Showing papers on "Reference architecture published in 2020"

Blockchain for Industry 4.0: A Comprehensive Review

Abstract: Due to the proliferation of ICT during the last few decades, there is an exponential increase in the usage of various smart applications such as smart farming, smart healthcare, supply-chain & logistics, business, tourism and hospitality, energy management etc. However, for all the aforementioned applications, security and privacy are major concerns keeping in view of the usage of the open channel, i.e., Internet for data transfer. Although many security solutions and standards have been proposed over the years to enhance the security levels of aforementioned smart applications, but the existing solutions are either based upon the centralized architecture (having single point of failure) or having high computation and communication costs. Moreover, most of the existing security solutions have focussed only on few aspects and fail to address scalability, robustness, data storage, network latency, auditability, immutability, and traceability. To handle the aforementioned issues, blockchain technology can be one of the solutions. Motivated from these facts, in this paper, we present a systematic review of various blockchain-based solutions and their applicability in various Industry 4.0-based applications. Our contributions in this paper are in four fold. Firstly, we explored the current state-of-the-art solutions in the blockchain technology for the smart applications. Then, we illustrated the reference architecture used for the blockchain applicability in various Industry 4.0 applications. Then, merits and demerits of the traditional security solutions are also discussed in comparison to their countermeasures. Finally, we provided a comparison of existing blockchain-based security solutions using various parameters to provide deep insights to the readers about its applicability in various applications.

Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing

Abstract: Digital twin (DT) is gaining popularity due to its significant impacts on bridging the gap between the physical and cyber worlds. As reported by Grand View Research, Inc., the global market of DT is expected to reach $26.07 billion by 2025 with a Compound Annual Growth Rate of 38.2%. The growing adoption of cyber-physical system (CPS), Internet of Things, big data analytics, and cloud computing in manufacturing sector has paved the way for low cost and systematic implementation of DT, with promising impacts on (a) product design and development, (b) machine and equipment health monitoring, and (c) product support and services. Successful implementation of DT would increase transparency, cooperation, flexibility, resilience, production speed, scalability, and manufacturing efficiency. Realisation of smart manufacturing requires collaborative and autonomous interactions between sensing, networking, and computational resources across manufacturing assets where data is gathered from physical systems is utilised for the extraction of actionable insights and provision of predictive services. In this study, a reference architecture based on deep learning, DT, and 5C-CPS is proposed to facilitate the transformation towards smart manufacturing and Industry 4.0.

Model-Driven Development of a Digital Twin for Injection Molding

Abstract: Digital Twins (DTs) of Cyber-Physical Production Systems (CPPSs) enable the smart automation of production processes, collection of data, and can thus reduce manual efforts for supervising and controlling CPPSs. Realizing DTs is challenging and requires significant efforts for their conception and integration with the represented CPPS. To mitigate this, we present an approach to systematically engineering DTs for injection molding that supports domain-specific customizations and automation of essential development activities based on a model-driven reference architecture. In this approach, reactive CPPS behavior is defined in terms of a Domain-Specific Language (DSL) for specifying events that occur in the physical system. The reference architecture connects to the CPPS through a novel DSL for representing OPC-UA bindings. We have evaluated this approach with a DT of an injection molding machine that controls the machine to optimize the Design of Experiment (DoE) parameters between experiment cycles before the products are molded. Through this, our reference implementation of the DT facilitates the time-consuming setup of a DT and the subsequent injection molding activities. Overall, this facilitates to systematically engineer digital twins with reactive behavior that help to optimize machine use.

Generic Digital Twin Architecture for Industrial Energy Systems

Abstract: Digital Twins have been in the focus of research in recent years, trying to achieve the vision of Industry 4.0. In the domain of industrial energy systems, they are applied to facilitate a flexible and optimized operation. With the help of Digital Twins, the industry can participate even stronger in the ongoing renewable energy transition. Current Digital Twin implementations are often application-specific solutions without general architectural concepts and their structures and namings differ, although the basic concepts are quite similar. For this reason, we analyzed concepts, architectures, and frameworks for Digital Twins in the literature to develop a technology-independent Generic Digital Twin Architecture (GDTA), which is aligned with the information technology layers of the Reference Architecture Model Industry 4.0 (RAMI4.0). This alignment facilitates a common naming and understanding of the proposed architectural structure. A proof-of-concept shows the application of Semantic Web technologies for instantiating the proposed GDTA for a use case of a Packed-Bed Thermal Energy Storage (PBTES).

