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Sensors 2015, 15(10), 26838-26865; doi:10.3390/s151026838

Harvesting Entropy for Random Number Generation for Internet of Things Constrained Devices Using On-Board Sensors

1
Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre 3960, Switzerland
2
Department of Information Technologies, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow 30-348, Poland
3
Research and Development Department, HOP Ubiquitous, 30562 Ceuti, Murcia, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Yunchuan Sun and Shengling Wang
Received: 27 July 2015 / Revised: 14 October 2015 / Accepted: 15 October 2015 / Published: 22 October 2015
(This article belongs to the Special Issue Identification, Information & Knowledge in the Internet of Things)
View Full-Text   |   Download PDF [1038 KB, uploaded 26 October 2015]   |  

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. View Full-Text
Keywords: random number generation; entropy; on-board sensors; Internet of Things random number generation; entropy; on-board sensors; Internet of Things
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pawlowski, M.P.; Jara, A.; Ogorzalek, M. Harvesting Entropy for Random Number Generation for Internet of Things Constrained Devices Using On-Board Sensors. Sensors 2015, 15, 26838-26865.

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