Dennis Krummacker

Siyu Yuan, Bin Han, Dennis Krummacker et al.

As a complement to conventional AI solutions, emergent intelligence (EI) exhibits competitiveness in 6G IIoT scenario for its various outstanding features including robustness, protection to privacy, and scalability. However, despite the low computational complexity, EI is challenged by its high demand of data traffic in massive deployment. We propose to leverage massive twinning, which 6G is envisaged to support, to reduce the data traffic in EI and therewith enhance its performance.

Bin Han, Dennis Krummacker, Qiuheng Zhou et al.

Enabled by the emerging industrial agent (IA) technology, swarm intelligence (SI) is envisaged to play an important role in future industrial Internet of Things (IIoT) that is shaped by Sixth Generation (6G) mobile communications and digital twin (DT). However, its fragility against data injection attack may halt it from practical deployment. In this paper we propose an efficient trust approach to address this security concern for SI.

Simon Duque Anton, Daniel Fraunholz, Dennis Krummacker et al.

Advances in industrial control lead to increasing incorporation of intercommunication technologies and embedded devices into the production environment. In addition to that, the rising complexity of automation tasks creates demand for extensive solutions. Standardised protocols and commercial off the shelf devices aid in providing these solutions. Still, setting up industrial communication networks is a tedious and high effort task. This justifies the need for simulation environments in the industrial context, as they provide cost-, resource- and time-efficient evaluation of solution approaches. In this work, industrial use cases are identified and the according requirements are derived. Furthermore, available simulation and emulation tools are analysed. They are mapped onto the requirements of industrial applications, so that an expressive assignment of solutions to application domains is given.

Anthony Kiggundu, Dennis Krummacker, Hans D. Schotten

To improve business efficiency and minimize costs, Artificial Intelligence (AI) practitioners have adopted a shift from formulating models from scratch towards sharing pretrained models. The pretrained models are then aggregated into a global model with higher generalization capabilities, which is afterwards distributed to the client devices. This approach is known as federated learning and inherently utilizes different techniques to select the candidate client models averaged to obtain the global model. This approach, in the case of communication systems, faces challenges arising from the existential diversity in device profiles. The multiplicity in profiles motivates our conceptual assessment of a metaheuristic algorithm (FedAvgen), which relates each pretrained model with its weight space as metadata, to a phenotype and genotype, respectively. This parent-child genetic evolution characterizes the global averaging step in federated learning. We then compare the results of our approach to two widely adopted baseline federated learning algorithms like Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD).