Integrating smart manufacturing ecosystems with industrial-grade smart energy and building automation systems enables real-time adaptation to changes in demands and factory conditions, the supply chain, and the needs of customers and society. However, integrating, managing, and controlling data exchange usually incurs high overheads in such a collaborative industrial environment. Smart home IoT technologies are a cost-effective solution for smart energy and building automation systems; they are not fully interoperable with industrial IoT technologies. This paper presents a mechanism to solve this interoperability problem using the Eclipse Arrowhead framework. The proposed solution provides a microservice-oriented architecture to develop protocol-specific smart adapter systems for the Arrowhead framework. These smart adapter systems provide seamless and highly scalable integrations between smart home and industrial IoT technologies. Our solution enables smart manufacturing ecosystems to meet Industry 5.0’s core values and reduce their carbon footprint to save the planet. We present the performance of our solution using an example from a real-world use case of a smart heating system scenario in a smart factory.

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 USDA 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.

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.

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.

Industry 4.0 has revolutionized industrial automation, with models like 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 paper proposes a multidimensional visualization approach, illustrated through a smart heat pump example, to map information and operational technologies, their interactions, and value chains. Combining 3D visualizations for integrated system overviews with 2D 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 rapid transformation of the manufacturing industry under Industry 4.0 demands systems that can quickly adapt to dynamic market conditions and 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 paper 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 post-production VCA, contrasting with traditional methods that rely on historical data. The first stage involves top-down cost calculations from resources to microservices, while the second focuses on constructing efficient manufacturing activities based on product requirements, allowing for a 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.