Monitoring and life cycle management of industrial plants and their devices is a crucial task to avoid plant downtimes and safety risks. The emergence of Edge Computing allows monitoring applications to be deployed close to the devices, benefiting from a minimum response time, but also adhering to strong privacy policies. At the same time, life cycle management applications need to involve information from multiple parties, e.g., the device vendor, the operator, and maintenance staff. Thus, information needs to be processed in a standardized format so that it can be exchanged across company borders. The Asset Administration Shell (AAS) serves as such a standardized information modeling and exchange format in Industry 4.0. This paper discusses use cases that benefit from a deployment of an AAS infrastructure on edge computing devices. We collected and analyzed use cases around device monitoring and management from recent sources in the industrial context and discuss how our vision of "AAS on the edge" enables these use cases. Furthermore, we list open challenges that currently hinder the successful implementation of these use cases in practice.

As malfunctioning devices may lead to expensive plant downtimes or to severe safety risks, monitoring of industrial plants and their devices is a crucial task. There are many providers for device monitoring solutions on the market. However, integrating third-party monitoring approaches into real industrial systems, especially for time-critical data, is complicated due to mismatching APIs and different parties using different formats for specifying the involved assets and data. This leads to expensive, time-taking, and error-prone manual work. In this paper, we present an edge-computing-based approach to integrate monitoring functionality in an industrial IoT system. It addresses the presented challenges by combining edge computing with an Industry 4.0 information modeling and access format, the Asset Administration Shell.

Summary Distributed control systems are currently evolving towards industrial Internet of Things (IoT) systems communicating fully using Internet protocols. This creates opportunities for streamlining costly commissioning processes, which today require substantial manual work for installing, configuring, and integrating thousands of actuators and sensors. The vision of “plug‐and‐produce” control systems has been pursued for more than 15 years, but existing approaches fell short regarding configuration tasks and vendor neutrality. This paper introduces the standards‐based IoT reference architecture OpenPnP, which allows largely automating the configuration and integration tasks of industrial commissioning processes. The architecture includes a number of design and technology decisions and the required implementation can be scaled down to resource‐constrained industrial devices. This paper demonstrates how OpenPnP can reduce configuration and integration efforts up to 90% in typical settings, while potentially scaling well up to tens of thousands of communicated signals. Practitioners can orient their implementations towards OpenPnP, therefore potentially enabling “plug‐and‐produce” in many thousands of control systems.

The OPC UA communication architecture is currently becoming an integral part of industrial automation systems, which control complex production processes, such as electric power generation or paper production. With a recently released extension for pub/sub communication, OPC UA can now also support fast cyclic control applications, but the bottlenecks of OPC UA implementations and their scalability on resource-constrained industrial devices are not yet well understood. Former OPC UA performance evaluations mainly concerned client/server round-trip times or focused on jitter, but did not explore resource bottlenecks or create predictive performance models. We have carried out extensive performance measurements with OPC UA client/server and pub/sub communication and created a CPU utilization prediction model based on linear regression that can be used to size hardware environments. We found that the server CPU is the main bottleneck for OPC UA pub/sub communication, but allows a throughput of up to 40,000 signals per second on a Raspberry Pi Zero. We also found that the client/server session management overhead can severely impact performance, if more than 20 clients access a single server.

Today, software components are provided by global markets in the form of services. In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester's requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user's decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach.

The Collaborative Research Centre "On-The-Fly Computing" works on foundations and principles for the vision of the Future Internet. It proposes the paradigm of On-The-Fly Computing, which tackles emerging worldwide service markets. In these markets, service providers trade software, platform, and infrastructure as a service. Service requesters state requirements on services. To satisfy these requirements, the new role of brokers, who are (human) actors building service compositions on the fly, is introduced. Brokers have to specify service compositions formally and comprehensively using a domain-specific language (DSL), and to use service matching for the discovery of the constituent services available in the market. The broker's choice of the DSL and matching approaches influences her success of building compositions as distinctive properties of different service markets play a significant role. In this paper, we propose a new approach of engineering a situation-specific DSL by customizing a comprehensive, modular DSL and its matching for given service market properties. This enables the broker to create market-specific composition specifications and to perform market-specific service matching. As a result, the broker builds service compositions satisfying the requester's requirements more accurately. We evaluated the presented concepts using case studies in service markets for tourism and university management.

Nowadays, many service providers offer software components in the form of Software as a Service. Requesters that want to discover those services in order to use or to integrate them, need to find out which service satisfies their requirements best. For this purpose, service matching approaches determine how well the specifications of provided services satisfy their requirements (including structural, behavioral, and non-functional requirements). In this paper, we describe the tool-suite MatchBox that allows the integration of existing service matchers and their combination as part of flexibly configurable matching processes. Taking requirements and service specifications as an input, MatchBox is able to execute such matching processes and deliver rich matching results. In contrast to related tools, MatchBox allows users to take into account many different kinds of requirements, while it also provides the flexibility to control the matching process in many different ways.

Today, software components are traded on markets in form of services. These services can be service compositions consisting of several services. If a software architect wants to provide such a service composition in the market for trade, she needs to perform several tasks: she needs to model the composition, to discover existing services to be part of that composition, and to analyze the composition's functional correctness as well as its quality, e.g., performance. Up to now, the architect needed to find and use different tools for these tasks. Typically, these tools are not interoperable with each other. We provide the tool SeSAME that supports a software architect in all of these tasks. SeSAME is an integrated Eclipse-based tool-suite providing a comprehensive service specification language to model service compositions and existing services. Furthermore, it includes modules for service matching, functional analysis, and non-functional analysis. SeSAME is the first tool that integrates all these tasks into one tool-suite and, thereby, provides holistic support for trading software services. Thus, it contributes to the acceptance and success of a service market.

One of the main ideas of Service-Oriented Computing (SOC) is the delivery of flexibly composable services provided on world-wide markets. For a successful service discovery, service requests have to be matched with the available service offers. However, in a situation in which no service that completely matches the request can be discovered, the customer may tolerate slight discrepancies between request and offer. Some existing fuzzy matching approaches are able to detect such service variants, but they do not allow to explicitly specify which parts of a request are not mandatory. In this paper, we improve an existing service matching approach based on Visual Contracts leveraging our preliminary work of design pattern detection. Thereby, we support explicit specifications of service variants and realize gradual matching results that can be ranked in order to discover the service offer that matches a customer's request best.

In the future vision of software engineering, services from world-wide markets are composed automated in order to build custom-made systems. Supporting such scenarios requires an adequate service matching approach. Many existing approaches do not fulfill two key requirements of emerging concepts like On-The-Fly-Computing, namely (1) comprehensiveness, i.e., the consideration of different service views that cover not only functional properties, but also non-functional properties and (2) fuzzy matching, i.e., the ability to deliver gradual results in order to cope with a certain extent of uncertainty, incompleteness, and tolerance ranges. In this paper, I present a fuzzy matching process that distinguishes between different fuzziness sources and leverages fuzziness in different matching steps which consider several service views, e.g., behavior and quality properties.