Bluetooth Mesh under the Microscope: How much ICN is Inside?

Bluetooth (BT) mesh is a new mode of BT operation for low-energy devices that offers group-based publish-subscribe as a network service with additional caching capabilities. These features resemble concepts of information-centric networking (ICN), and the analogy to ICN has been repeatedly drawn in the BT community. In this paper, we compare BT mesh with ICN both conceptually and in real-world experiments. We contrast both architectures and their design decisions in detail. Experiments are performed on an IoT testbed using NDN/CCNx and BT mesh on constrained RIOT nodes. Our findings indicate significant differences both in concepts and in real-world performance. Supported by new insights, we identify synergies and sketch a design of a BT-ICN that benefits from both worlds

PASSt – Predictive Analytics Services für Studienerfolgsmanagement

Hochschulen haben zunehmendes Interesse daran, den Studienerfolg ihrer Studierenden analysieren und quantifizieren zu können. In diesem Zusammenhang versucht das Projekt PASSt – Predictive Analytics Services für Studienerfolgsmanagement – einen Rahmen für die empirische Analyse und Vorhersage des Studienerfolges herzustellen: Studenten- und Studiendaten werden in eine generische Datenstruktur importiert, auf die Machine Learning und Simulationen angewendet werden. Die beiden wichtigsten Ergebnisse der Anwendung dieser Ansätze sind eine Vorhersage des Studienerfolgs und eine Strukturanalyse von Lehrplänen, die zur Verbesserung der Studienbedingungen für Studierende genutzt werden können. Das Framework verfügt darüber hinaus über eine zusammenfassende Visualisierung, die eine einfache Interpretation und Nutzung der Ergebnisse für die Curriculumsplanung ermöglicht. Dieses Projekt wurde am 1. Juni 2023 im Rahmen einer Online-Veranstaltung des BMBWF präsentiert. Die Präsentationsunterlagen finden Sie hier

Improving the Reliability of Bluetooth Low Energy Connections

o sustain a reliable data exchange, applications based on Bluetooth Low Energy (BLE) need to effectively blacklist channels and adapt the physical mode of an active connection at runtime. Although the BLE specification foresees the use of these two mechanisms, their implementation is left up to the radio vendors and has not been studied in detail yet. This paper fills this gap: we first investigate experimentally how to assess the quality of a BLE connection at runtime using information gathered from the radio. We then show how this information can be used to promptly blacklist poor channels and select a physical mode that sustains a high link-layer reliability while minimizing power consumption. We implement both mechanisms on two popular platforms and show experimentally that they allow to significantly improve the reliability of BLE connections, with a reduction in packet loss by up to 22 % compared to existing solutions