Towards Increased Flexibility and Interoperability in Distributed Process Control Applications

The modern process automation plants are changing into flexible designs, which raises the requirements for distributively controlled logic, a high degree of interoperability, dynamic reconfiguration and software reusability. Thus, creating an opportunity to integrate the distributed control system standards and platform independent communication protocols. In this paper, we propose the use of OPC UA to increase interoperability of communication and the utilization of Arrowhead Framework to enhance interoperable service compositions of control applications implemented in IEC 61499. The concept is outlined for the integration and modeling of a distributed control system for a FESTO laboratory batch process system. A control application example is provided to create distributed control of Cyber-Physical Systems using services that are connected using IEC 61499 in accordance to Industry 4.0 for improved interoperability and flexibility.acceptedVersionPeer reviewe

Evaluating Arrowhead Framework for Dynamic Condition Monitoring Applications and Edge Computing in Mining

Condition monitoring and predictive maintenance of the equipment is an important topic of production environments, and many equipment manufacturers offer data services and build service business upon them. To further advance Metso's capabilities, the configuration on how the data collected at the field, and how it is moved to the cloud for further processing and analysis, needs more advanced solutions. Typical equipment manufacturer's customers from all over the world have numerous pieces of equipment on their sites. In this paper Arrowhead Framework and dynamic service discovery provided by it, are evaluated as a potential technology that could help machine and equipment providers to achieve better configurability of its data collection devices at the edge of the network.acceptedVersionPeer reviewe

Edge to Cloud Tools: A Multivocal Literature Review

Edge-to-cloud computing is an emerging paradigm for distributing computational tasks between edge devices and cloud resources. Different approaches for orchestration, offloading, and many more purposes have been introduced in research. However, it is still not clear what has been implemented in the industry. This work aims to merge this gap by mapping the existing knowledge on edge-to-cloud tools by providing an overview of the current state of research in this area and identifying research gaps and challenges. For this purpose, we conducted a Multivocal Literature Review (MLR) by analyzing 40 tools from 1073 primary studies (220 PS from the white literature and 853 PS from the \rc{grey} literature). We categorized the tools based on their characteristics and targeted environments. Overall, this systematic mapping study provides a comprehensive overview of edge-to-cloud tools and highlights several opportunities for researchers and practitioners for future research in this area

Cognitive Cloud: The Definition

Cognitive Cloud has drawn increasing attention from practitioners, academics, and funding agencies and has been adopted progressively. However, the concept remains mired in various definitions with different studies providing contrasting descriptions. Therefore, to understand the concept of cognitive cloud and to provide its definition, in this work we conduct a systematic mapping study of the literature investigating 24 papers proposing five main definitions. The main outcome of this work is a complete definition that merges all the common aspects of cognitive cloud, enabling practitioners and researchers to better understand what cognitive cloud is.Peer reviewe

Monitoring of production processes and the condition of the production equipment through the internet

The decreasing prices of monitoring equipment have vastly increased the opportunities to utilize local data, and data processing for wider global web-based monitoring purposes. The possible amount of data flowing though different levels can be huge. Now the question is how to handle this opportunity in both dynamic and secure way. The paper presents a new concept to manage data for monitoring through the Internet. The concept is based on the use of Arrowhead Framework (AF) and MIMOSA data model, and selected edge, and gateway devices together with cloud computing opportunities. The concept enables the flexible and secure orchestration of run-time data sources and the utilization of computational services for various process and condition monitoring needs.acceptedVersionPeer reviewe