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Description
One of the primary challenges in the Internet of Things (IoT) is efficiently authenticating massively deployed devices without relying on user technical expertise. Traditional methods such as certificates and public-private key pairs are impractical on resource-constrained embedded devices due to their computational demands, while read-only unique identifiers incur high implementation costs, vulnerability to circumvention, and require oversight by authoritative entities. In contrast, Physical Unclonnable Functions (PUFs) utilize the inherent physical properties of devices to securely and reliably generate unique identifiers.
This paper presents an analysis of computational performance in processing data streams from a nonlinear oscillator circuit to generate Physical Unclonable Function (PUF)-based identifiers. The investigated computational tasks include signal acquisition, quantization, error correction, and the application of a final hashing function. These tasks are executed and evaluated on an ARM Cortex-M processor, chosen for its suitability in IoT applications.
The significance of this research lies in exploring computational processes that provide cost-effective, reliable, and scalable methods to enhance IoT device authentication, ensuring trust in such systems. By analyzing the performance of PUF-based unique identifier computation, this study contributes important findings to improving IoT device security through modern, robust authentication mechanisms.
