Harvesting entropy for random number generation for internet of things constrained devices using on-board sensors

0Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

Abstract

Entropy in computer security is associated with the unpredictability of a source of randomness. The random source with high entropy tends to achieve a uniform distribution of random values. Random number generators are one of the most important building blocks of cryptosystems. In constrained devices of the Internet of Things ecosystem, high entropy random number generators are hard to achieve due to hardware limitations. For the purpose of the random number generation in constrained devices, this work proposes a solution based on the least-significant bits concatenation entropy harvesting method. As a potential source of entropy, on-board integrated sensors (i.e., temperature, humidity and two different light sensors) have been analyzed. Additionally, the costs (i.e., time and memory consumption) of the presented approach have been measured. The results obtained from the proposed method with statistical fine tuning achieved a Shannon entropy of around 7.9 bits per byte of data for temperature and humidity sensors. The results showed that sensor-based random number generators are a valuable source of entropy with very small RAM and Flash memory requirements for constrained devices of the Internet of Things.

Cite

CITATION STYLE

APA

Pawlowski, M. P., Jara, A., & Ogorzalek, M. (2015). Harvesting entropy for random number generation for internet of things constrained devices using on-board sensors. Sensors (Switzerland), 15(10), 26838–26865. https://doi.org/10.3390/s151026838

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free