IIP-Ecosphere – Next Level Ecosphere for Intelligent Industrial Production
The digitization of the industry as well as the increasing interconnectedness fundamentally change the efficiency and complexity of technical systems and the related processes in this field. Industrial production (Industry 4.0) is considered a promising candidate for the introduction of artificial intelligence (AI) based methods offering high-potential for improving production efficiency, flexibility and quality within and across factory borders.
The vision of IIP-Ecosphere is to enable self-optimization of the production on the basis of interconnected, intelligent, autonomous systems in order to increase productivity, flexibility, robustness and efficiency. The goals is the creation of a novel ecosystem - the Next Level Ecosphere for Intelligent Industrual Production (IIP-Ecosphere), which enables a "next level" in the intelligent production. To realize this vision, IIP-Ecosphere includes activities that ease the application of AI methods in the intelligent production and demonstrate them in real applicaiton scenarios. These activities target the removal of existing obstacles and foster the inclusion of new stakeholders. In particular, SMEs and startups shall be enabled to successfully apply and evolve AI-methods for the intelligent production.
After the realization phase, the consortium expects that a working ecosystem for the intelligent production has been created, which offers added values for its stakeholders in a sustainable manner, e.g., services, innovations, best practices, AI solutions, blue prints, technologies, interconnections and qualifications. During the realization phase, the ecosystem will be built up by an active community, which takes up the offers of the ecosystem and supports its creation and evolution.
The research of the Software Systems Enginnering group of the University Hildesheim will focus on the systematic development of the platform, which operates at the core of the ecosystem and interconnects the actors and stakeholders. In particular, the work will focus on topics like software architectures, platform engineering, configuration and adaptation mechanisms.
Runtime: 3 years
Contact: Dr. Holger Eichelberger

The IIP-Ecosphere project is supported by the programm / the innovation contest "Künstliche Intelligenz als Treiber für volkswirtschaftlich relevante Ökosysteme" of the Federal Ministry for Economic Affairs and Climate Action (BMWK)
Publications
Abt. Software Systems Engineering
S/N | Publication |
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6. |
Kevin Feichtinger, Kristof Meixner, Felix Rinker, István Koren, Holger Eichelberger, Tonja Heinemann, Jörg Holtmann, Marco Konersmann, Judith Michael, Eva-Maria Neumann, Jérôme Pfeiffer, Rick Rabiser, Matthias Riebisch and Klaus Schmid
(2023):
Software in Cyberphysischen Produktionssystemen - Herausforderungen zur Umsetzung in der Industrie
In: ATP-Magazin, 2023 (4): 62-68.
abstract Um den effektiven und effizienten Betrieb von Cyberphysischen Produktionssystemen (CPPSen) sicherzustellen, spielt Software eine zunehmend wichtige Rolle. Die enormen Fortschritte bei Softwareentwicklungsmethoden, welche in den letzten Jahren erzielt wurden, scheinen jedoch die aktuellen Herausforderungen der Industrie nicht zu erfüllen, weil diese die Industrie nicht oder nur langsam erreichen. In diesem Beitrag werden die Herausforderungen für die Softwareentwicklung in CPPSen aus Sicht von neun Industrievertretern aus acht europäischen Unternehmen unterschiedlicher Größe diskutiert. Um den digitalen Transformationsprozess für eine zukunftsfähige Produktion zu begleiten, wurden aus den beschriebenen Herausforderungen Perspektiven für die Forschung erarbeitet. Die Umsetzung dieser Ziele ist vor dem Hintergrund von ökonomischen, sozialen und Nachhaltigkeitsanforderungen notwendig. |
5. |
Holger Eichelberger, Gregory Palmer and Claudia Niederee
(2023):
Developing an AI-enabled Industry 4.0 platform - Performance experiences on deploying AI onto an industrial edge device
In: Softwaretechnik-Trends, 43 (1): 35-37.
abstract Maximizing the benefits of AI for Industry 4.0 is about more than just developing effective new AI methods. Of equal importance is the successful integration of AI into production environments. One open challenge is the dynamic deployment of AI on industrial edge devices within close proximity to manufacturing machines. Our IIP-Ecosphere platform was designed to overcome limitations of existing Industry 4.0 platforms. It supports flexible AI deployment through employing a highly configurable low-code based approach, where code for tailored platform components and applications is generated. |
4. |
Christian Severin Sauer and Holger Eichelberger
(2023):
Performance Evaluation of BaSyx based Asset Administration Shells for Industry 4.0 Applications
In: Softwaretechnik-Trends, 43 (1): 47-49.
abstract The Asset Administration Shell (AAS) is an upcoming information model standard, which aims at interoperable modeling of “assets”, i.e., products, machines, services or digital twins in IIoT/Industry 4.0. Currently, a number of IIoT-platforms use proprietary information models similar to AAS, but not a common standard, which affects interoperability. |
3. |
Ahmad Alamoush and Holger Eichelberger
(2023):
Adapting Kubernetes to IIoT and Industry 4.0 protocols - An initial performance analysis
In: Softwaretechnik-Trends, 43 (1): 41-43.
abstract Kubernetes (K8s) is one of the most frequently used container orchestration tools offering, as it offers a rich set of functions to manage containerized applications, it is customizable and extensible. Container virtualization of applications and their orchestration on heterogeneous resources including edge devices is a recent trend in Industrial Internet of Things (IIoT)/Industry 4.0, where K8s is also applied. However, IIoT/Industry 4.0 is a domain with high standardization requirements. Besides equipment standards, e.g., for electrical control cabinets, there are also demands to standardize network protocols, data formats or information models. Such standards can foster interoperability and reduce complexity or deployment/integration costs. Here, the proprietary communication protocol of K8s and similar orchestrators can be an obstacle for adoption. |
2. |
Holger Eichelberger, Gregory Palmer, Svenja Reimer, Tat Trong Vu, Hieu Do, Sofiane Laridi, Alexander Weber, Claudia Niederée and Thomas Hildebrandt
(2022):
Developing an AI-enabled IIoT platform - An early use case validation
In:
SASI4 @ ECSA'22
abstract For a broader adoption of AI in industrial production, adequate infrastructure capabilities are crucial. This includes easing the integration of AI with industrial devices, support for distributed deployment, monitoring, and consistent system configuration. |
1. |
Holger Eichelberger, Svenja Reimer, Claudia Niederée and Gregory Palmer
(2022):
Virtuelle IIoT-Plattform für die Digitalisierung der Fertigung
In: Zeitschrift für wirtschaftlichen Fabrikbetrieb, 117 (12): 884-887.
abstract Für die erfolgreiche Digitalisierung in der Produktion ist die IT-Infrastruktur, zum Beispiel zur einfachen Anbindung von Geräten und Steuerung von Datenflüssen, von zentraler Bedeutung. |