Evaluation of Random Number Generator Utilizing Weather Data and LFSR

Abstract

Entropy sources (e.g., physical phenomena) are essential for true random number (TRN) generation. An unpredictable random number (URN) generator was previously proposed, which uses processor internal registers as its entropy sources. Another study proposed to integrate a linear feedback shift register (LFSR) in a processor, and sample it to generate a URN sequence. The entropy source of this URN is the fluctuation of sampling period. The current study proposes to use weather data as the entropy source for URN generation, where the sampling period is modified by the wind direction data. The derived URN sequences passed the Diehard test when the least sampling period β > 29 with a 32-bit LFSR. It also passed the NIST test when the weather data were accessed with an appropriate hash function when β was 32.