The increasing complexity and dynamic nature of Industrial Internet of Things (IIoT) demand scalable, adaptive, intuitive, and real-time automation frameworks. This paper presents a systematic literature review (SLR) of edge- and cloud-based collaborative learning frameworks for predictive maintenance and smart manufacturing tasks. In this SLR, we highlight the under-utilization of distributed computational architectures that provide complete automation support (design and run-time), flexibility, scalability, and inter- & intra-cloud service exchange while adhering to security management and integrity principles for solving IIoT tasks using modern artificial intelligence (AI) models at the edge/cloud. Recently, many IIoT applications have been designed using AI models that require robust, low-latency, and data-secure frameworks. This demand drives a trend toward distributed computational architectures in which data storage and processing are partially or fully decentralized. Common paradigms addressing this resource distribution include edge computing, federated learning, and private or hybrid clouds. We analyze 50 recent studies against IoT characteristics, AI performance metrics, and network/system management requirements. Our findings reveal underutilization of distributed architectures that support automation, interoperability, and security. While most solutions rely on centralized or hybrid clouds, fewer than 5% adopt federated or transfer learning, and over 60% remain dependent on supervised models. We also introduce a comparative perspective on network and security management, showing that local/private cloud implementations can reduce control-plane overhead and synchronization latency, though gaps persist in dynamic bandwidth allocation and zero-trust adoption. Finally, we benchmark our previously proposed local cloud-based collaborative learning (CCL) model against state-of-the-art solutions, highlighting its strengths in automation and interoperability, as well as limitations in adaptive computation and intelligent offloading. This review identifies the research gaps and opportunities for integrating collaborative AI, secure automation, and hybrid architectures to meet Industry 5.0 objectives of resilience, sustainability, and human-centricity.

Management for Industry 4.0/5.0 will require considerable engineering effort. To reduce such efforts by cost and time is a considerable challenges. This paper highlight that the usage of Service Oriented Architecture and microservice architecture technologies can create substantial saving on engineering for production automation. The impact on engineering cost and time has been investigated by the Arrowhead Tools project. For the purpose 28 industrial use cases were defined. For each of the 28 use cases a baseline for the engineering cost and time was defined, using for each industrial partner the available technology and competence. Based on use case requirements, a dedicated edge microservice architecture was defined and implemented using the Eclipse Arrowhead architecture and reference implementation, complemented by use case-specific edge microservices and tool chain interoperability. The engineering cost and time for implementing the microservice architecture for the use cases were calculated. Costs, as time savings were then determined for each use case. The resulting median cost and time savings were just above 80%, with a range from 30% to 95%.

The engineering of complex, service-oriented systems of systems (SoS) demands reconciling the adaptability of distributed architectures with the rigor of traceable, modelbased design. In this paper, we introduce the concept of digital tweads-a convergence of digital threads and digital twins-as a foundation for holistic engineering governance. We present GO-VerN, a three-pillar co-modeling framework that integrates SysML v2-based system design, OWL-based representations from the Industrial Data Ontology (IDO), and the runtime semantics of Eclipse Arrowhead local clouds. This approach enables multimodal traceability across design, deployment, and semantic domains. Through an illustrative thermal control scenario, we demonstrate how SysML v2 and IDO can jointly support tracedriven validation, digital twin synchronization, and semantic reasoning. Our work lays the foundation for future lifecycle management practices that are both dynamic and verifiable-supporting engineering that is simultaneously modular, interpretable, and automation-ready.

The rapid evolution of manufacturing processes driven by Industry 4.0 demands systems capable of quickly adapting to dynamic market conditions and evolving customer needs. Agile manufacturing emphasizes flexibility, adaptability, and real-time responsiveness, posing challenges in run-time value chain analysis (VCA), including cost flows and production times. This article presents a novel two-stage VCA approach using an activity-based costing mechanism via microservices to address these challenges. The VCA system enables real-time cost accounting and decision-making, supporting both pre and postproduction VCA, contrasting with traditional methods that rely on historical data. The first stage involves top-down cost calculations from resources to microservices. In contrast, the second focuses on constructing efficient manufacturing activities based on product requirements, allowing for granular analysis of costs and production times across microservices, activities, broader business processes, and finally, cost objects (e.g., customized products, batches of products, or customer invoices). The approach is validated through a proof-of-concept implementation of the VCA system integrated with the Eclipse Arrowhead framework and simulating Fischertechnik indexed line milling, drilling, and conveying operations. The results demonstrate the effectiveness of the proposed method in providing detailed insights into costs and production times, enhancing the efficiency and competitiveness of agile manufacturers.

