När förutsättningarna för ett orkestrerat system förändras blir dess orkestreringskonfiguration inaktuell och måste uppdateras (Delsing, 2017). Ofta är denna konfiguration manuell. Syftet med den här studien är att utforska hur konfiguration av orkestreringssystem kan automatiseras med hjälp av ett konfigurationsverktyg.  Under arbetets gång har ett verktyg som kan konfigureras för att tillsammans med metadata om tjänsteinstanser generera en ny orkestreringskonfiguration utvecklats och demonstrerats genom att presenteras för utvecklare. Verktyget kan användas för att automatiskt anpassa konfigurationen hos ett orkestreringssystem efter ändrade förutsättningar.

The use of micro controllers in automotive application have exploded during the last half century. What was initially a set of mechanical systems that formed a vehicle have now become a collection of computers on wheels. The reason is quite obvious: micro controllers use several inputs to optimize the performance of systems; for example an engine control or an active safety system.The different inputs and outputs to these electronic units (electronic control unit, ECU) are of interest to other such units thereby justifying the need of inter-ECU communications. The Controller Area Network (CAN) bus has been developed to facilitate this communication. It is a message based protocol and is very resilient. It is however relatively slow and limited in terms of security. Security is assured only by trying to keep the message identification tags confidential and the bus physically separated to other network. A couple of decades ago our society embraced the Information Technology (IT) revolution. It allowed people to have extensive access to information. From a technology point of view, IT is based on the use of the Internet, which has been initially designed by the US military for robust applications. It is fast and its security is sufficiently high that we use it to communicate with our banks where we keep all our life savings.The aim of this thesis has been to combine these technologies such that a vehicle with a CAN bus could offer services (just like a bank does) over the Internet. The goal then is to transform a CAN bus to become a service provider over the Internet. The services are the broadcasted CAN messages made available to authorized interested parties and can post information and actuations to the ECUs connected to the CAN bus. A vehicle in that case becomes a cyber physical system. To make this transformation possible, we use the open source Arrowhead Framework, which is based on a Service Oriented Architecture (SOA). The available services are made known via a Service Registry and Orchestration service prosumers. Concretely, the work in this thesis project has been to develop (i.e., to design and implement) a CAN service prosumer that is Arrowhead Framework compliant. It has been successfully tested with another service prosumer, which is an Arrowhead Framework compliant data logger. The driving motivation for the thesis project are construction equipment machines, such as wheel loaders and excavators, which are vehicles with booms or arms. The aspiration is that they not only drive autonomously but also dig autonomously. This ambition shall require large amount of data to be exchanged, something that a CAN bus cannot handle.

As Industry 4.0 and Internet of Things are established across factories and enterprises, the interest for learning more about these concepts and the possibilities they provide for condition based maintenance is expressed by a factory in Sweden. By addressing the aspects of Internet of Things and Industry 4.0, a system for performing data acquisition from sensors in an industrial environment is developed using Arrowhead Framework. This framework is evaluated around its suitability for this kind of application, and regarding what the framework may provide to the factory compared to other solutions and systems. A solution featuring a system based on Arrowhead Framework is developed, implemented, and briefly tested. The system is successful in performing data acquisition, and Arrowhead Framework is considered a viable option that may be used to provide a system tailored for different purposes, presumed that the factory is prepared to allocate resources on developing a solution around it.

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Throughout history, humans have proven to develop and improve their quality of life constantly. Incorporating mechanical principles into otherwise manual labor was one such aspect. Today, introducing mechanization to the industry and steam power is regarded as the first industrial revolution. Since then, a second and a third have followed, introducing concepts such as electricity, automation, computers, and computerization. Finally, in 2011 the fourth industrial revolution was introduced as Industry 4.0 by a project started in Germany, which considers digitalization. Industry 4.0 uses concepts such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), and cloud computing. The goal of these concepts is to be able to further the capabilities of automation in the industry, creating smart factories. A problem experienced today when making changes to the production line is that an immense amount of hard coding is required for the Programmable Logic Controller (PLC) controlling the system as well as for the system controlling the factory. The Eclipse Arrowhead Framework addresses these issues by applying a particular set of parameters to all systems within the IT infrastructure. The Arrowheadframework offers parameters such as orchestrator, authorization, and a service registry to each system incorporated within the framework. By applying this, the System of Systems (SoS) and the parameters provided by the Arrowhead framework create a local cloud. In doing so, each system within a factory belongs to its local cloud, building a factory made of a cluster of local clouds. By applying the ideas of Industry 4.0, a proof of concept is created, showing that today’s industry would benefit from using the concepts stated above with digital twins. The thesis explores and proves that, with the help of digital twins, today’s industry can cut down on manufacturing process development. In addition, it reduces the time it takes to apply changes to the production line by enabling machine learning to facilitate human interaction. A model factory created by fischertechnik is used, together with a PLC, as the physical twin in this master thesis. In addition, using Siemens Computer-Aided Design (CAD) software NX, a digital twin is created; this digital twin will communicate with the physical twin to simulate the behavior of the physical twin in real-time. A server has been created and is acting as a hub of communication for both of the twins. The use of the digital twin to communicate with the physical twin will enable tremendous advances in automation regarding industry 4.0.

