Indoor localization systems based on UltraWideBand (UWB) technology can typically achieve cm-level accuracy, but their performance degrades in Non-Line-of-Sight (NLoS) conditions. To cope with this problem, Machine Learning (ML) techniques have been applied to detect such NLoS conditions and adapt the localization algorithm accordingly. However, such ML techniques are typically optimized for accuracy, resulting in computationally-complex models that cannot be run on resource-constrained UWB devices. In this paper, we study and propose methods to reduce the computational complexity of NLoS classification models by applying ML-based feature selection and by reducing the window length of the channel impulse response for feature extraction. Specifically, we consider 29 features and study the effect of feature selection across five different datasets to obtain generalizable results. We show that we can extract two sets of only 3 and 8 features, which result in tiny ML models (smaller than 1kB), and low computation times ($3.6 \mathrm{~ms}$ and $27.7 \mathrm{~ms}$ on a 80MHz ESP8266 microcontroller, respectively). This allows a reduction of the runtime by more than $90 \%$ compared to the state of the art, while still maintaining an average classification accuracy above $85 \%$ across all five datasets.

Security is a vital property of SCADA systems, especially in critical infrastructure. An important aspect is maintaining (sub-)system integrity in networks of embedded control devices. One technology that is used to achieve this is remote attestation. It is used to prove the integrity of one system (prover) to another (challenger). However, due to the complexity of the maintenance of reference measurement, it is seen as impractical in such constrained distributed systems. In this work, we show how recent advances such as privilege-based attestation enable an architecture that is more feasible to use. Based on real control systems used for hydro-electric power plants, we evaluate the impact of the proposed infrastructure on the device performance and discuss our experiences with the consequences of using such technologies for the production and development processes of such systems.