Secure Chaos-based PRNG for Low-Power and Resource-Constrained Devices

Abstract This paper presents a secure and effi-cient chaos-based pseudo-random number generator (PRNG) optimized for low-power, resource-constrained platforms. A lightweight, self-adaptive Perturbation-Randomization mechanism is embedded into the Tin-kerbell map to mitigate precision-induced degrada-tion and boost entropy, without increasing complex-ity. A hardware-efficient post-processing unit further enhances output randomness.Extensive evaluation confirms compliance with NIST SP800-22, Dieharder, and TestU01, while nonlinear measures (LLE, PE, SampEn, RQA) highlight strong dynamical complexity. Implemented on an Artix-7 FPGA, the design achieves 2.33 Gbps throughput at just 0.115 W power. Compared to ex-isting works, it offers full statistical validation, low area (227 LUTs, 232 FFs, 16 DSPs), and proven IoT suitability—unlike many prior designs that lack empir-ical support.