Cyber-Physical Systems (CPS) powered by Artificial Intelligence (AI) have the potential to revolutionize industries by enabling advanced analytics and autonomous decision-making. To support resource-intensive applications, there is often a need to dynamically allocate additional compute resources. The Edge-Cloud Continuum enables allocation and deployment of workloads across platforms, including IoT devices, edge clusters, and cloud environments. However, the growing computational demands of these systems can unfortunately result in increased energy consumption and higher carbon emissions. This paper investigates the development of a carbon-aware scheduler for the Edge-Cloud Continuum, designed to optimize workload placement by balancing energy consumption, temporal variations in carbon intensity and resource availability. Key contributions of the paper include a spatiotemporal scheduling algorithm, a discrete-event simulator capable of replaying realistic workloads from the MIT SuperCloud dataset, and a comprehensive empirical evaluation. Findings from the paper show substantial reductions in carbon emissions by prioritizing renewable energy sources and time-shifting workloads to periods of lower carbon intensity. However, when clusters operate under high utilization, time-shifting can inadvertently result in significantly higher emissions. In such scenarios, simpler greedy algorithms can be more effective.

Increasing demands for low latency, cost efficiency, and digital sovereignty are driving organisations beyond centralised cloud models towards decentralised and hybrid computing environments. The Edge-to-Cloud computing continuum aims to unify edge and cloud infrastructures into a seamless execution environment, but achieving this remains complex in practice.ColonyOS is an open-source, ready-to-use meta-operating system that coordinates distributed workloads across diverse computing environments. ColonyOS separates coordination from execution using declarative, intent-based function specifications and a lightweight, broker-based architecture that provides OS-like abstractions. This paper outlines key properties of a meta-operating system and describes how these are realised in ColonyOS. The paper also presents practical experiences from two industrial deployments: real-time seismic monitoring at RockSigma AB and integration with EuroHPC supercomputers. These use cases demonstrate ColonyOS’s capabilities in enabling resilient workload management and hybrid orchestration across HPC and Kubernetes environments. A referenced study on carbon-aware scheduling further illustrates how ColonyOS supports time-shifting of non-urgent workloads to reduce environmental impact. The paper concludes with a discussion of ColonyOS’s architecture and future research directions.

Industry 4.0 has revolutionized industrial automation, with models, such as Industry 4.0 Reference Architectural Model (RAMI 4.0), providing a structured framework for optimizing value chains and processes. However, the complexity and abstract nature of RAMI 4.0 have limited its practical application, especially due to the lack of clear visualization methods to understand industrial ecosystems. Effective visualization is essential to translate this framework into actionable insights, enabling stakeholders to grasp system interactions, dependencies, and value-creation processes. This article proposes a multidimensional visualization approach, illustrated through a smart heat pump example, to map information and operational technologies, their interactions, and value chains. Combining 3-D visualizations for integrated system overviews with 2-D visualizations for task-specific analysis, the approach provides a comprehensive understanding of RAMI 4.0 value chains, enabling stakeholders to address their analytical needs with clarity. It facilitates run-time value chain analysis, offering real-time insights for decision-making during operations. The approach maps industrial systems across RAMI 4.0 axes and aligns them with engineering processes and lifecycle phases, enabling the exploration of system interactions, dependencies, and stakeholder contributions. This supports the analysis of engineering and business processes, optimizes infrastructure, and facilitates smooth technological transitions. It enhances RAMI 4.0’s utility for real-time decision-making and operational efficiency, boosting competitiveness in industrial ecosystems.