Toward Distributed Computing Environments with Serverless Solutions in Edge Systems

Abstract: Computation offloading through stateless applications is gaining momentum thanks to the emergence of serverless frameworks with inherent scalability properties. However, adoption of a serverless framework in an edge computing system requires careful consideration to keep its advantages unscathed. In the cloud, micro-services are scaled automatically according to demands, but in edge computing this would incur a significantly higher cost than in a data center and cannot be as fluid. This is especially relevant in scenarios where edge nodes are spread across large areas and have relatively small computation capabilities. In this article we propose to overcome this issue by adapting the allocation of demands to the currently allocated micro-services at short timescales, with two alternative mechanisms designed for different target scenarios, both aimed at enabling distributed computing environments. The proposed solution can be integrated within the ETSI MEC standard, which specifies a reference architecture and open service interfaces. Our contribution is validated in a proof-of-concept scenario with a prototype implementation released as open source.

Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain.

Abstract: The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.

IoVT: Internet of Vulnerable Things? Threat Architecture, Attack Surfaces, and Vulnerabilities in Internet of Things and Its Applications towards Smart Grids

Abstract: In recent years, people have witnessed numerous Internet of Things (IoT)-based attacks with the exponential increase in the number of IoT devices. Alongside this, the means to secure IoT-based applications are maturing slower than our budding dependence on them. Moreover, the vulnerabilities in an IoT system are exploited in chains to penetrate deep into the network and yield more adverse aftereffects. To mitigate these issues, this paper gives unique insights for handling the growing vulnerabilities in common IoT devices and proposes a threat architecture for IoT, addressing threats in the context of a three-layer IoT reference architecture. Furthermore, the vulnerabilities exploited at the several IoT attack surfaces and the challenges they exert are explored. Thereafter, the challenges in quantifying the IoT vulnerabilities with the existing framework are also analyzed. The study also covers a case study on the Intelligent Transportation System, covering road transport and traffic control specifically in terms of threats and vulnerabilities. Another case study on secure energy management in the Smart Grid is also presented. This case study covers the applications of Internet of Vulnerable Things (IoVT) in Smart energy Grid solutions, as there will be tremendous use of IoT in future Smart Grids to save energy and improve overall distribution. The analysis shows that the integration of the proposed architecture in existing applications alarms the developers about the embedded threats in the system.

Shipping 4.0: Security Requirements for the Cyber-Enabled Ship

Abstract: The cyber-enabled ship (C-ES) is either an autonomous or a remotely controlled vessel which relies on interconnected cyber physical-systems for its operations. Such systems are not well protected against cyberattacks. Considering the criticality of the functions that such systems provide, it is important to address their security challenges, thereby ensuring the ship's safe voyage. In this article, we leverage the maritime architectural framework reference architecture to analyze and describe the environment of the C-ES. We then apply the Secure Tropos methodology to systematically elicit the security requirements of the three most vulnerable cyber–physical systems (CPSs) onboard a C-ES, namely the automatic identification system (AIS), the electronic chart display information system, and the global maritime distress and safety system. The outcome is a set of cyber-security requirements for the C-ES ecosystem in general and these systems in particular.

The FORA Fog Computing Platform for Industrial IoT.

Abstract: Industry 4.0 will only become a reality through the convergence of Operational and Information Technologies (OT & IT), which use different computation and communication technologies. Cloud Computing cannot be used for OT involving industrial applications, since it cannot guar-antee stringent non-functional requirements, e.g., dependability, trustworthiness and timeliness. Instead, a new computing paradigm, called Fog Computing, is envisioned as an architectural means to realize the IT/OT convergence. In this paper we propose a Fog Computing Platform (FCP) reference architecture targeting Industrial IoT applications. The FCP is based on: deter-ministic virtualization that reduces the effort required for safety and security assurance; middle-ware for supporting both critical control and dynamic Fog applications; deterministic networking and interoperability, using open standards such as IEEE 802.1 Time-Sensitive Networking (TSN) and OPC Unified Architecture (OPC UA); mechanisms for resource management and or-chestration; and services for security, fault tolerance and distributed machine learning. We pro-pose a methodology for the definition and the evaluation of the reference architecture. We use the Architecture Analysis Design Language (AADL) to model the FCP reference architecture, and a set of industrial use cases to evaluate its suitability for the Industrial IoT area.

