News and Announcements

  • New IEEE Transactions on Molecular, Biological and Multi-Scale Communications article

    August 23, 2025

    Our article Machine Learning-Driven Localization of Infection Sources in the Human Cardiovascular System has been accepted for publication in IEEE Transactions on Molecular, Biological and Multi-Scale Communications. In vivo localization of infection sources is essential for effective diagnosis and targeted disease treatment. In this work, we leverage machine learning models to associate the temporal dynamics of biomarkers detected at static gateway positions with different infection source locations. In particular, we introduce a simulation that models infection sources, the release of biomarkers, and their decay as they flow through the bloodstream. From this, we extract time-series biomarker data with varying decay rates to capture temporal patterns from different infection sources at specific gateway positions. We then train a stacked ensemble model using LightGBM and BernoulliNB to analyze biomarker time-series data for classification. Our results reveal that higher biomarker degradation rates significantly reduce the localization accuracy by limiting the biomarker signal detected at the gateways. A fivefold increase in decay rate lowers the mean cross-validation accuracy from ∼92% to ∼66%.
    (link to more information)

  • New IEEE Transactions on Molecular, Biological and Multi-Scale Communications article

    August 17, 2025

    Our article Blood Makes a Difference: Experimental Evaluation of Molecular Communication in Different Fluids has been accepted for publication in IEEE Transactions on Molecular, Biological and Multi-Scale Communications. The experimental appraisal of existing molecular communication (MC) testbeds and modeling frameworks in real blood is an important step for future internet of bio-nano-things applications. In this paper, we experimentally compare the MC flow characteristics of water, blood substitute, and real porcine blood for a previously presented superparamagnetic iron oxide nanoparticles (SPION) MC testbed. We perform an extensive analysis of the system impulse response behavior of the testbed for the different fluids. Based on the identified MC flow characteristics, we extend an existing mathematical framework for our SPION testbed to capture the flow properties of blood. We evaluate its applicability to the collected data in comparison to two existing theoretical SIR models for MC in blood. In our work, we see that the added complexity of the transmission in blood opens up promising new possibilities to improve communication through the human circulatory system.
    (link to more information)

  • New IEEE Internet of Things Journal article

    August 14, 2025

    Our article Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things has been accepted for publication in IEEE Internet of Things Journal. This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.
    (link to more information)

  • Paper Presentation at IEEE ICCCN 2025

    August 05, 2025

    Jakob Johannes Rühlow and Joana Angjo presented our paper Random Access in IRS-assisted 802.11 Networks at the The 34th International Conference on Computer Communications and Networks (ICCCN 2025), Tokyo, Japan. This work investigates the performance of IRS-assisted networks working based on CSMA/CA random access. Upon showing that these scenarios are prone to hidden terminals, we propse two solutions towards them, which are also based on usage of IRS. To evaluate them, a new ns-3 framework is developed, ns3IRS, which enables modelling the full-stack IRS-assisted Wi-Fi networks. The two mitigation strategies are splitting a centralized IRS or deploying small IRSs near the stations. Results show these methods enhance the networks performance in terms of several key performance indicators, showing that a strategic deployment of IRS can also help in mitigating the hidden terminals it creates in the first place.

  • Guest Lecture: Artificial Intelligence and the Internet of Bio-Nano-Things

    July 10, 2025

    Our team member Jorge Torres Gómez gave a lecture on the joint topic of AI and the Internet of Bio-Nano-Things (IoBNT). The lecture took place at the University of Linz with researchers from the Institute of Communications Engineering and RF-Systems. The lecture overviewed the recent trends on neural networks for the IoBNT with a focus on the synthesis of neural networks in the molecular domain.

