In recent years, companies have increasingly invested in Industry 4.0 research to automate repetitive tasks or activities that may be harmful to humans. For instance, large-scale warehouses, such as those operated by Amazon, rely heavily on mobile robots to streamline logistics operations and ensure worker safety. At the same time, robot manufacturers are releasing more reliable platforms with advanced capabilities, making them suitable for complex industrial tasks. Despite this growing interest and technological progress, significant challenges remain. A key and non-trivial issue lies in the integration of heterogeneous robot fleets: each vendor typically employs proprietary technologies and interfaces, which hinders interoperability and limits the potential of multi-brand deployments.In this work, we introduce HarmoniKt, an extensible middle-ware that addresses this challenge by introducing an abstraction layer and providing a unified REST API for the control and management of heterogeneous robots. Our solution has been validated in a physical industrial-like environment using a mixed fleet of Boston Dynamics Spot and Mobile Industrial Robots (MiR). Furthermore, we present a comparative analysis showing that the access latency introduced by our middleware is not significantly higher than that of direct robot access, demonstrating the feasibility of unified robot management without compromising performance.

According to both academic and industry perspectives, the Fourth Industrial Revolution has brought about a paradigm shift in the manufacturing sector enabling companies to enhance their competitiveness in the global market. To achieve this goal, manufacturing companies will need to undertake a deep digital transformation, primarily by introducing advanced Information Technology into traditionally less digitalized departments, such as shop floors, where Operational Technology currently dominate. For the full achievement of Industry 4.0 revolution objectives, practitioners believe in the strong requirement of a progressive and tight integration between IT and OT departments. In the depicted scenario, communication technologies are expected to play a pivotal role in facilitating the integration process, but other more recent and advanced IT have also proven helpful. In particular, the topic of IT/OT integration has attracted significant attention from various research communities that have sought to identify both the opportunities and challenges associated with its implementation. Although some good surveys of those works have appeared in the literature, to the best of our knowledge, no comprehensive review has yet been conducted that is fully dedicated to the topic of IT/OT convergence. In this paper, we propose a holistic approach to examine the various dimensions of IT/OT integration, which we classify into five interconnected realms, Communication, IT-Driven Support to OT, Human Centricity, Advanced Industrial Control Systems, and cybersecurity. Furthermore, we develop a realm-oriented taxonomy to organize the surveyed works in a structured manner, offering readers a clear overview of the current state of the literature, along with insights into unexplored opportunities and future directions for IT/OT integration.

The recent trend pushing towards reliance on edge computing, virtualization and programmatic 5G network control has sparked the development of a myriad of open-source resource management and orchestration projects for improved control and added flexibility, making up for a rich and complex ecosystem of frameworks and tools with varying degree of support for standardized features. In this technological panorama, the ETSI Multi-Access Edge Computing (MEC) standard proposes a conceptual reference architecture, standardizing edge integration, interoperability and application management in an extended 5G edge-core architecture. In this context, we propose an application-aware orchestration solution for 5G edge-core distributed deployments, currently lacking support in state-of-the-art frameworks and tools. The proposal is built on an experimental and distributed deployment of the OpenAirInterface minimal MEC platform implementation and relies on the Kubernetes Operator pattern for the automatic MECApp lifecycle management. To validate our approach, we conduct a series of experiments, reporting key metrics of interest.

The convergence of multi-access edge computing and fog computing in industrial settings offers benefits such as scalability, low latency, availability, and high-level management. In this context, service virtualization plays a pivotal role, and the Kubernetes microservice orchestrator performs the platform role in numerous industrial environments. However, Kubernetes does not provide advanced support for service migration and service data synchronization. The management of application migration, whether stateless or stateful, between cluster nodes is crucial, and it is not currently addressed. To fill this gap, we propose stateful service migration support entirely managed within Kubernetes. The approach we present in this article enhances platform awareness by integrating a proactive preparation mechanism for migration. This mechanism manages information and makes it available for applications, dynamically applying specific policies. We tested the solution in a challenging real industrial environment, stressing the mechanism with demands such as availability requests, network congestion, and a high volume of data to migrate. The experimental results confirm the effectiveness of the proposed solution in terms of reliability, good uptime, and easy applicability to Industry 4.0 stateful services.

The advent of Industry 4.0, resulting from the digitalization of industry, has led to a surge in demand for Industrial IoT and Edge Computing, which in turn has prompted major IT players and Foundations to launch their own IIoT frameworks and service solutions. Despite the huge advantages that this service solutions offer, the ready-to-use IoT-Cloud services the big players provide are seen by companies as black boxes, and more than often these services do not fit with the real industrial scenario of the companies or do not fully meet the use case requirements. To address these issues, we propose an integration solution to empower and adapt the services of one of the most used IoT Cloud solutions, AWS IoT. In addition, we show the implementation of our work in a real scenario provided by the company Northvolt. Finally, we demonstrate the goodness and efficiency of the proposed solution by showing the performance and latency tests along with their experimental results.

