Disruptions threaten supply chains, creating a need for more resilient manufacturing networks. Manufacturing-as-a-Service (MaaS) has emerged as a promising Industry 4.0 approach to address this challenge. Yet, its effectiveness relies on interoperable digital twins (DTs), enabling the standardized exchange of manufacturing capabilities across organizational boundaries. The Asset Administration Shell (AAS) standards can be used to meet this requirement. However, modeling AAS-compliant DTs is considered challenging due to the standard’s complexity. This paper, therefore, investigates the automatic generation of AAS-compliant DTs for representing manufacturing capabilities. Requirements from MaaS use cases in two research projects reveal limitations in current approaches. To address these limitations, this paper introduces an automated, LLM-supported generation process that leverages ontologies as a domain-specific knowledge base. The approach is operationalized in a modular software architecture and demonstrated through two use cases.

This paper advances the Manufacturing-as-a-Service paradigm through the Tec4MaaSEs (T4M) project, where production and manufacturing processes are delivered as on-demand services using advanced Industry 4.0 and Industry 5.0 technologies, in order to create a resilient ecosystem of distributed value networks. This idea is based on a highly configurable Digital Twin architecture that dynamically adapts to fluctuations in supply and demand, enabling collaboration and optimization across diverse manufacturing scenarios and various stakeholders. Although Manufacturing-as-a-Service (MaaS) platforms promise to enable dynamic configuration of distributed production systems, most existing implementations exhibit limited ability to handle multi-actor processes involving diverse service types, heterogeneous data, and coordination needs. This work presents T4M, a MaaS framework that combines production planning, semantic interoperability, and service modeling to support the flexible composition of manufacturing value chains. A key innovation lies in its iterative feedback structure, allowing analytics and planning functions to co-evolve with service configurations. To structure the design space, we introduce a three-dimensional framework encompassing product-process variety, granularity, and Functional Integration Level (FILe). These dimensions guide the functional specification of platform services and determine where analytics and automation provide tangible value. The framework is instantiated through three representative value networks (VN1-VN3), each illustrating distinct demands in terms of information flows, coordination intensity, and decision complexity. Our analysis shows that effective MaaS ecosystems must align digital mechanisms not only with physical production resources, but also with the informational structure and functional logic of each setting. In particular, the volume-variety concept, and more specifically the notions of granularity and FILe, emerge as key enablers in identifying the level of platform integration and the appropriate scope in shaping MaaS ecosystems. These insights support the development of collaborative, resilient, and circular industry practices.

The Digital Product Passport (DPP) is a data set that contains information about the product throughout all stages of its lifecycle. Recently, a wide variety of activities are being undertaken to introduce DPP to the industry. However, putting the DPP into practice is still challenging. In this paper, we propose a method to integrate production data acquired from existing manufacturing systems into elements within the DPP submodel, following the required structure of the AAS. We also validate the feasibility of the proposed method and provide a use case of DPP in a real-world environment. The proposed approach provides the benefit of easily creating and managing DPP without the need to modify existing systems. Additionally, it facilitates flexible updates to the information contained within the DPP.

Manufacturing as a Service (MaaS) enables a paradigm shift in the current manufacturing landscape, from integrated production and inflexible, fragile supply chains to open production and flexible, robust supply chains. As part of this evolution, new scaling effects for production capacities and customer segments are possible. This article describes how to accomplish this paradigm shift for the automotive industry by building a digital MaaS ecosystem for the large-scale automotive innovation project Catena-X, which aims at a standardized global data exchange based on European values. A digital MaaS ecosystem can not only achieve scaling effects, but also realize new business models and overcome current and future challenges in the areas of legislation, sustainability, and standardization. This article analyzes the state-of-the-art of MaaS ecosystems and describes the development of a digital MaaS ecosystem based on an updated and advanced version of the reference architecture for smart connected factories, called the Smart Factory Web. Furthermore, this article describes a demonstrator for a federated MaaS marketplace for Catena-X which leverages the full technological potential of this digital ecosystem. In conclusion, the evaluation of the implemented digital ecosystem enables the advancement of the reference architecture Smart Factory Web, which can now be used as a blueprint for open, sustainable, and resilient digital manufacturing ecosystems.