  • New Elsevier Computer Communications article

    July 10, 2025

    Our article Deep Reinforcement Learning based Interference Optimization for Coordinated Beamforming in Ultra-Dense Wi-Fi Networks has been accepted for publication in Elsevier Computer Communications. Next-generation Wi-Fi networks are expected to have an ultra-dense deployment of access points (APs), thus, interference from overlapping basic service sets (OBSSs) poses challenges for interference management. Wi-Fi 8 aims at mitigating such interference using multi-access point coordination (MAPC). One of the MAPC variants is coordinated beamforming (Co-BF), where neighboring APs direct their signals towards specific users. Besides beam steering, APs can also perform null steering, which is more complex but can bring greater performance gains. In this paper, we present a centralized approach named intelligent null steering by reinforcement learning (IntelliNull), designed to reduce interference from neighboring transmitters by coordinated nulling while maximizing the signal quality at each station. We show that training the beam and null steering mechanism with a deep deterministic policy gradient (DDPG), it is possible to steer beams toward associated stations while intelligently nulling the most destructive interference from OBSS rather than nulling random interference directions. This method enhances communication between the AP and neighboring stations by reducing channel access contention, enabling transmissions at full power, and reducing worst-case latency. The proposed IntelliNull agent continuously adapts to changes in the network environment, including node mobility using channel state information (CSI) collected in real-time.
    (link to more information)

  • TKN team at Berlin 6G Conference

    July 02, 2025

    The TKN team joined the Berlin 6G Conference to discuss research results and new ideas related to 6G and beyond. The picture shows our team in front our booth as part of the 6G-RIC project.

  • New DFG project SmartSynch

    June 20, 2025
    Our proposal Synchronizing in the nanoscale: A research project to enable communication networks in molecular communication channels (SmartSynch) has been accepted for funding by the German research foundation DFG. In this project, we will develop robust synchronization mechanisms for nanodevices in the dynamic blood flow environment. Our objectives include devising a realistic model for the transmission-reception scheme in human vessels, optimizing reported synchronization mechanisms in the literature for this particular environment, and designing a methodology to evaluate the preamble sequence and the data packet length.
    (link to more information)

  • New IEEE Transactions on Mobile Computing article

    June 16, 2025

    Our article Reconsidering Sparse Sensing Techniques for Channel Sounding using Splicing has been accepted for publication in IEEE Transactions on Mobile Computing. Multi-band splicing offers a promising solution to extend existing band-limited communication systems to support high-precision sensing applications. This technique involves performing narrow-band measurements at multiple center frequencies, which are then combined to effectively increase the bandwidth without changing the sampling rate. In this paper, we introduce a mmWave channel sounder based on multi-band splicing, leveraging the sparse nature of wireless channels through compressed sensing and sparse recovery techniques for channel reconstruction. We focus on three sparse recovery methods: the widely used grid-based orthogonal matching pursuit (OMP) algorithm as a baseline, our newly developed two-stage mmSplicer algorithm, which extends the OMP method by introducing an additional stage for improving its performance for our application, and our adaptation of sparse reconstruction by separable approximation (SpaRSA), named Net-SpaRSA, optimized for wireless applications.
    (link to more information)

  • New IEEE Transactions on Wireless Communications article

    June 15, 2025

    Our article Rejuvenating IRS: AoI-based Low Overhead Reconfiguration Design has been accepted for publication in IEEE Transactions on Wireless Communications. Intelligent reflective surface (IRS) technologies help mitigate undesirable effects in wireless links by steering the communication signal between transmitters and receivers. An IRS improves the communication link but inevitably introduces more communication overhead. This occurs especially in mobile scenarios, where the user's position must be frequently estimated to re-adjust the IRS elements periodically. Such an operation requires balancing the amount of training versus the data time slots to optimize the communication performance in the link. Aiming to study this balance with the age of information (AoI) framework, we address the question of how often an IRS needs to be updated with the lowest possible overhead and the maximum of freshness of information. We derive the corresponding analytical solution for a mobile scenario, where the transmitter is static and the mobile user (MU) follows a random waypoint mobility model. We provide a closed-form expression for the average peak age of information (PAoI), as a metric to evaluate the impact of the IRS update frequency.
    (link to more information)

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Last modified: 2024-04-28