Recent progress in quantum computers severely endangers the security of widely used public-key cryptosystems and of all communication that relies on it. Thus, the US NIST is currently exploring new post-quantum cryptographic algorithms that are robust against quantum computers. Security is seen as one of the most critical issues of low-power IoT devices—even with pre-quantum public-key cryptography—since IoT devices have tight energy constraints, limited computational power and strict memory limitations. In this paper, we present, to the best of our knowledge, the first in-depth investigation of the application of potential post-quantum key encapsulation mechanisms (KEMs) and digital signature algorithms (DSAs) proposed in the related US NIST process to a state-of-the-art, TLS-based, low-power IoT infrastructure. We implemented these new KEMs and DSAs in such a representative infrastructure and measured their impact on energy consumption, latency and memory requirements during TLS handshakes on an IoT edge device. Based on our investigations, we gained the following new insights. First, we show that the main contributor to high TLS handshake latency is the higher bandwidth requirement of post-quantum primitives rather than the cryptographic computation itself. Second, we demonstrate that a smart combination of multiple DSAs yields the most energy-, latency- and memory-efficient public key infrastructures, in contrast to NIST’s goal to standardize only one algorithm. Third, we show that code-based, isogeny-based and lattice-based algorithms can be implemented on a low-power IoT edge device based on an off-the-shelf Cortex M4 microcontroller while maintaining viable battery runtimes. This is contrary to much research that claims dedicated hardware accelerators are mandatory.

With the recent increase in the use of augmented reality (AR) in educational laboratory settings, there is a need for new intelligent sensor systems capturing all aspects of the real environment. We present a smart sensor system meeting these requirements for STEM (science, technology, engineering, and mathematics) experiments in electrical circuits. The system consists of custom experiment boxes and cables combined with an application for the Microsoft HoloLens 2, which creates an AR experiment environment. The boxes combine sensors for measuring the electrical voltage and current at the integrated electrical components as well as a reconstruction of the currently constructed electrical circuit and the position of the sensor box on a table. Combing these data, the AR application visualizes the measurement data spatially and temporally coherent to the real experiment boxes, thus fulfilling demands derived from traditional multimedia learning theory. Following an evaluation of the accuracy and precision of the presented sensors, the usability of the system was evaluated with $n=20$ pupils in a German high school. In this evaluation, the usability of the system was rated with a system usability score of 94 out of 100.

Digitization offers new perspectives for educational research to identify the effects of visualizations regarding cognitive processing. In addition, new types of data can be generated, expanding the possibilities for visualizing cognitive processes and understanding human learning. Digital twins are already used in Industry 4.0, as an additional visualization to a real object, for data mining and data analysis for process optimization. The increasing integration of digital twins in the industrial sector requires the formulation of corresponding educational goals to ensure high-quality and future-oriented education. Therefore, future generations must be introduced to technologies from industry during their education. In this paper, an intelligent photometric measurement system called SmaEPho with a digital twin for science, technology, engineering, and mathematics (STEM) learning is presented. In addition to its function as a photometric measurement device, an intelligent sensor technology allows for data generation on the user’s usage behavior. The digital twin reflects and visualizes these data in real-time. This enables a variety of new didactic and methodological approaches in teaching. A first study evaluating the hardware and tracking components of SmaEPho shows that the deviation accuracy of the measurement system is sufficient for experimental applications in schools. Another study with $n=52$ students confirmed the excellent usability of the SmaEPho hardware platform. These research results lay the foundation for a variety of future research questions on data analysis and machine learning algorithms with the aim of increasing the quality of education. The use of intelligent digital twins as an element of digitization in educational contexts offers the extended possibility of identifying cognitive processing steps using this technology.

Energy harvesting is a key technology towards a more sustainable Internet of Things (IoT), as it facilitates to operate IoT sensor devices without the need of batteries. However, energy harvesting-based power sources are transient, which is why such IoT devices need to be prepared for unpredictable power losses. This can be ensured by a power loss detection method that triggers a backup procedure just before a power loss. In this procedure, the volatile system data is saved in a nonvolatile memory (NVM), from which it is restored once the power returns (checkpointing). State-of-the-art detection methods monitor the supply voltage to detect upcoming power losses by a dropping voltage level. However, these methods detect power losses very late and thus leave only limited amounts of residual energy for the backup procedure. Therefore, it is very difficult to guarantee a reliable checkpointing process on energy-intensive flash memory, which is the state-of-the-art NVM technology on common IoT sensor devices.In this paper, we propose a new method for thermal energy harvesters, i.e., thermoelectric generators (TEGs), which allows a significantly earlier detection of potential power losses than related work. The core concept is to monitor the exciting quantity of the harvester, i.e., the temperature gradient across the surfaces of the TEG, to predict the future trend of the power output once this gradient starts to decline. This method exploits the thermal capacity of the TEG, which leads to a delayed temperature penetration from the surface into its internal, power generating structure. Our results show, that the shortfall below critical power output levels can be predicted several seconds in advance.