Mihai Penica

The transition toward fully wireless industrial automation under Industry 4.0 requires communication systems capable of deterministic and ultra-reliable performance. This study experimentally evaluates the potential of multi-connectivity, specifically the simultaneous use of private 5G, public 5G/LTE, and Wi-Fi in order to meet the 99.9% reliability target defined in 3GPP TS 22.104 for mission-critical applications. Using a mobile robotic platform within the Confirm Smart Manufacturing Centre, four experimental scenarios were conducted under stationary and mobile conditions with 64 byte and 25000 byte payloads. Results show that no individual interface achieved the 99.9% reliability threshold independently, however composite multi-path configurations employing packet duplication across interfaces consistently met or exceeded this benchmark, achieving up to 100% reliability. Dual private 5G + Wi-Fi and triple-interface configurations eliminated single-path deficiencies, while private 5G demonstrated superior stability under mobility due to its single cell architecture. Although packet duplication increases overall network load, it ensures mission-critical communication with a minimum reliability of 99.98%, fulfilling deterministic performance requirements for industrial control and robotic systems. These findings confirm that multi-connectivity offers a practical and robust approach to achieving ultra-reliable wireless communication in industrial environments.

This paper proposes a new Unified Namespace (UNS)-based architecture to improve predictive and prescriptive maintenance of industrial equipment and overcome challenges such as incomplete data, poor interoperability, and disconnected IT/OT environments. The framework combines interoperable data formats in real-time sensor data, predictive modeling, prescriptive analytics, and simulations of digital twins, using UNS as a centralized, protocol-agnostic data layer that is scalable and complies with Industry 4.0 and Pharma 4.0 standards. The suggested methodology increases data accessibility, reduces integration complexity, and allows low-latency analytics and automated decision-making. Machine learning predictive models achieved more than 94% accuracy in predicting equipment failures. Prescriptive analytics provides maintenance recommendations to reduce downtime and risks. The feedback loops of digital twins can enhance the accuracy of predictions and allow decision optimization through what-if analysis. A test-bench deployment showed a higher performance compared to traditional point-to-point integration, with lower latency (approximately 18 ms vs. approximately 31 ms), decreasing packet loss (0.40% vs. 3.11%), and higher model accuracy (94.20% vs. 87.51%). The structure avoided more than 4000 simulated breakdowns in the test-bench environment, indicating dependability. The study connects the theoretical applications of the UNS with the actual maintenance processes and provides a sound approach to the industrial analytics and optimization of the equipment.

Wireless connectivity plays a pivotal role in enabling real-time telemetry, sensor feedback, and autonomous navigation within Industry 4.0 environments. This paper presents a ROS 2-based mobile robotic platform designed to perform real-time network diagnostics across both private 5G and Wi-Fi technologies in a live smart manufacturing testbed. The system integrates high-frequency telemetry acquisition with spatial localization, multi-protocol connection analysis, and detailed performance monitoring. Metrics such as latency, packet loss, bandwidth, and IIoT (Industrial Internet of Things) data stream health are continuously logged and analysed. Telemetry is captured during motion and synchronously stored in an InfluxDB time-series database, enabling live visualization through Grafana dashboards. A key feature of the platform is its dual-path transmission architecture, which provides communication redundancy and allows side-by-side evaluation of network behaviour under identical physical conditions. Experimental trials demonstrate the platform’s ability to detect roaming events, characterize packet loss, and reveal latency differences between Wi-Fi and 5G networks. Results show that Wi-Fi suffered from roaming-induced instability and packet loss, whereas 5G maintained stable and uninterrupted connectivity throughout the test area. This work introduces a modular, extensible framework for mobile network evaluation in industrial settings and provides practical insights for infrastructure tuning, protocol selection, and wireless fault detection.

The convergence of the Internet of Things (IoT) and the construction industry represents a potent opportunity to fundamentally redefine the processes of constructing and maintaining buildings. The integration of IoT technology has the potential to revolutionize building safety, efficiency, and sustainability, offering a promising avenue for innovation and advancement within the industry. In this work, we propose an innovative design and implementation of an interoperable physical access control IoT-based system that can be easily integrated with existing access control security systems and used in various environments such as offices, homes, and public buildings. Based on our developed and implemented physical access control IoT-based system, we identified several challenges in the current state of access control systems. These challenges include difficulties in integrating different security systems, the absence of interoperable standards, and the need for further research to enhance the scalability and performance of such systems.

As Industry 4.0 networks continue to evolve at a rapid pace, they are becoming increasingly complex and distributed. These networks incorporate a range of technologies that are integrated into smart manufacturing systems, requiring adaptability, security, and resilience. However, managing the complexity of Industry 4.0 networks presents significant challenges, particularly in terms of security and the integration of diverse technologies into a functioning and efficient infrastructure. To address these challenges, emerging digital twin standards are enabling the connection of various systems by linking individual digital twins, creating a system of systems. The objective is to develop a “universal translator” that can interpret inputs from both the real and digital worlds, merging them into a seamless cyber-physical reality. It will be demonstrated how the myriad of technologies and systems in Industry 4.0 networks can be connected through the use of digital twins to create a seamless “system of systems”. This will improve interoperability, resilience, and security in smart manufacturing systems. The paper will also outline the potential benefits and limitations of digital twins in addressing the challenges of Industry 4.0 networks.

The recent increase in computational capability has led to an unprecedented increase in the range of new applications where machine learning can be used in real time. Notwithstanding the range of use cases where automation is now feasible, humans are likely to retain a critical role in the operation and certification of manufacturing systems for the foreseeable future. This paper presents a use case review of how human operators affect the performance of cyber–physical systems within a ’smart’ or ’cognitive’ setting. Such applications are classified using Industry 4.0 (I4.0) or 5.0 (I5.0) terminology. The authors argue that, as there is often no general agreement as to when a specific use case moves from being an I4.0 to an I5.0 example, the use of a hybrid Industry X.0 notation at the intersection between I4.0 and I5.0 is warranted. Through a structured review of the literature, the focus is on how secure human-mediated autonomous production can be performed most effectively to augment and optimise machine operation.