Sustainability is a pressing need, as well as an engineering challenge, in the modern world. Developing smart technologies is a critical way to ensure that future manufacturing systems are sustainable. Blockchain is a next-generation development of information technology for realizing sustainability in businesses and industries. Much research on blockchain-empowered sustainable manufacturing in Industry 4.0 has been conducted from technical, commercial, organizational, and operational perspectives. This paper surveys how blockchain can overcome potential barriers to achieving sustainability from two perspectives, namely, the manufacturing system perspective and the product lifecycle management perspective. The survey first examines literature on these two perspectives, following which the state of research in blockchain-empowered sustainable manufacturing is presented, which sheds new light on urgent issues as part of the UN's Sustainable Development Goals. We found that blockchain-empowered transformation of a sustainable manufacturing paradigm is still in an early stage of the hype phase, proceeding toward full adoption. The survey ends with a discussion of challenges regarding techniques, social barriers, standards, and regulations with respect to blockchain-empowered manufacturing applications. The paper concludes with a discussion of challenges and social barriers that blockchain technology must overcome to demonstrate its sustainability in industrial and business spheres.

Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey

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Privacy-Enhancing Technologies

The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real-time. The Cloud-centric execution of IoT applications barely meets such requirements as the Cloud datacentres reside at a multi-hop distance from the IoT devices. \textit{Fog computing}, an extension of Cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed, heterogeneous and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing.

Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions

This article presents the results of a systematic review of the literature pertaining to the use of blockchain technology in the engineering and manufacturing activities. The uses of blockchain have been growing quickly in the literature, and this fast pace can make it difficult for researchers to keep abreast of the state of the art. Using a systematic approach to search the primary electronic indices, the compiled list of papers is then analyzed and a number of themes are identified. The results of this article suggest that the literature surrounding blockchain and the engineering/manufacturing industry can be categorized into three primary groups: blockchain to protect data validity, blockchain to enhance inter and intraorganizational communications, and blockchain to increase the efficiency of the manufacturing process. Within each top-level theme, there exist some subthemes that provide a more finely defined categorization of the papers contained in that theme. These subthemes include specific areas of blockchain application such as additive manufacturing, cloud manufacturing, and building information models (BIMs). For each group of papers identified, each study is discussed briefly. This article concludes with a set of conclusions about the current state of the literature and suggestions for further research directions. The primary contribution of this article is to provide a foundation upon which additional research can be accomplished as well as to provide a resource for practitioners to help them understand the current state of the industry and help them determine how blockchain might be beneficial to their organization.

Engineering and Manufacturing on the Blockchain: A Systematic Review

The FORA Fog Computing Platform for Industrial IoT

This paper introduces a novel Machine Sharing Economy (MSE) approach to support machine owners in sharing idle machine capacities by means of a marketplace. Manufacturing networks and supply chains are thus no longer static and driven by long-term agreements, but rather dynamic and time-bound. In such distributed manufacturing systems, it is important to keep track of the operations that have been performed, including relevant production information such as the actual machines performing a task, tools, and their parameters. We propose a tamper-proof blockchain-based framework to close the feedback cycle by emitting relevant events of the production process and storing process values in the blockchain. This paper describes the overall architecture of the system including a detailed discussion how the feedback cycle for distributed manufacturing systems has been realized using blockchains. In addition, we perform an empirical validation at a real-world digital factory and present our lessons learned.

Process traceability in distributed manufacturing using blockchains

Today many service-based systems follow the microservice architecture style. As microservices are used to build distributed systems and promote architecture properties such as independent service development, polyglot technology stacks including polyglot persistence, and loosely coupled dependencies, architecting data management is crucial in most microservice architectures. Many patterns and practices for microservice data management architectures have been proposed, but are today mainly informally discussed in the so-called “grey literature”: practitioner blogs, experience reports, and system documentations. As a result, the architectural knowledge is scattered across many knowledge sources that are usually based on personal experiences, inconsistent, and, when studied on their own, incomplete. In this paper we report on a qualitative, in-depth study of 35 practitioner descriptions of best practices and patterns on microservice data management architectures. Following a model-based qualitative research method, we derived a formal architecture decision model containing 325 elements and relations. Comparing the completeness of our model with an existing pattern catalog, we conclude that our architectural decision model substantially reduces the effort needed to sufficiently understand microservice data management decisions, as well as the uncertainty in the design process.