A Model for Predictive Maintenance Based on Asset Administration Shell.

Abstract: Maintenance is one of the most important aspects in industrial and production environments. Predictive maintenance is an approach that aims to schedule maintenance tasks based on historical data in order to avoid machine failures and reduce the costs due to unnecessary maintenance actions. Approaches for the implementation of a maintenance solution often differ depending on the kind of data to be analyzed and on the techniques and models adopted for the failure forecasts and for maintenance decision-making. Nowadays, Industry 4.0 introduces a flexible and adaptable manufacturing concept to satisfy a market requiring an increasing demand for customization. The adoption of vendor-specific solutions for predictive maintenance and the heterogeneity of technologies adopted in the brownfield for the condition monitoring of machinery reduce the flexibility and interoperability required by Industry 4.0. In this paper a novel approach for the definition of a generic and technology-independent model for predictive maintenance is presented. Such model leverages on the concept of the Reference Architecture Model for Industry (RAMI) 4.0 Asset Administration Shell, as a means to achieve interoperability between different devices and to implement generic functionalities for predictive maintenance.

Research on digital production technology for traditional manufacturing enterprises based on industrial Internet of Things in 5G era

Abstract: Based on the analysis of 5G and Internet of Things technology, this paper proposes the reference architecture of smart factory and its application path for traditional manufacturing enterprises in China, in which the intelligent manufacturing workshop is the core component of smart factory. The Internet of Things technology combined the advanced technologies (Industrial Big Data, WSN, RFID, Cloud Computing Platform) and provides hardware network foundation and technical theory for designing the real-time tracking and monitoring system of intelligent workshop products. The developed system has the advantages of low cost, rapid deployment, and convenient expansion, which traditional manufacturing enterprises realize intelligent management based on IoT application platform.

The HORSE Project: The application of Business Process Management for flexibility in Smart Manufacturing

Abstract: Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products These technologies include configurable robots, augmented reality and the Internet-of-Things Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, ie, the ability to perform a larger variety of activities within a shorter time However, the flexibility offered by these new technologies is not fully exploited, because current operations management techniques are not dynamic enough to support high variability and frequent change The HORSE Project investigated several of the new technologies to find novel ways to improve flexibility, as part of the Horizon 2020 research and innovation program The purpose of the project was to develop a system, integrating these new technologies, to support efficient and flexible manufacturing This article presents the core result of the project: a reference architecture for a manufacturing operations management system It is based on the application and extension of business process management (BPM) to manage dynamic manufacturing processes It is argued that BPM can complement current operations management techniques by acting as an orchestrator in manufacturing processes augmented by smart technologies Building on well-known information systems’ architecting frameworks, design science research is performed to determine how BPM can be applied and adapted in smart manufacturing operations The resulting reference architecture is realized in a concrete HORSE system and deployed and evaluated in ten practical cases, of which one is discussed in detail It is shown that the developed system can flexibly orchestrate the manufacturing process through vertical control of all agents, and dynamic allocation of agents in the manufacturing process Based on that, we conclude that BPM can be applied to overcome some of the obstacles toward increased flexibility and smart manufacturing

Insights into Mapping Solutions Based on OPC UA Information Model Applied to the Industry 4.0 Asset Administration Shell

Abstract: In the context of Industry 4.0, lot of effort is being put to achieve interoperability among industrial applications. As the definition and adoption of communication standards are of paramount importance for the realization of interoperability, during the last few years different organizations have developed reference architectures to align standards in the context of the fourth industrial revolution. One of the main examples is the reference architecture model for Industry 4.0, which defines the asset administration shell as the corner stone of the interoperability between applications managing manufacturing systems. Inside Industry 4.0 there is also so much interest behind the standard open platform communications unified architecture (OPC UA), which is listed as the one recommendation for realizing the communication layer of the reference architecture model. The contribution of this paper is to give some insights behind modelling techniques that should be adopted during the definition of OPC UA Information Model exposing information of the very recent metamodel defined for the asset administration shell. All the general rationales and solutions here provided are compared with the current OPC UA-based existing representation of asset administration shell provided by literature. Specifically, differences will be pointed out giving to the reader advantages and disadvantages behind each solution.