The paper describes a reference architecture for open marketplaces to be used for networked stakeholders in industrial production ecosystems. The motivation for such an endeavor comes from the idea to apply the basic principle of the platform economy to offer functions of an asset “as a service” to industrial production, including the associated supply chain networks. Currently, commercial offers of “production as a service” usually lead to proprietary systems with the risk of platform vendor lock-ins. Hence, there is a need for an open approach that relies upon international (emerging) standards, especially those from IETF, IEC, the Plattform Industrie 4.0 and the International Data Spaces Association (IDSA). The presented approach enables federation of marketplaces according to well-defined interfaces. This article proposes a technology-independent open architecture derived from functional and non-functional system requirements and driven by the idea of the Smart Factory Web, a testbed of the Industrial Internet Consortium (IIC). Furthermore, the architecture of the Smart Factory Web (SFW) platform is presented and assessed against the current and future demands of open federated marketplaces for industrial production ecosystems.

The constant development of sensing applications using innovative and affordable measurement devices has increased the amount of data transmitted through networks, carrying in many cases, redundant information that requires more time to be analyzed or larger storage centers. This redundancy is mainly present because the network nodes do not recognize environmental variations requiring exploration, which causes a repetitive data collection in a set of limited locations. In this work, we propose a multiagent learning framework that uses the Gaussian process regression (GPR) to allow the agents to predict the environmental behavior by means of the neighborhood measurements, and the rate distortion function to establish a border in which the environmental information is neither misunderstood nor redundant. We apply this framework to a mobile sensor network and demonstrate that the nodes can tune the parameter $s$ of the Blahut–Arimoto algorithm in order to adjust the gathered environment information and to become more or less exploratory within a sensing area.

This presentation describes work carried out by the Fraunhofer Institute IOSB and the University of Perugia in the European H2020 project “HEritage Resilience Against CLimate Events on Site” (HERACLES) as part of the test bed in Gubbio (Italy) to protect its historic buildings (cf. http://www.heracles-project.eu/). Sensors positioned in the heritage buildings of Gubbio measure parameters such as acceleration, room temperature and crack amplitudes and deliver sampling data streams. The data is stored on a server implementation of the OGC standard SensorThings API (see also https://github.com/FraunhoferIOSB/SensorThingsServer). Special techniques have been developed to select, aggregate and visualize sensor data streams in a performant way in a web application.

The Smart Factory Web (SFW) is a platform that connects Smart Factories over a network to enable flexible sharing and management of resources, assets and inventory to maximize production and efficiency. In order to become part of the Smart Factory Web, network participants describe not only their products, but also the factory capabilities to order to improve factory-to-factory collaboration. A Smart Factory Web Portal (SFWP) enables secure data and service integration in cross-site application scenarios as well as ‘plug & work’ functions for devices, machines, and data analytics software by applying industrial standards, Open Platform Communications Unified Architecture (OPC UA), and Automation Markup Language (AutomationML or AML for short). OPC UA serves as a comprehensive and secure communication protocol from the machine level into the cloud. A Cloud Coupler on the shop floor publishes the availability of a factory and selected process data in the SFWP. Customers or even smart machines can use this information to make decisions for placing an order and track their orders in real time. In order to simplify the setup of the connection to the cloud and minimize the commissioning time the cloud coupler also aggregates all OPC UA servers on devices in the factory into a single OPC UA aggregated server address space. In this paper an architecture is proposed which uses cloud coupler and plug and work techniques to make a new or retrofit factory available in a SFW to share capability information towards a new marketplace for manufacturing. It is shown that the integration efforts are decreased but also the use of standards reduces the effort to define interfaces.

The Industrial Internet of Things (IIoT) is cited as the latest means for making manufacturing more flexible, cost effective, and responsive to changes in customer demands. However, a major concern surrounding the IIoT is interoperability between devices and machines that function within different protocols and architectures. This paper presents the Smart Factory Web (SFW), which is based on the IIoT concept of improving factory-to-factory interoperability. The proposed SFW enables secure data and service integration in cross-site application scenarios as well as ‘plug & work’ functions for devices, machines, and data analytics software by applying industrial standards, Open Platform Communications Unified Architecture (OPC UA), and Automation Markup Language (AutomationML) . To reach the goal, experimental factories that have heterogeneous manufacturing infrastructures are linked and the SFW is implemented in four phases. The usage scenario, called order-driven adaptive production, used to align capacity across factories, will also be validated in the real deployment .