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Supporting Architectural Decision Making on Data Management in Microservice Architectures

The Industry 4.0 initiative is pushing traditional production systems and methodologies through fundamental changes. In traditional industries such as manufacturing, utilities, and infrastructure monitoring, customers are demanding more flexibility and greater efficiency. The software that oversees these systems addresses these demands by adopting novel architectural styles and patterns. Industrial edge systems are gaining momentum because they offer superior data security and better response time to field operations. Microservice style has been adopted from cloud services because it improves development efficiency. This paper presents architecture patterns that cover four phases in the life cycle of industrial software. In the deployment phase, DOWNSHIFTING moves microservices from the cloud to the edge. During runtime, MONITORING CONNECTORS oversee properties of microservices at the edge, such as performance and resource usage. When adaptation is needed, EDGE-TO-CLOUD LOAD BALANCING leverages cloud resources to fulfill performance expectations. When a new microservice version is deployed, TEST PROBE enables testing the microservice at the edge without interfering with ongoing operations.This paper is intended for architects, developers and practitioners who are involved in delivering software in industrial edge environments.

Microservice Patterns for the Life Cycle of Industrial Edge Software

Microservices are the go-to architectural style for building applications that are polyglot, support high scalability, independent development and deployment, and are rapidly adaptable to changes. Among the core tenets for a successful microservice architecture is high independence of the individual microservices, i.e. loose coupling. A number of patterns and best practices are well-established in the literature, but most actual microservice-based systems do not, as a whole or in part, conform to them. Assessing this conformance manually is not realistically possible for large-scale systems. This study aims to provide the foundations for an automated approach for assessing conformance to coupling-related patterns and practices specific for microservice architectures. We propose a model-based assessment based on generic, technology-independent metrics, connected to typical design decisions encountered in microservice architectures. We demonstrate and assess the validity and appropriateness of these metrics by performing an assessment of the conformance of real-world systems to patterns through statistical methods.

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Assessing Architecture Conformance to Coupling-Related Patterns and Practices in Microservices

One of the chief problems in software architecture is avoiding architecture model drift and erosion in all kinds of complex software systems. Microservice-based systems introduce new challenges in this context, as they often use a large variety of technologies in their latest iteration, and are changed and released very frequently. Existing solutions that can be used to reconstruct architecture models fall short in addressing these new challenges, as they cannot easily cope with continuous evolution, their accuracy is too low, and highly polyglot settings are not supported well. In this work, we report on a research study aiming to design a highly accurate architecture model abstraction approach for comprehending component architecture models of highly polyglot systems that can cope with continuous evolution. After analyzing the results of related studies, we found two possible architecture model abstraction approaches that meet the requirements of our study: an opportunistic, and a reusable semi-automatic detector-based approach. We have conducted an empirical case study for validation and comparison of the two approaches. We conclude that both detector approaches are feasible. In our case study, the reusable approach breaks even in terms of time and effort needed for establishing reuse, if modest reuse of detectors is possible, and is producing slightly more high quality and evolution-stable solutions than the opportunistic approach.

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Sebastian Meixner

Papers

Process traceability in distributed manufacturing using blockchains

Supporting Architectural Decision Making on Data Management in Microservice Architectures

Microservice Patterns for the Life Cycle of Industrial Edge Software

Assessing Architecture Conformance to Coupling-Related Patterns and Practices in Microservices

Detector-based component model abstraction for microservice-based systems