A Systematic Literature Review on Existing Digital Government Architectures: State-of-the-Art, Challenges, and Prospects

Abstract: System architecture plays a crucial role in the establishment of Digital Government infrastructure. Over recent decades, various architectures have been introduced by scholars for the establishment of Digital Government infrastructure. However, there is no uniform agreement on Digital Government architecture concepts required for Digital Government infrastructure. To more thoroughly examine the Digital Government architecture introduced in this article, we collected 103 papers published between 2003 and 2020 retrieved from five leading literature databases. To conduct our research, we followed best practice scholarly accepted guidelines for researchers. Per the guidelines, we formulated research questions and employed an approach based on specific inclusion and exclusion criteria to meet our research goals. Our study found evidence that there is a lack of knowledge in terms of the state-of-the-art in Digital Government infrastructure and its challenges concerning existing Digital Government architectures. We identified a set of primary Digital Government architecture characteristics and building blocks on which the Digital Government infrastructures are built. These components are meant to improve the design of future Digital Government systems and applications. Furthermore, our research revealed a need for designing a reference architecture to provide government organizations with the best practice knowledge of already existing Digital Government architectures.

Seaport Data Space for Improving Logistic Maritime Operations

Abstract: The maritime industry expects several improvements to efficiently manage the operation processes by introducing Industry 4.0 enabling technologies. Seaports are the most critical point in the maritime logistics chain because of its multimodal and complex nature. Consequently, coordinated communication among any seaport stakeholders is vital to improving their operations. Currently, Electronic Data Interchange (EDI) and Port Community Systems (PCS), as primary enablers of digital seaports, have demonstrated their limitations to interchange information on time, accurately, efficiently, and securely, causing high operation costs, low resource management, and low performance. For these reasons, this contribution presents the Seaport Data Space (SDS) based on the Industrial Data Space (IDS) reference architecture model to enable a secure data sharing space and promote an intelligent transport multimodal terminal. Each seaport stakeholders implements the IDS connector to take part in the SDS and share their data. On top of SDS, a Big Data architecture is integrated to manage the massive data shared in the SDS and extract useful information to improve the decision-making. The architecture has been evaluated by enabling a port authority and a container terminal to share its data with a shipping company. As a result, several Key Performance Indicators (KPIs) have been developed by using the Big Data architecture functionalities. The KPIs have been shown in a dashboard to allow easy interpretability of results for planning vessel operations. The SDS environment may improve the communication between stakeholders by reducing the transaction costs, enhancing the quality of information, and exhibiting effectiveness.

Extending reference architecture of big data systems towards machine learning in edge computing environments

Abstract: Augmented reality, computer vision and other (e.g. network functions, Internet-of-Things (IoT)) use cases can be realised in edge computing environments with machine learning (ML) techniques. For realisation of the use cases, it has to be understood how data is collected, stored, processed, analysed, and visualised in big data systems. In order to provide services with low latency for end users, often utilisation of ML techniques has to be optimized. Also, software/service developers have to understand, how to develop and deploy ML models in edge computing environments. Therefore, architecture design of big data systems to edge computing environments may be challenging. The contribution of this paper is reference architecture (RA) design of a big data system utilising ML techniques in edge computing environments. An earlier version of the RA has been extended based on 16 realised implementation architectures, which have been developed to edge/distributed computing environments. Also, deployment of architectural elements in different environments is described. Finally, a system view is provided of the software engineering aspects of ML model development and deployment. The presented RA may facilitate concrete architecture design of use cases in edge computing environments. The value of RAs is reduction of development and maintenance costs of systems, reduction of risks, and facilitation of communication between different stakeholders.