Recently, the Industrial Internet of Things (IIoT) and Industry 4.0 paradigms have attracted considerable relevance by leading all manufacturing companies to invest in IIoT solutions in order to be competitive on the market. Despite the huge number of IIoT solutions on the market, all platforms refer to a common architectural model and share the same functionalities. Among these functionalities, the most relevant is the capability of deploy and run custom containerized applications. This feature has a pivotal role of customizing the IIoT platform and tailoring it to each business scenario. In this paper, we define the general architectural model and common functionalities of the IIoT platforms. Then, we analyze and compare two of the most used IIoT solutions on the market, Siemens Industrial Edge, and EdgeX Foundry. We also define three metrics for evaluating the functionality of deploying custom services on the edge, and we test these two platforms. Finally, we present their comparative performance, advantages, and limitations.

In the last recent years Internet of Things has extended its adoption to the industrial manufacturing field. The digitalization of industry brings the new concept of Industry 4.0 to light. In fact, the enormous value for the companies generated from the adoption of Industrial IoT and Edge Computing has led to a vertiginous increments of IIoT platform demands. Therefore, world-wide big IT players and Foundations start to introduce their own flagship IIoT platforms into market. This multitude of platforms presents similar common features, but with different APIs. These platforms are hardly interoperable, and they are frequently bounded to their own vertical solution stack. To overcome these limitations, in this paper, we propose MIINT, a Middleware for IIoT platforms INTegration. MIINT groups the common functionalities of IIoT frameworks, and it integrates different platforms by providing standard access APIs. To prove the feasibility of MIINT, in this paper, we show an integration use case of Azure IoT and EdgeX Foundry IIoT platforms. Moreover, we also thoroughly assess MIINT by executing on the edge a field data reading functionality in the two considered IIoT platforms, by showing their advantages and limitations.

Condition Monitoring is one of the most critical applications of the Internet of Things (IoT) within the context of Industry 4.0. Current deployments typically present interoperability and management issues, requiring human intervention along the engineering process of the systems; in addition, the fragmentation of the IoT landscape, and the adoption of poor architectural solutions often make it difficult to integrate third-party devices in a seamless way. In this paper, we tackle these issues by proposing a tool-driven architecture that supports heterogeneous sensor management through well-established interoperability solutions for the IoT domain, i.e. the Eclipse Arrowhead framework and the recent Web of Things (WoT) standard released by the W3C working group. We deploy the architecture in a real Structural Health Monitoring (SHM) scenario, which validates each developed tool and demonstrates the increased automation derived from their combined usage.

Industry 4.0 outlines the trend of the massively adoption of Internet of Things (IoT) nodes in supply chains, manufacturing, and factories in general. The industry digitalization is the key enabler to ease the productive process, drastically reduce its costs, and boost up the associated business. In this context, Arrowhead Tools (AHT) is a H2020 EU project provided by ECSEL that targets automation and digitalization solutions for the industry in Europe. AT is based on a framework, named Arrowhead Framework (AHF), developed and provided by the previous Arrowhead (AH) project. AHF is open source and addresses IoT-based automation and integration by abstracting IoT objects to services. AHF enables IoT interoperability and provides real time data handling, security features, automation system engineering, and automation systems scalability. In this paper, after a rapid overview of the AT project and the AHF architecture, we originally introduce the concept of Tool and Tool Chain for Industry 4.0 in AH. We also present a vertical AHT use case along with its implementation, as well as all the steps to turn a service/application into an AH-compliant Tool.

Consolidation of Internet of Things and Fog computing paradigms requires effective and efficient application-layer protocols between service seekers and providers. As most of these nodes run on batteries, discovering service providing devices in an IoT-Fog environment has to be performed in an energy-efficient way. In this paper, we propose Power Efficient Node Discovery (PEND), which is an MQTT-driven IoT-fog integration solution for the sustainability of object discovery in a publish/subscribe environment. By enabling the MQTT broker to serve as a fog node to trigger turning on/off of the Bluetooth interfaces of subscriber objects, Bluetooth Low Energy Scanner (BLE-S), and to monitor the trajectories of publishers/advertisers, Bluetooth low Energy Advertiser (BLE-A), we introduce significant reduction in the Bluetooth, CPU, and process- specific power consumption of the mobile devices. The reduction in the battery drain by the BLE interface under the proposed scheme can be as low as 10%-20% of a naive, locality based discovery benchmark whereas the process specific battery drain of the proposed scheme can be as low as 55%-62% of the node discovery benchmark. Furthermore, with the synchronization of the publishers and subscribers at the fog layer entity, i.e., MQTT broker, 100% node discovery can be achieved by the scanning/service subscriber devices.