Computational Science Hub (CSH)

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/16924

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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, Jianwei
Monitoring 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.
Network impact analysis on the performance of Secure Group Communication schemes with focus on IoT
(2024) Prantl, Thomas; Amann, Patrick; Krupitzer, Christian; Engel, Simon; Bauer, André; Kounev, Samuel
Secure and scalable group communication environments are essential for many IoT applications as they are the cornerstone for different IoT devices to work together securely to realize smart applications such as smart cities or smart health. Such applications are often implemented in Wireless Sensor Networks, posing additional challenges. Sensors usually have low capacity and limited network connectivity bandwidth. Over time, a variety of Secure Group Communication (SGC) schemes have emerged, all with their advantages and disadvantages. This variety makes it difficult for users to determine the best protocol for their specific application purpose. When selecting a Secure Group Communication scheme, it is crucial to know the model’s performance under varying network conditions. Research focused so far only on performance in terms of server and client runtimes. To the best of our knowledge, we are the first to perform a network-based performance analysis of SGC schemes. Specifically, we analyze the network impact on the two centralized SGC schemes SKDC and LKH and one decentralized/contributory SGC scheme G-DH. To this end, we used the ComBench tool to simulate different network situations and then measured the times required for the following group operations: group creation, adding and removing members. The evaluation of our simulation results indicates that packet loss and delay influence the respective SGC schemes differently and that the execution time of the group operations depends more on the network situations than on the group sizes.
On the structural analysis and optimal input design for joint state and parameter estimation
(2025) Lepsien, Arthur; Kügler, Philipp; Schaum, Alexander
This paper addresses the problem of joint state and parameter estimation for nonlinear affine-input systems with positive parameters including the design of a closed-loop optimal input adaptation to increase an identifiability measure for the system. The identifiability itself is considered in the context of structural observability of the system dynamics based on structural analysis of the system including the unknown parameters as additional states. In particular, the network graph-based interpretation of structural observability is employed at this point. This analysis motivates to include time derivatives of the measurements as additional system outputs to enhance the structural observability properties. For this purpose robust exact differentiation is considered, relying on the super-twisting algorithm to obtain finite time convergent estimates of these signals. Using the extended measurement signal, a continuous-discrete Extended Kalman filter is proposed that ensures strictly positive estimates for the parameters. Based on the estimates of states and parameters the input signal is determined using a moving horizon optimal predictive control that evaluates the condition number of the Fisher information matrix, thus maximizing the information content of the measurements with respect to the parameters. The proposed scheme extends and combines different previously discussed approaches from the literature and is evaluated by means of a thermal process example in simulation and experiment, showing high potential for similar system identification problems.
Recommendation of secure group communication schemes using multi-objective optimization
(2023) Prantl, Thomas; Bauer, André; Iffländer, Lukas; Krupitzer, Christian; Kounev, Samuel
The proliferation of IoT devices has made them an attractive target for hackers to launch attacks on systems, as was the case with Netflix or Spotify in 2016. As the number of installed IoT devices is expected to increase worldwide, so does the potential threat and the importance of securing these devices and their communications. One approach to mitigate potential threats is the usage of the so-called Secure Group Communications (SGC) schemes to secure the communication of the devices. However, it is difficult to determine the most appropriate SGC scheme for a given use case because many different approaches are proposed in the literature. To facilitate the selection of an SGC scheme, this work examines 34 schemes in terms of their computational and communication costs and their security characteristics, leading to 24 performance and security features. Based on this information, we modeled the selection process for centralized, distributed, and decentralized schemes as a multi-objective problem and used decision trees to prioritize objectives.

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