Fakultätsübergreifend / Sonstige Einrichtung
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Publication Benchmarking of secure group communication schemes with focus on IoT(2024) Prantl, Thomas; Bauer, André; Engel, Simon; Horn, Lukas; Krupitzer, Christian; Iffländer, Lukas; Kounev, SamuelAs Internet of Things (IoT) devices become ubiquitous, they face increasing cybersecurity threats. Unlike standard 1-to-1 communication, the unique challenge posed by n-to-n communication in IoT is that messages must not be encrypted for a single recipient but for a group of recipients. For this reason, using Secure Group Communication (SGC) schemes is necessary to encrypt n-to-n communication efficiently for large group sizes. To this end, the literature presents various SGC schemes with varying features, performance profiles, and architectures, making the selection process challenging. A selection from this multitude of SGC schemes should best be made based on a benchmark that provides an overview of the performance of the schemes. Such a benchmark would make it much easier for developers to select an SGC scheme, but such a benchmark still needs to be created. This paper aims to close this gap by presenting a benchmark for SGC schemes that focus on IoT. Since the design of a benchmark first requires the definition of the underlying business problems, we defined suitable problems for using SGC schemes in the IoT sector as the first step. We identified a common problem for the centralized and decentralized/hybrid SGC schemes, whereas the distributed/contributory SGC schemes required defining an independent business problem. Based on these business problems, we first designed a specification-based benchmark, which we then extended to a hybrid benchmark through corresponding implementations. Finally, we deployed our hybrid benchmark in a typical IoT environment and measured and compared the performance of different SGC schemes. Our findings reveal notable impacts on calculation times and storage requirements without a trusted Central Instance (CI) in distributed/contributory SGC schemes.Publication Doing research in Chinaein Einblick in die Kultur und Forschungslandschaft Chinas. - China-Kompetenz in Hohenheim
(2020) Klenk, Johannes; Schmidt, Alexander; Waschek, FranziskaMit dieser Broschüre soll Wissenschaftlerinnen und Wissenschaftlern, die sich für Forschung in und mit China interessieren oder eventuell bereits ein konkretes Forschungsvorhaben mit Bezug zu China haben, einen Überblick über einige Themen geben werden, die aus der Sicht der Autoren für geplante Projekte mit chinesischen Partnerinnen und Partnern oder für Aufenthalte vor Ort relevant sind.Publication Enabling adaptive food monitoring through sampling rate adaptation for efficient, reliable critical event detection(2025) Jox, Dana; Schweizer, Pia; Henrichs, Elia; Krupitzer, Christian; Jox, Dana; Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany; Schweizer, Pia; Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany; Niu, Jianwei; Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany; Niu, JianweiMonitoring systems are essential in many fields, such as food production, storage, and supply, to collect information about applications or their environments to enable decision-making. However, these systems generate massive amounts of data that require substantial processing. To improve data analysis efficiency and reduce data collectors’ energy demand, adaptive monitoring is a promising approach to reduce the gathered data while ensuring the monitoring of critical events. Adaptive monitoring is a system’s ability to adjust its monitoring activity during runtime in response to internal and external changes. This work investigates the application of adaptive monitoring—especially, the adaptation of the sensor sampling rate—in dynamic and unstable environments. This work evaluates 11 distinct approaches, based on threshold determination, statistical analysis techniques, and optimization methods, encompassing 33 customized implementations, regarding their data reduction extent and identification of critical events. Furthermore, analyses of Shannon’s entropy and the oscillation behavior allow for estimating the efficiency of the adaptation algorithms. The results demonstrate the applicability of adaptive monitoring in food storage environments, such as cold storage rooms and transportation containers, but also reveal differences in the approaches’ performance. Generally, some approaches achieve high observation accuracies while significantly reducing the data collected by adapting efficiently.Publication Jahresbericht 2006 des Rektors / Universität Hohenheim(2007) ; Liebig, Hans-PeterPublication Jahresbericht 2007 des Rektors / Universität Hohenheim(2008) ; Liebig, Hans-PeterPublication Jahresbericht 2008 / Universität Hohenheim(2009) Liebig, Hans-PeterPublication Jahresbericht 2009 / Universität Hohenheim(2010) Liebig, Hans-PeterPublication Jahresbericht 2010 / Universität Hohenheim(2011) Liebig, Hans-PeterPublication Jahresbericht 2011 / Universität Hohenheim(2012) Liebig, Hans-PeterPublication Jahresbericht 2012 / Universität Hohenheim(2013) Dabbert, StephanPublication Jahresbericht 2016 / Universität Hohenheim(2017) Dabbert, StephanPublication Jahresbericht 2017 / Universität Hohenheim(2018) Dabbert, StephanPublication Jahresbericht 2018 / Universität Hohenheim(2019) Dabbert, StephanPublication Jahresbericht 2019 / Universität Hohenheim(2020) Dabbert, StephanPublication Jahresbericht 2020 / Universität Hohenheim(2021) Dabbert, StephanPublication Jahresbericht 2021 / Universität Hohenheim(2022) Dabbert, StephanPublication Jahresbericht 2022 / Universität Hohenheim(2023) Dabbert, StephanPublication Publication Publication Jahresbericht mit Zahlenspiegel 2013 / Universität Hohenheim(2014) Dabbert, Stephan