With the rise of Industry 4.0 and the 4:th industrial revolution withthe Internet of Things, infrastructures have become more prevalent to connect and monitor many different systems within an industrial set-ting. With many candidates for this IoT infrastructure, there is a need to evaluate the different candidates to determine the different strengthsand weaknesses of the infrastructure.This thesis investigates the use of the Eclipse Arrowhead framework in the application of scalable infrastructure used for predictive mainte-nance. This investigation is conducted by converting an existing pre-dictive maintenance implementation that is using Amazon Web Services as the IoT infrastructure into a predictive maintenance implementationusing the Eclipse Arrowhead framework as the infrastructure.This design science artifact which results from this thesis shows that the Eclipse Arrowhead framework is suitable for a scalable infrastruc-ture though some shortcomings of the framework were found during the implementation. And though it is a suitable infrastructure, the usage ofthe framework should depend on the specific needs of the infrastructureand should not be used as a “one size fits all” solution.

Cyber-physical systems (CPS) connect the physical and digital domains and are often realized as spatially distributed. CPS is built on the Internet of Things (IoT) and Internet of Services, which use cloud architecture to link a swarm of devices over a decentralized network. Modern CPSs are undergoing a foundational shift as Industry 4.0 is continually expanding its boundaries of digitization. From automating the industrial manufacturing process to interconnecting sensor devices within buildings, Industry 4.0 is about developing solutions for the digitized industry. An extensive amount of engineering efforts are put to design dynamically scalable and robust automation solutions that have the capacity to integrate heterogeneous CPS. Such heterogeneous systems must be able to communicate and exchange information with each other in real-time even if they are based on different underlying technologies, protocols, or semantic definitions in the form of ontologies. This development is subject to interoperability challenges and knowledge gaps that are addressed by engineers and researchers, in particular, machine learning approaches are considered to automate costly engineering processes. For example, challenges related to predictive maintenance operations and automatic translation of messages transmitted between heterogeneous devices are investigated using supervised and unsupervised machine learning approaches. In this thesis, a machine learning-based collaboration and automation-oriented IIoT framework named Cloud-based Collaborative Learning (CCL) is developed. CCL is based on a service-oriented architecture (SOA) offering a scalable CPS framework that provides machine learning-as-a-Service (MLaaS). Furthermore, interoperability in the context of the IIoT is investigated. I consider the ontology of an IoT device to be its language, and the structure of that ontology to be its grammar. In particular, the use of aggregated language and structural encoders is investigated to improve the alignment of entities in heterogeneous ontologies. Existing techniques of entity alignment are based on different approaches to integrating structural information, which overlook the fact that even if a node pair has similar entity labels, they may not belong to the same ontological context, and vice versa. To address these challenges, a model based on a modification of the BERT_INT model on graph triples is developed. The developed model is an iterative model for alignment of heterogeneous IIoT ontologies enabling alignments within nodes as well as relations. When compared to the state-of-the-art BERT_INT, on DBPK15 language dataset the developed model exceeds the baseline model by (HR@1/10, MRR) of 2.1%. This motivated the development of a proof-of-concept for conducting an empirical investigation of the developed model for alignment between heterogeneous IIoT ontologies. For this purpose, a dataset was generated from smart building systems and SOSA and SSN ontologies graphs. Experiments and analysis including an ablation study on the proposed language and structural encoders demonstrate the effectiveness of the model. The suggested approach, on the other hand, highlights prospective future studies that may extend beyond the scope of a single thesis. For instance, to strengthen the ablation study, a generalized IIoT ontology that is designed for any type of IoT devices (beyond sensors), such as SAREF can be tested for ontology alignment. Next potential future work is to conduct a crowdsourcing process for generating a validation dataset for IIoT ontology alignment and annotations. Lastly, this work can be considered as a step towards enabling translation between heterogeneous IoT sensor devices, therefore, the proposed model can be extended to a translation module in which based on the ontology graphs of any device, the model can interpret the messages transmitted from that device. This idea is at an abstract level as of now and needs extensive efforts and empirical study for full maturity.