arXiv subjects

Nikolaj Marchenko

Publications and source records attributed to Nikolaj Marchenko.

5 recordsLinked to original sources

arXiv2025

Cloud-Fog Automation: The New Paradigm towards Autonomous Industrial Cyber-Physical Systems

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.

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arXiv2023

Convergence of Manufacturing and Networking in Future Factories

The roll out of 5G has been mainly characterized by its distinct support for vertical industries, especially manufacturing. Leveraging synergies among these two worlds, namely production facilities and network systems, is a fundamental aspect to enable flexibility and economic viability in future factories. This work highlights the potential for intelligent networking and advanced machine learning-based solutions in 5G-and-beyond systems in the context of Industry 4.0 and flexible manufacturing. The intersection thereof allows to create versatile machines and dynamic communication networks that can adapt to changes in the manufacturing process, factory layout and communication environment, supporting real-time interaction between humans, machines, and systems. We present a vision and corresponding framework by introducing the network-aware and production-aware principles, outlining results achieved in this context and summarizing them into three key use cases. Finally, we discuss a selection of remaining open challenges in private networks as well as give an outlook on future 6G research directions.

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arXiv2022

HARU: Haptic Augmented Reality-Assisted User-Centric Industrial Network Planning

To support Industry 4.0 applications with haptics and human-machine interaction, 6G requires a new framework that is fully autonomous, visual, and interactive. In this paper, we provide an end-to-end solution, HARU, for private network planning services, especially industrial networks. The solution consists of the following functions: collecting visual and sensory data from the user device, reconstructing 3D radio propagation environment and conducting network planning on a server, and visualizing network performance with AR on the user device with enabled haptic feedback. The functions are empowered by three key technical components: 1) vision- and sensor fusion-based 3D environment reconstruction, 2) ray tracing-based radio map generation and network planning, and 3) AR-assisted network visualization enabled by real-time camera relocalization. We conducted the proof-of-concept in a Bosch plant in Germany and showed good network coverage of the optimized antenna location, as well as high accuracy in both environment reconstruction and camera relocalization. We also achieved real-time AR-supported network monitoring with an end-to-end latency of about $32$ ms per frame.

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arXiv2021

Age of Loop for Wireless Networked Control Systems Optimization

Joint design of control and communication in Wireless Networked Control Systems (WNCS) is a promising approach for future wireless industrial applications. In this context, Age of Information (AoI) has been increasingly utilized as a metric that is more representative than latency in the context of systems with a sense-compute-actuate cycle. Nevertheless, AoI is commonly defined for a single communication direction, Downlink or Uplink, which does not capture the closed-loop dynamics. In this paper, we extend the concept of AoI by defining a new metric, Age of Loop (AoL), relevant for WNCS closed-loop systems. The AoL is defined as the time elapsed since the piece of information causing the latest action or state (depending on the selected time origin) was generated. We then use the proposed metric to learn the WNCS latency and freshness bounds and we apply such learning methodology to minimize the long term WNCS cost with the least amount of bandwidth. We show that, using the AoL, we can learn the control system requirement and use this information to optimize network resources.

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arXiv2021

Leveraging Machine Learning for Industrial Wireless Communications

Two main trends characterize today's communication landscape and are finding their way into industrial facilities: the rollout of 5G with its distinct support for vertical industries and the increasing success of machine learning (ML). The combination of those two technologies open the doors to many exciting industrial applications and its impact is expected to rapidly increase in the coming years, given the abundant data growth and the availability of powerful edge computers in production facilities. Unlike most previous work that has considered the application of 5G and ML in industrial environment separately, this paper highlights the potential and synergies that result from combining them. The overall vision presented here generates from the KICK project, a collaboration of several partners from the manufacturing and communication industry as well as research institutes. This unprecedented blend of 5G and ML expertise creates a unique perspective on ML-supported industrial communications and their role in facilitating industrial automation. The paper identifies key open industrial challenges that are grouped into four use cases: wireless connectivity and edge-cloud integration, flexibility in network reconfiguration, dynamicity of heterogeneous network services, and mobility of robots and vehicles. Moreover, the paper provides insights into the advantages of ML-based industrial communications and discusses current challenges of data acquisition in real systems.

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