Environmental decision support systems normally require a data processing workflow based on models to explore alternatives. The typical workflow to handle environmental modelling involves several steps covering data discovery, access, pre-processing, model execution and validation, concluded by result visualization. These time consuming steps are usually setup for a particular scenario and set of input data. Scientists normally create their models in specific languages such as R or MATLAB. A challenge is understanding the data model of the scientists and getting the data into the model. In general, scientists are also not able to make their models available as web services. To make scientific models that fuse sensor data fit better into a service-oriented architecture, a software framework called Fusion4Decision was developed. The software framework provides a standard interface to processing algorithms, the so-called Fusors. The term Fusor refers to a general fusion or processing of input data, including through a model based computation. A common fusor is the spatiotemporal interpolation of measurement data. The Fusor is written in any software code that can be integrated into a Java environment, such as MATLAB, R, Python, and C variants. This framework based on Open Geospatial Consortium (OGC) standards can make scientific models available as a web service with standardized interfaces.The OGC services used in Fusion4Decision are: (a) Sensor Observation Service (SOS) to access sensor observations with queries filtering on the phenomenon (property) and the spatial and temporal domains of the observations, and (b) Sensor Planning Service (SPS) to parameterize and task (schedule and execute) assets such as sensors, sensor platforms (e.g. satellites), models or even persons (e.g. to conduct ex-situ measurements). The OGC information models Observation & Measurement Model (O&M) and Sensor Model Language (SensorML) also play a fundamental role.The main operations of the SPS are DescribeTasking (to get the tasking parameters), GetFeasibility (to ascertain if the asset can be tasked with the given parameters) and Submit (to actually execute the task). During the execution the operations GetStatus and Cancel are available. In Fusion4Decision we apply the SPS to models and the model result(s) become new observations for a SOS, i.e. the model is considered to be sensor and its meta-data is described in OGC SensorML. The SPS operations are functionally richer than those of the Web Processing Service (WPS) that is also often used to wrap processing modules as a web service.The formal description of the input and output arguments of the models in a language suitable both for scientists and client software is essential. The model description is encoded as a JSON object and consists of fields for the model name, a human readable descriptive text as well as formal descriptions of the inputs and outputs. The inputs and outputs allow for arrays of the basic variable types scalar, string, time, URL and file. Their description includes a) units of scalars, b) default, minimum and maximum values of scalars and optionally c) an annotation as a URI linking to an authoritative definition in an ontology. This covers the requirements of typical scientific models and also encourages the inclusion of comprehensive meta-data needed to convey full understanding of the model algorithm and its limitations. The JSON description of a model can be automatically translated into SensorML for use by the OGC services SOS and SPS.The increasing proliferation of sources of geospatial data on the web as well as models to process the data and derive new information underlines the need for a standardised framework to better link data, models and their results. Standards of the Open Geospatial Consortium can be used to integrate data access and models into web services, thus being a step towards the Model Web in which scientists and decision makers can work together effectively. The paper proposes a simple way of describing the input and output arguments of a model using JSON. This JSON description can be readily understood and generated by model providers and also translated into the sensor description language SensorML. The latter is the basis for applying the sensor concept in the OGC standards SOS and SPS to models ("model as a sensor"). This approach bridges the gap between scientists and IT specialists.

The current procedure for the reporting of cholera cases in Uganda contains many manual steps across several levels of the health infrastructure. Because of this there is a large chance of errors in the information flow, possibly delaying the signalling of an outbreak. The lack of accurate and complete data also hinders research into the spread of cholera. To improve the cholera reporting an application called Dira (Disease Incidence Reporting Application) has been developed for mobile devices, that allows the field registration of patients to be done quickly, easily and accurately. By entering the data directly on an electronic device there is no longer the need for separate digitization steps. By transferring case data directly from the hand-held device to a central server at the Ugandan Ministry of Health, the reliability of the data can be increased and the time to launch a response to an outbreak can be decreased.

