The proliferation of Internet of Things (IoT) devices and embedded processors has recently spurred rapid advances in hardware-level security. This paper systematically reviews developments in securing microcontroller units (MCUs) and constrained embedded platforms from 2020 to 2026, a period marked by the finalization of NIST’s post-quantum cryptography standards and accelerated commercial deployment of hardware security primitives. Through analysis of the peer-reviewed literature, industry implementations, and standardization efforts, we survey five critical areas: post-quantum cryptography (PQC) implementations on resource-constrained hardware, physically unclonable functions (PUFs) for device authentication, hardware Roots of Trust and secure boot mechanisms, side-channel attack mitigations, and Trusted Execution Environments (TEEs) for microcontroller-class devices. For each domain, we analyze technical mechanisms, deployment constraints (power, memory, cost), security guarantees, and commercial maturity. Our review distinguishes itself through its integration perspective, examining how these primitives must be composed to secure real-world embedded systems, and its emphasis on post-standardization PQC developments. We highlight critical gaps including PQC memory overhead challenges, ML-resistant PUF designs, and TEE developer friction, while documenting commercial progress such as PSA Level 3 certified components and 500+ million PUF-enabled devices deployed. This synthesis provides practitioners with practical guidance for securing the next generation of IoT and embedded systems.

This paper presents a beta regression framework for modeling bounded biometric performance metrics in longitudinal studies. While existing research appropriately uses standardized scores and transformations, operational stakeholders often require direct modeling of recognition rates for decision-making. Through simulation studies, we demonstrate that direct application of linear mixed models to True Accept Rates can produce invalid predictions exceeding probability bounds, while beta regression maintains mathematical validity. Using synthetic data calibrated to published child face recognition patterns (Deb et al., Bahmani et al.), beta regression correctly captures heteroscedastic variance structures and provides appropriate confidence intervals for bounded outcomes. The framework identifies distinct agespecific degradation patterns: rapid decline for ages 3-5, surprising stability for ages 5.5-7, and delayed degradation after puberty. Simulation results suggest differentiated re-enrollment strategies could optimize resource allocation, though validation on real biometric data is required before operational deployment. We provide implementation code and a detailed validation protocol to facilitate real-data studies. This work bridges statistical methodology and biometric evaluation, offering a complementary tool when percentage-based metrics are operationally essential. Available at: https://github.com/astoreyai/ beta-regression-pediatricface.