The industrial landscape is swiftly progressing toward Industry 5.0, marking the fifth revolution characterized by the integration of sustainable practices and digital sovereignty. This article advocates for the adoption, expansion, and implementation of artificial intelligence (AI)-enabled hardware, tools, methods, and semiconductor technologies in the journey toward Industry 5.0. Beyond the initial proposal, the article explores primary research areas and the diverse challenges inherent in this transition. Notably, significant accomplishments in pivotal industrial use cases are appended, providing validation evidence. This comprehensive approach aims to bridge academic advancements with practical industrial application, fostering a symbiotic relationship between humans and machines for increased efficiency, innovation, and adaptability.

The development of miniaturized embedded inductors holds crucial significance in advancing modern electronic devices, contributing to their size reduction, enhanced efficiency, and improved performance. This study introduces a groundbreaking, fully additive manufacturing process designed for fabricating miniaturized embedded double-stacked copper spiral inductors. The Sequential Build Up-Covalent Bonded Metallization (SBU-CBM) method serves as the foundation for this novel approach. The experimental process evolves in three stages. In Stage I, the first layer of polyurethane (PU1) is initially spin-coated onto the FR-4 base substrate. The lower embedded copper spiral inductor is then fabricated on top of PU1, employing the SBU-CBM method. Moving to Stage II, the second layer of polyurethane (PU2) is spin-coated onto the existing PU1 layer. Subsequently, a single microvia is created and copper-plated using the SBU-CBM method, establishing a crucial vertical connection between the upper and lower embedded copper spiral inductors. Finally, Stage III involves the fabrication of the upper embedded copper spiral inductor on PU2, utilizing the SBU-CBM method. Optical microscopy and X-ray Computed Tomography (XCT) images confirm the successful formation of embedded double-stacked copper spiral inductors, a configuration where two embedded copper spiral inductors are interconnected through a copper microvia. Notably, the copper strip lines within the spiral inductor configuration are miniaturized to a width of 10 μm, while the diameter of the microvias is reduced to 10 μm, indicating the miniaturization precision achieved through this novel additive manufacturing process.

A digital twin (DT), the digital counterpart of a physical entity, process, or system, is a pivotal innovation driving the manufacturing industry’s digital transformation. DT plays a significant role in product lifecycle management (PLM) and product condition monitoring. However, the diversity of systems and processes involved poses challenges in DT and data management within PLM, particularly regarding efficiency, standardized data mapping, and latency. The paper presents a solution architecture to address these challenges and contribute towards an efficient and cost-effective product lifecycle management system. The architecture focuses on DT’s data management and communication aspects, utilizing the edge-based, decentralized Eclipse Arrowhead Framework and EDMtruePLM (Enterprise Data Management True Product Lifecycle Management) for standardized data management and condition monitoring of products. Integrating the ISO 10303 STEP standard for data modeling and the Open Platform Communications Unified Architecture (OPC UA) standard for communication is emphasized, improving the contextual significance of the data and the system’s interoperability. A use case implementation is presented, where a fischertechnik assembly line is monitored, capturing sensor data through the PLC’s OPC UA server. The sensor data is then aligned with the STEP standard and stored in the EDMTruePLM database for monitoring.

Interoperability is a central problem in digitization and System of Systems (SoS) engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cyber-physical systems (CPSs) at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as SoS, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish SoSs with purposeful CPS communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with artificial intelligence (AI)-enabled solutions in relation to SoS interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex SoSs, and realistic testbeds are needed.

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

The semiconductor industry demands high input/output (I/O) density, requiring sub-l0-micrometer microvia. Here we propose a novel, fully additive, economical approach for creating and copper plating of microvias. The experimental process consisted of three stages. In Stage I, a polyurethane layer was spin-coated onto a FR-4 PCB base, followed by target copper layer deposition using the sequential build-up-covalent bonded metallization (SBU -CBM) method. In Stage II, first another layer of polyurethane was spin-coated on the top of the target copper layer, and then a microvia was created on the polyurethane layer using a picosecond pulsed ultraviolet (UV) laser. Finally, in Stage III, the SBU-CBM method was used to selectively copper plating of the microvia. Optical microscopy and cross-section scanning electron microscopy (SEM) images confirmed the successful formation and copper plating of sub-l0 micrometer microvia.