Air quality and air pollution have a very large impact on human health. The sensitivity to different pollutants varies per person, therefore it is important that citizens can get personalised air quality information. The Personal Environmental Information System (PEIS) aims at delivering just that. The PEIS takes sensor data from several data providers and employs a service-oriented architecture to deliver these observations to the user through a smartphone application. The PEIS also uses scientific models to fuse the sensor data and create new, derived observations. To make scientific models that fuse sensor data fit better in a service-oriented architecture, a software framework called Fusion4Decision was developed. This framework is based on Open Geospatial Consortium standards and allows scientific models written in languages like MATLAB or R to be available as a web service.

The project EO2HEAVEN “Earth observation and environmental modelling for the mitigation of health risks” advanced knowledge on the impact of environmental factors on public health outcomes. The multidisciplinary and user-driven project approach focused on the effect of atmospheric pollution (in case studies in Durban / South Africa and Saxony / Germany) on cardiovascular and respiratory diseases and the waterborne disease cholera (in a case study in Uganda). EO2HEAVEN has developed methodologies, models, spatial data services (using OGC standards) and applications supporting the main activities involved in environmental health: discovery and acquisition of environmental data, integration of heterogeneous Earth observations (satellite, in-situ and field data), development of models of health effects, development of risk maps and predictions for early warning systems. EO2HEAVEN has specified and implemented a Spatial Information Infrastructure (SII). This is an open architecture based on international standards and geospatial web services supporting the large-scale initiative GEOSS of GEO.

(1) University of Southampton IT Innovation Centre, Southampton SO16 7NP, United Kingdom (zas@it-innovation.soton.ac.uk / +44 23 8076 0833), (2) Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum, Potsdam, Germany (wae@gfz-potsdam.de ; kueppers@gfz-potsdam.de /+49-331-2881703), (3) Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, Germany (kym.watson@iosb.fraunhofer.de)

This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities' environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term "envirofied". Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management).

One of the key issues for the analysis and the management of the environmental status is the integration of information from various sources, being in situ, airborne or space borne sensors or environmental databases. The European Integrated Project SANY has published a specification of the Sensor Service Architecture (SensorSA) enabling large-scale environmental information systems. The SensorSA belongs to the family of service-oriented architectures but has a particular focus on the access, the management, the processing of information and event notifications provided by sensors and sensor networks. It contains sensor-specific services, primarily based upon standards of the Sensor Web Enablement initiative of the Open Geospatial Consortium abstracting from the peculiarities of sensors and underlying sensor network technologies. The SensorSA follows a multi-style architectural approach: In addition to remote invocations, it also supports an event-driven and a resource-oriented architectural style. This paper presents the basic architectural concepts, supported sensor network topologies, generic use cases, sensor service types and information models. It concludes with a description of the SERVUS design methodology. The SERVUS design methodology is tailored to the design of geospatial service-oriented architectures and environmental information systems and leverages the idea of a uniform modeling of use cases and capabilities of SensorSA implementations as resources.

Fraunhofer has realized a special SOS in its environmental sensor testbed called a Fusion SOS, that is able to aggregate or fuse sensor data from several SOS. The Fusion SOS queries the semantic catalogue for available SOS of the required type and then conducts a selected procedure to produce a spatial or spatial-temporal interpolation. The interpolation result is a so-called coverage, a function defined on a space-time grid of sampling points. The procedure takes the inaccuracy of the input sensor data into account and can optionally eliminate outliers. The spatial-temporal uncertainty of the fusion result is specified using uncertML, an XML schema developed by the INTAMAP project to describe the statistics of uncertain data. The configuration and execution of fusion algorithms (e.g. for model based spatial fusion) is done using the OGC SWE Sensor Planning Service. The fusion procedure is described, just as for the underlying sensors, with the OGC sensor model language SensorML. In this way, the fusion procedure can be treated as a sensor, but with the important characteristic that its result is a coverage. The fusion result is a new, refined collection of observations on a grid with associated uncertainty data.