Language (SysML) 2.0 for precise modeling. We define metadata information that are used as tags for SysML 2.0 model instances. Afterwards, we create a graph-based model of the system. These graphs are used to validate safety and security standards and requirements. Finally, we automatically generate artifacts, such as code or documentation, which adhere to the standards. Our approach is extensible and supports reusability already after covering two standards. Having provided support for the standards IEC 62443-3-3 and IEC 61508, we reuse our approach to validate the standard ISO 13850:2015.

The domain of Industrial Internet of Things (IIoT) contains a vast variety of standards, protocols, tool suites, coding languages, etc. The Reference Architecture Model Industry (RAMI) 4.0 provides an abstraction of the different views involved in designing IIoT systems. However, the life-cycle model of the reference architecture suggests a progression from the development into production. This progression does not conform well with the best practices of software engineering, e.g., DevOps, where runtime data is given back to the design-time. In this paper, we suggest an agile life-cycle management of the IIoT systems, and propose an approach of Model-Based Systems Engineering (MBSE) based on the System Modeling Language (SysML) 2.0. We define metadata in the domain of event-driven IIoT systems. Architects can create tagged model instances, which are automatically validated against system requirements. The validated models are passed to an artifact generator to create code, test cases, or documentation. Our approach assists in the development and commissioning of system assets. We feed the runtime data to design-time to improve the quality of service.

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber- Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.

Cyber-Physical Systems represent a fusion of spatially distributed digital and physical elements. Due to the increased complexity of such systems, understanding their holistic operational conditions becomes more and more challenging, especially for non-skilled operators. Event detection is a proven technique in this problem domain. However, manual event specification can be laborious and error-prone, motivating automated approaches. This paper proposes a knowledge-based event detection system that leverages abstract reasoning rules to derive time series event specifications from a system model. By automating the derivation of specifications, system integrators can focus on crafting event specifications for the system's intricate parts. Leveraging Semantic Web technology and Signal Temporal Logic, we evaluate the feasibility of the proposed approach in an electric vehicle charging scenario.

The Industrial Internet of Things (IIoT) involves various standards, protocols, and tools, requiring extensive expertise for system design. The traditional automation pyramid limits scalability due to tightly-coupled components. Integrating IIoT data in the cloud through event-driven communication, like MQTT brokers, provides loose coupling. This integration also aids in resource monitoring and planning, enhancing IIoT application performance. Despite many IIoT architecture studies, there is a lack of empirical comparisons of MQTT broker deployment strategies. This paper examines an edge-cloud aggregation scenario, where IIoT device requests are aggregated at the edge and fog services before being transmitted to the cloud. We study four MQTT deployment architectures and compare the results empirically. We use an Arduino Opta as an IIoT device. Also, we use software-based load generation to support replicability of our experiment and reproducibility of our results. Findings indicate that a central broker on a dedicated virtual machine gives 13.8% improvements of the mean response time compared to the shared deployment of MQTT broker and device gateways. Our results offer insights into effective deployment strategies.

The Industrial Internet of Things (IIoT) is characterized by a multitude of standards, protocols, and tools. Moreover, the convergence of Information Technology (IT) and Operational Technology (OT) brings new architectural styles, e.g., event-driven communication, that are easier to scale and integrate IIoT devices. Architects need extensive expertise to manage the complex task of designing systems that encompass hardware, software, information, communication, and users. Proven approaches such as Model-Based Systems Engineering (MBSE) can simplify the design of IIoT systems by offering abstractions through system models and views. In this paper, we introduce an MBSE approach grounded in Systems Modeling Language (SysML) 2.0. Our method involves defining metadata tailored to event-driven IIoT systems. System designers can generate tagged model instances that undergo automatic validation against system requirements, such as asynchronous messaging, authentication, and health checks. Following validation, these models are utilized by an artifact generator to produce code, test cases, or documentation. Our approach is designed for reusability, and we provide tool support to streamline the implementation of requirements checking for emerging standards and guidelines. This enhances the flexibility and efficiency of IIoT system design, ensuring compliance with diverse and evolving industry requirements.

The paper addresses the challenge of efficiently and reliably transforming system models into semantically enriched runtime models by integrating ontologies to preserve semantic integrity. The approach leverages domain specialists to annotate system models with semantic tags, bridging expert perspectives and creating a unified, comprehensive representation of the system. Manually created runtime models tend to be inconsistent with the original system model. Moreover, the process is time-consuming and does not scale well for larger models. Therefore, a tool-assisted workflow is proposed that offers support at every step, from annotating the system model to generating the semantically enriched runtime model. This approach allows using definitions from semantic web ontologies in system models as tags. These semantic annotations are then used in the generation of the system's runtime model. The workflow is showcased by applying it to a system model of a Packed Bed Thermal Energy Storage (PBTES).

This paper describes an approach for integrating a Machine Learning (ML) model for fault detection into an Asset Administration Shell (AAS), focusing on the seamless deployment of updated versions of this model during operation without causing downtime in production processes. To this end, a generic submodel was developed that conforms to the AAS metamodel definition and can support different types of ML models and application scenarios. In addition, three deployment strategies were identified that allow switching between model versions without downtime. Evolved concepts were evaluated with a proof-of-concept to illustrate the applicability and suitability of the approach. The evaluation shows that the most appropriate deployment strategy depends on the specific use case.

In order to achieve energy flow optimization, Energy Communities have to rely on real-time measuring data that is usually provided by so-called Internet of Things (IoT) platforms. Constrained devices are especially difficult to integrate into these frameworks, because of their limited configuration options. In this paper, we propose a system design that uses principles from the Web of Things (WoT) and the Sparkplug specification to accomplish a higher level of interoperability. The proposed system design facilitates the integration of constrained MQTT devices into a standardized and normalized topic and payload structure. Furthermore, it is capable of augmenting constrained devices to support state management through birth and death certificate messages. As a proof of concept, the integration into the IoT platform FIWARE is demonstrated.

Cyber Physical Production Systems have a need of sharing and interlinking information and knowledge over different domains. In the area of industrial automation, OPC Unified Architecture (OPC UA) is a widely used and established standard for communication and information modeling. We propose an ontology-based OPC UA data access method utilizing custom property functions, which enables interlinking between OPC UA information and other factory data. To avoid duplicated data and to reduce the communication overhead in the proposed method, the OPC UA run-time data are loaded on-demand and are not persistently stored in the triplestore. To enable fast and easy ontology-based access and interlinking of the OPC UA information, the needed ontology is automatically generated from the OPC UA information model. A proof of concept demonstrates the application of our approach for a laboratory use-case of a Packed-Bed Regenerator.