Browsing by Author "Dalkilic, Gokhan"

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    An Ultra-light PRNG Passing Strict Randomness Tests and Suitable for Low Cost Tags
    (UNIV SUCEAVA FAC ELECTRICAL ENG, 2016) Mehmet Hilal Ozcanhan; Mehmet Suleyman Unluturk; Gokhan Dalkilic; Unluturk, Mehmet Suleyman; Dalkilic, Gokhan; Ozcanhan, Mehmet Hilal
    A pseudo-random number generator for low-cost RFID tags is presented. The scheme is simple sequential and secure yet has a high performance. Despite its lowest hardware complexity our proposal represents a better alternative than previous proposals for low-cost tags. The scheme is based on the well-founded pseudo random number generator Mersenne Twister. The proposed generator takes low-entropy seeds extracted from a physical characteristic of the tag and produces outputs that pass popular randomness tests. Contrarily previous proposal tests are based on random number inputs from a popular online source which are simply unavailable to tags. The high performance and satisfactory randomness of present work are supported by extensive test results and compared with similar previous works. Comparison using proven estimation formulae indicates that our proposal has the best hardware complexity power consumption and the least cost.
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    Citation - WoS: 2
    Citation - Scopus: 1
    The Pseudorandom Number Generator Generation Method with Genetic Programming for Lightweight Devices
    (Institute of Electrical and Electronics Engineers Inc., 2018) Cem Kösemen; Ömer Aydin; Gokhan Dalkiltc; Aydin, Omer; Dalkilic, Gokhan; Aydm, Omer; Dalklhc, Gokhan; Kosemen, Cem
    In this research a genetic programming (GP) method is proposed for producing pseudorandom number generators (PRNG) for lightweight devices especially wireless identification and sensing platform (WISP) family devices. These PRNGs are produced with genetic programming methods using bit-entropy and serial correlation coefficient calculation as the fitness function. Qualities of these PRNGs are tested with the NIST statistical test suite that is a comprehensive tool that evaluates the statistical quality of the output of given PRNG. In addition a set of PRNG is generated with this GP method and the results are examined statistically. © 2019 Elsevier B.V. All rights reserved.