In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.

In an era ruled by data and information, engineers need new tools to cope with the increased complexity of industrial operations. New architectural models for industry enable open communication environments, where workflows can play a major role in providing flexible and dynamic interactions between systems. Workflows help engineers maintain precise control over their factory equipment and Information Technology (IT) services, from the initial design stages to plant operations. The current application of workflows departs from the classic business workflows that focus on office automation systems in favor of a manufacturing-oriented approach that involves direct interaction with cyber-physical systems (CPSs) on the shop floor. This paper identifies relevant industry-related challenges that hinder the adoption of workflow technology, which are classified within the context of a cohesive workflow lifecycle. The classification compares the various workflow management solutions and systems used to monitor and execute workflows. These solutions have been developed alongside the Eclipse Arrowhead framework, which provides a common infrastructure for designing systems according to the microservice architectural principles. This paper investigates and compares various solutions for workflow management and execution in light of the associated industrial requirements. Further, it compares various microservice-based approaches and their implementation. The objective is to support industrial stakeholders in their decision-making with regard to choosing among workflow management solutions.

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired "neuromorphic" processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital-computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

Manufacturing systems are in the middle of a digital transformation. As systems in the assembly line are upgraded into cyber-physical systems (CPSs), capable of communicating between each other and carrying out complex computational tasks, the need for tight centralized control from an enterprise resource planning (ERP) and manufacturing execution system (MES) is less vital. In fact, not only manufacturing processes follow the trend toward decentralization and are moved to the edge layer. Other business processes along the supply chain have the potential to follow the digitalization process, such as procurement and supply flow management. This evolution brings new opportunities and challenges to the field. On the opportunity side, we identify shorter cycle times from product design to production, flexible production systems and multi-stakeholder production. Among the associated challenges, the collaboration of product, production, and business aware edge assets in multi-stakeholder environments stands out. This work proposes a new architecture for smart factories, in an environment where the products, supply stations and manufacturing equipment are controlled by different stakeholders. Requested manufacturing operations and supply flow are generated from machine-to-machine (M2M) negotiated business agreements between pairs of involved stakeholders. The manufacturing workflows are created and managed at each production workstation based on the smart product's needs. Operations and supply flow progress is logged in distributed ledgers for the involved pairs of stakeholders, providing non-repudiation and immutable data on the M2M business agreement. The proposed architecture enables the automation of business processes providing benefits in terms of decreased transaction time and cost.

Modeling Industrial Internet of Things (IIoT) architectures for the automation of wind turbines and farms (WT/F), as well as their condition monitoring (CM) is a growing concept among researchers. Several end-to-end automated cloud-based solutions that digitize CM operations intelligently to reduce manual efforts and costs are being developed. However, establishing robust and secure communication across WT/F is still difficult for the wind energy industry. We propose a fully automated cloud-based collaborative learning (CCL) architecture using the Eclipse Arrowhead Framework and an unsupervised dictionary learning (USDL) CM approach. The scalability of the framework enabled digitization and collaboration across the WT/Fs. Collaborative learning is a novel approach that allows all WT/Fs to learn from each other in real-time. Each turbine has CCL based CM using USDL as micro-services that autonomously perform feature selection and failure prediction to optimize cost, computation, and resources. The fundamental essence of the USDL approach is to enhance the WT/F's learning and accuracy. We use dictionary distances as a metric for analyzing the CM of WT in our proposed USDL approach. A dictionary indicates an anomaly if its distances increased from the dictionary computed at a healthy state of that WT. Using CCL, a WT/F learns all types of failures that could occur in a similar WT/F, predicts any machinery failure, and sends alerts to the technicians to ensure guaranteed proactive maintenance. The results of our research support the notion that when testing a turbine with dictionaries of all the other turbines, every dictionary converges to similar behavior and captures the fault that occurs in that turbine.