Guido Dartmann

Latest Publications

Topal, O. A., Demir, , Liang, Z., Pusane, A. E., Dartmann, G., Ascheid, G. and Kur, G. K.: A Physical Layer Security Framework for Cognitive Cyber-Physical Systems, in IEEE Wireless Communications, Vol. 27, No. 4, pp. 32-39, 2020, 10.1109/MWC.01.1900543

Machhamer, R., Dziubany, M., Czenkusch, L., Laux, H., Schmeink, A., Gollmer, K.-U., Naumann, S. and Dartmann, G.: Online Offline Learning for Sound-based Indoor Localization Using Low-cost Hardware, in IEEE Access, Vol. 7, pp. 155088-155106, Oct. 2019, ISSN: 2169-3536, 10.1109/ACCESS.2019.2947581 ©2019 IEEE

Peine, A., Hallawa, A., Schöffski, O., Dartmann, G., Begic Fazlic, L., Schmeink, A., Marx, G. and Martin, L.: A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study, in JMIR Med Inform, Vol. 7, No. 4, p. e14806, Oct. 2019, ISSN: 2291-9694, 10.2196/14806

Ayad, A., Zamani, A., Schmeink, A. and Dartmann, G.: Design and Implementation of a Hybrid Anomaly Detection System for IoT, in Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 1-6, IEEE, Oct. 2019, 10.1109/IOTSMS48152.2019.8939206 ©2019 IEEE

Hauck, M., Machhamer, R., Czenkusch, L., Gollmer, K.-U. and Dartmann, G.: Node and Block-based Development Tools for Distributed Systems with AI Applications, in IEEE Access, Sep. 2019, 10.1109/ACCESS.2019.2940113 ©2019 IEEE

Full list

Research Interests

The research group deals with distributed systems, hardware-software co-design, data analysis, signal processing & machine learning and security in communication systems and has a special reference in the application of these technologies in different disciplines (medicine, logistics, mobility, SMEs).

The Internet of Things as the basis for Industry 4.0 is ubiquitous and poses new challenges to science and society. The fields of mechanical engineering, electrical engineering and computer science will therefore grow together more and more in the future. The term cyberphysical systems is therefore often used to illustrate that technology will merge with our environment in the future. In addition to risks such as threats to information security, which are a central topic of the research of the working group, this networking and digitization can also open up great opportunities to meet the global challenges. 

One research area of Distributed Systems is the distributed and efficient implementation of classical concepts like signal processing & machine learning. Earlier signal processing algorithms were optimized for low-dimensional signals and the implementation in DSPs was correspondingly simple. Today's applications generate high-dimensional data sets and require the implementation of more complex data processing systems and algorithms. Processing steps in signal processing and machine learning are now increasingly implemented in complex distributed processing chains. Thus, classical concepts of computer science and information technology need to be further developed for future intelligent cyberphysical systems (CPS). Due to their distributed and decentralized nature, new distributed and efficient optimization methods, algorithms for data analysis and knowledge extraction, and hardware platforms need to be explored for CPS. 

In the following, the three central research areas of the working group are described as examples:


Research area 1 - Platforms for digital services: 

In the current wave of digitalization, the physical world is connecting with the Internet and a variety of intelligent services (smart services) are emerging. In the application areas of logistics, industry and health, the research group is exploring platform concepts based on new technologies such as smart contracts. Through this, micro contracts can be implemented in versatile application areas, allowing diverse new business ideas to be developed. The development of distributed software systems for smart services is a central aspect of the working group. Exemplarily, these systems are implemented with a logistics service in an autonomous demonstrator. To accelerate this development, the WG is also researching associated development tools.  

Associated third-party funded projects: 

  • LandLeuchten (BMVI-funded, ongoing).
  • KI-Pilot (BMEL-funded, ongoing)
  • SAVE (BMWi-funded, ongoing)
  • APEROL (BMVI-funded, completed)


Research Field 2 - Data Analysis and Signal Processing 

In this research field, we distinguish between algorithms for real-time capable systems, algorithms for Big Data applications, and model-based learning. The research of the group is motivated by the application. Elementary is a good modeling of the application. The research approaches of the research group therefore deal with the fusion of data-based and model-based approaches, because model-based approaches allow the integration of expert knowledge and manage with smaller amounts of data. 

In addition to modeling, the working group is investigating algorithms and methods that can be implemented in the IoT end node. Algorithms that have low complexity and low energy consumption are suitable for this purpose. One focus of the investigated methods is the analysis of multimodal sensor data streams in distributed systems.

Systems of signal processing and data analysis in Distributed Systems need to efficiently use the available resources of communication channels in addition to efficient use of energy. Massive IoT systems produce enormous amounts of data when the data is fully analyzed in a central cloud. That's why the research group here is exploring distributed learning techniques, researching how much knowledge can be learned locally and distributed algorithms that can in turn efficiently learn global knowledge from local knowledge. 

Related third-party funded projects 

  • KI-Pilot (BMEL-funded, ongoing)
  • PINOT (BMEL-funded, ongoing)
  • COSY (BMBF-funded, completed)
  • IoT-Pilot (BMEL-funded, completed)
  • IMEDALytics (BMBF-funded, completed)
  • Claire (EU-funded, completed)

Research Field 3 - Technology for Distributed Systems: 

Low-cost hardware: Together with the IoT expert group of the national Digital Summit, we have developed a generic IoT device. This device is characterized by low cost and flexibility. An important aspect of this Device is the development of a generic hardware interface to diverse components in different applications. Another aspect is the connection of additional sensors. Here, radio-based sensors are in focus due to their flexibility. Currently, the research group is researching the implementation of an Edge-AI Device to implement local learning and local data analysis methods.

Hardware-Software Interfaces:  In addition to hardware, a semantic software interface is another important aspect of this effort. Due to the diversity of different connected components, aspects of the respective hardware have to be abstracted. Self-learning software can learn new components and automatically generate test code for the adaptation of the interfaces in the future. These tools from the BMEL project IoT-Pilot should have these skills to some extent.

Implementation in high-performance hardware: One challenge in the automotive sector is the development of machine learning methods for autonomous driving. The range of these methods is wide, and there are now powerful hardware platforms that can implement such machine learning methods in real time. To achieve real-time capability, optimization of these algorithms is elementary. 

IT security: Without ensuring security, cyber-physical systems will never be accepted in industry. A future cyber-physical system must be able to independently detect possible attacks using, for example, data analytics methods. Radio-based systems have become increasingly interesting for industrial applications due to their flexibility. In traffic (autonomous driving), moreover, communication is only possible via radio. Many such systems are embedded and do not have complex encryption mechanisms. Due to the distributed nature of the radio medium, it is an easy target for attackers. Therefore, I have been researching concepts to achieve IT security already in layer 1 and 2 for several years. 

Related third-party funded projects:

  • AI-Pilot (BMEL-funded, ongoing)
  • IoT-Pilot (BMEL-funded, completed)
  • COSY (BMBF-funded, completed)

More details of my research group at University of Applied Sciences Trier.