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.
A key question for a broad uptake is if AAS can be applied in a performant and scalable manner. In this paper, we examine this question for the open source Eclipse BaSyx middleware. To explore capabilities and possible performance limitations, we present four experiments measuring the performance of experimental AAS in BaSyx and, within the context set by our experiments, i.e., 10-1000 AAS instances, can conclude good scalability.

Intelligent manufacturing is one goal of smart industry/ Industry 4.0 that could be achieved through Artificial Intelligence (AI). Flexibly combining AI methods and platform capabilities, such as dynamic offloading of code close to production machines, security or interoperability mechanisms are major demands in this context. However, recent Industry 4.0 software platforms fall short in various of these demands, in particular in upcoming ecosystem scenarios, e.g., when data or services shall be shared across platforms or companies without vendor lock-ins. The aim of the funded Intelligent Industrial Production (IIP) IIP-Ecosphere project is to research concepts and solutions for ‘easy-to-use’ AI in Industry 4.0 and to demonstrate the results in a prototypical software platform. Core questions are which demands shall drive the development of such a platform and how a feasible set of requirements can be determined that balances scientific and industrial interests. In this paper, we discuss our approach on eliciting requirements in this context for two interlinked requirements perspectives, a usage and a functional view. In summary, we collected 67 usage view activities / scenarios and 141 top-level requirements with 179 detailing sub-requirements. About 35% of the requirements have so far been realized in a prototype and some of the identified concepts are currently being taken up by a standardization initiative for edge devices in Industry 4.0.

Collective Adaptive Systems (CAS) achieve high resilience by providing distributed self-* properties through autonomous elements. While they are very beneficial for achieving high reliability, they are more difficult for dealing with targeted external inputs or ensuring globally optimal results. It is easier to achieve these properties with centralized approaches. Ideally, one could combine the benefits of collective adaptive systems with aspects of central control to achieve the best of both paradigms. While such combinations have already been shown in previous work, here, we aim at a systematic discussion of the range of approaches to integrate both control paradigms. As a result, we present a taxonomy of control action types, which describes how a central control unit can interact with a CAS to achieve the desired overall behavior, providing a first step towards CAS control patterns and identifying the involved trade-offs. Ideally, one could combine the benefits of collective adaptive systems with aspects of central control to achieve the best of both paradigms. While such combinations have already been shown in previous work, here, we aim at a systematic discussion of the range of approaches to integrate both control paradigms. As a result, we present a taxonomy of control action types, which describes how a central control unit can interact with a CAS to achieve the desired overall behavior, providing a first step towards CAS control patterns and identifying the involved trade-offs.