The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the "things" appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible. 

The evolution of the current technological landscape has opened an emergent paradigm that enables interoperability between the digital and physical world, leading to a new generation of industrial systems. This new digitalization era marks the beginning of the fourth industrial revolution, usually referred to as "Industry 4.0". By employing recent technologies and concepts such as Industrial Internet of Things (IIoT), Cyber-physical Systems (CPS), Cloud-based technologies, , Service-oriented Architecture (SoA), and Artificial Intelligence (AI), the Industry 4.0 approach aims to address the dynamic evolution of contemporary requirements as well as improve the sustainability and efficiency in industrial production. 

While this new industrial paradigm facilitates the integration and collaboration among industrial components, it also introduces greater complexity to the industrial systems, thereby potentially increasing costs related to system development and maintenance. Specifically, significant engineering effort is dedicated to addressing the heterogeneity, interoperability and scalability of those integrated components. As a result, in order to mitigate those challenges, the self-adaptive solution appears as a potential approach to automate the management and supervision of the systems. Self-adaptation allows the system to adapt in the face of changes in its operating environment and in the system itself without human intervention.

This thesis outlines the progress made towards self-adaptation in industrial production. It proposes an architectural design that enables dynamic adaptation for IIoT systems. Particularly, in order to facilitate the integration of heterogeneous and numerous physical components, the proposed approach shifts from tightly-coupled automation systems to loosely-coupled flexible information and communication infrastructure by employing service-oriented and decentralized technologies. Furthermore, the concept of Autonomic Computing (AC) is exploited to address the interoperability among the systems with the goal to enable autonomous decision-making based on real-time information from the integrated components.   

To illustrate the potential of this design, an Autonomic Adaptation System is proposed to provide dynamic adaptation as a service in order to assist IIoT systems to re-orchestrate the communication among them or re-configure their internal functionality. The prototype of the system has been implemented and tested with a simulated industrial use case.

The new Industry 4.0 envisions a future for agile and effective integration of the physical operational technologies (OT) and the cyber information technologies (IT) as well as autonomous cooperation among them. However, the wide variety and heterogeneity of industrial systems and field devices - especially on the factory floor - increase integration complexity. To address these challenges, new technologies and concepts such as the Industrial Internet of Things (IIoT), Service-oriented Architecture (SoA), Semantic Technologies, Machine Learning and Artificial Intelligence are being introduced to the industrial environment. In this paper, we focus on how industrial automation systems and field devices can be integrated into the IIoT framework and coordinated to adapt to dynamic operating environment. Specifically, this paper proposed an interoperability solution that makes use of SoA and Semantic Technologies to achieve supervised coordination of IIoT application systems. To illustrate the potential of this approach, the Service-oriented Architecture-based Arrowhead Framework is used as the fundamental framework for the implementation of the approach.

In this paper we present the results of a feature importance analysis of a chemical sulphonation process. The task consists of predicting the neutralization number (NT), which is a metric that characterizes the product quality of active detergents. The prediction is based on a dataset of environmental measurements, sampled from an industrial chemical process. We used a soft-sensing approach, that is, predicting a variable of interest based on other process variables, instead of directly sensing the variable of interest. Reasons for doing so range from expensive sensory hardware to harsh environments, e.g., inside a chemical reactor. The aim of this study was to explore and detect which variables are the most relevant for predicting product quality, and to what degree of precision. We trained regression models based on linear regression, regression tree and random forest. A random forest model was used to rank the predictor variables by importance. Then, we trained the models in a forward-selection style by adding one feature at a time, starting with the most important one. Our results show that it is sufficient to use the top 3 important variables, out of the 8 variables, to achieve satisfactory prediction results. On the other hand, Random Forest obtained the best result when trained with all variables.

Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Semantic technologies have been used to enable interoperability as well as deal with the heterogeneity, massive scale, and dynamic nature of IoT resources. With the emergence of Industry 4.0, semantic technologies arise as a potential approach toward information modeling and dynamic reconfiguration of highly complex automation systems with high diversity of domains, protocols, tools or hardware platforms. Applying semantics into existing IoT frameworks requires a thorough understanding of the framework architectures as well as careful considerations of different semantic technologies. To support this process, we survey the literature on the contributions and usage of semantics in IoT. We find that semantics are mainly used to handle interoperable systems and heterogeneous standards. In this paper, we also propose procedures for applying semantics into IoT frameworks. Further, we present our idea of using semantics to enable dynamical orchestration of services within the Arrowhead Framework - an IoT framework that supports the development of industrial automation systems.

Industrial cyber-physical systems are advancing rapidly along with the emergence of the fourth industrial revolution. It is, therefore, necessary to design and develop appropriate tools and frameworks that allow the migration/integration of legacy systems into the new Industry 4.0 environment. OPC-UA is one of the recommended technologies to enable communication and interoperability of digitalized assets in Industry 4.0. This paper proposes a solution to develop an OPC-UA interface for industrial systems for service-oriented architecture. With the use of the Arrowhead Framework - a cloud-based framework facilitating interoperability and integrability of Industrial Internet of Things - and Industry 4.0-compliant technologies, the authors describe the procedure of developing different application systems which dynamically produce and consume OPC-UA services within a local automation cloud.

Recent advances in the Internet of Things (IoT) has kindled the possibility of a lot of smart industrial systems. With the evolution of these IoT systems in the form of size and complexity, there is a growing need for a high level of interoperability. Autonomic Computing, with the vision of equipping software systems with self-management capabilities, emerges as a potential catalyst to support interoperability. In this paper, we present an approach which exploits Autonomic Computing to facilitate the development of interoperable IoT systems at semantic level. Our approach extends state-of-the-art IoT ontologies as well as Semantic Web Technologies to fit the MAPE-K (Monitor-Analyze-Plan-Execute-Knowledge) paradigm in Autonomic Computing. By using a Smart Home Use Case, the approach is also evaluated under different performance criteria.