Study on the Reference Architecture and Assessment Framework of Industrial Internet Platform

Abstract: With the continuous innovation of new-generation information technology and its accelerated integration with manufacturing industry, industrial internet platforms (IIPs) are rapidly emerging worldwide. Construction and application of IIP has become a new focus in international competition for leading enterprises, and also a new direction of industrial development for many countries worldwide. However, the development of IIP is still in the stage of exploration, and the industry sector still lacks unified understanding of the IIP. Therefore, this study firstly proposes a reference architecture of IIP to clarify its framework and core functions, so as to provide a general reference model for the industry to understand and jointly promote construction of IIP. Secondly, an assessment system is proposed to evaluate the usage of IIP. The assessment framework is composed from three domains namely the foundation, key capability, value and benefit. Finally, the practical value of the reference architecture and the assessment framework of IIP is verified by an industry practice.

Tracing Your Smart-Home Devices Conversations: A Real World IoT Traffic Data-Set.

Abstract: Smart-home installations exponential growth has raised major security concerns. To this direction, the GHOST project, a European Union Horizon 2020 Research and Innovation funded project, aims to develop a reference architecture for securing smart-homes IoT ecosystem. It is required to have automated and user friendly security mechanisms embedded into smart-home environments, to protect the users' digital well being. GHOST project aims to fulfill this requirement and one of its main functionalities is the traffic monitoring for all IoT related network protocols. In this paper, the traffic capturing and monitoring mechanism of the GHOST system, called NDFA, is presented, as the first mechanism that is able to monitor smart-home activity in a holistic way. With the help of the NDFA, we compile the GHOST-IoT-data-set, an IoT network traffic data-set, captured in a real world smart-home installation. This data-set contains traffic from multiple network interfaces with both normal real life activity and simulated abnormal functioning of the devices. The GHOST-IoT-data-set is offered to the research community as a proof of concept to demonstrate the ability of the NDFA module to process the raw network traffic from a real world smart-home installation with multiple network interfaces and IoT devices.

Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

Abstract: Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a large-scale Air Handling Unit (AHU).

Computational Tools for Modeling and Analysis of Power Generation and Transmission Systems of the Smart Grid

Abstract: The traditional power generation and distribution systems will be supplanted by the Internet of Energy, which accelerates the necessity to know the appropriate computation tools to perform any research in this future smart grid arena. However, there is a plethora of computational tools in this area, which challenges the researchers to find an appropriate tool based on their research objectives. Therefore, this article presents a comprehensive study about existing simulation tools related to electrical power generation, transmission, distribution, and associated systems. It provides an overview of more than 150 simulation software in these areas. The tools are classified and discussed based on both traditional and CEN-CENELEC-ETSI smart-grid reference architecture. Typical applications, sources, availability, and strengths of each tool are listed. Each tool has its own strengths and limitations to perform a certain task, and necessary information are provided to help researchers to find an appropriate computational tool for their specific research goal.

Machine Learning for Predictive Diagnostics at the Edge: an IIoT Practical Example.

Abstract: Edge Computing is becoming more and more essential for the Industrial Internet of Things (IIoT) for data acquisition from shop floors. The shifting from central (cloud) to distributed (edge nodes) approaches will enhance the capabilities of handling real-time big data from IoT. Furthermore, these paradigms allow moving storage and network resources at the edge of the network closer to IoT devices, thus ensuring low latency, high bandwidth, and location-based awareness. This research aims at developing a reference architecture for data collecting, smart processing, and manufacturing control system in an IIoT environment. In particular, our architecture supports data analytics and Artificial Intelligence (AI) techniques, in particular decentralized and distributed hybrid twins, at the edge of the network. In addition, we claim the possibility to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models and to store them at the edge. Furthermore, edges have the possibility of improving the global model (stored at the cloud) by sending the reinforced local models (stored in different shop floors) towards the cloud. In this paper, we describe our architectural proposal and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. Reported experimental results show the potential advantages of using the proposed approach for dynamic model reinforcement by using real-time data from IoT instead of using an offline approach at the cloud infrastructure.

The NIST Cloud Federation Reference Architecture

Abstract: This document presents the NIST Federated Cloud Reference Architecture model. This actor/role-based model used the guiding principles of the NIST Cloud Computing Reference Architecture to develop an eleven component model. This document describes these components individually and how they function as an ensemble. There are many possible deployments and governance options which lend themselves to create a suite of federation options from simple to complex. The basics of cloud federation can be described through the interactions of the actors in a layered three planes representation of trust, security, and resource sharing and usage. A discussion on possible future standards and use cases are also described in great detail.