TCA-Oriented Attractor Editing in a Discrete Memristive Rulkov Neuron with Application to Lightweight IoV Image Encryption
- 1School of Optoelectronic Engineering, Zaozhuang University, Zaozhuang, 277160, China
Abstract
The emerging demand for lightweight image security in the Internet of Vehicles (IoV) highlights the need for chaotic sources with programmable dynamical characteristics. This paper develops a three-dimensional discrete memristive Rulkov neuron and proposes a translation-constraint-assignment (TCA) criterion for attractor editing. By partitioning the memristive state space and assigning local gain coefficients to different regions, TCA establishes an explicit mapping among state intervals, local parameters, and attractor properties. Through coordinated translation, regional constraint, and parameter assignment, the proposed criterion enables attractor states to be edited according to prescribed dynamical and spatial requirements within a unified system structure. This mechanism supports the controllable construction of coexisting attractors through regional gain assignments and interval settings, enabling configurable combinations of dynamical states and spatial arrangements. The resulting nonlinear behaviors are analyzed numerically and verified on an STM32 platform. Furthermore, configurable chaotic sequences generated by the edited attractors are combined with SPECK64/128 and bidirectional diffusion to construct a lightweight image encryption scheme. Experimental results demonstrate favorable security, efficiency, and applicability to resource-constrained IoV devices.
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BibTeX
@article{gao2026tcaoriented,
title = {{TCA-Oriented Attractor Editing in a Discrete Memristive Rulkov Neuron with Application to Lightweight IoV Image Encryption}},
author = {Shan Gao and Hongjie Wang and Weiyao Xu and Jiayue Qian and Yike Zhou and Yongxin Li},
journal = {Mathematics},
year = {2026},
month = nov,
doi = {10.3390/math14214062},
url = {https://www.mdpi.com/2227-7390/14/21/4062},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - TCA-Oriented Attractor Editing in a Discrete Memristive Rulkov Neuron with Application to Lightweight IoV Image Encryption
AU - Shan Gao
AU - Hongjie Wang
AU - Weiyao Xu
AU - Jiayue Qian
AU - Yike Zhou
AU - Yongxin Li
PY - 2026
DA - 2026/11/05
JO - Mathematics
DO - 10.3390/math14214062
UR - https://www.mdpi.com/2227-7390/14/21/4062
AB - The emerging demand for lightweight image security in the Internet of Vehicles (IoV) highlights the need for chaotic sources with programmable dynamical characteristics. This paper develops a three-dimensional discrete memristive Rulkov neuron and proposes a translation-constraint-assignment (TCA) criterion for attractor editing. By partitioning the memristive state space and assigning local gain coefficients to different regions, TCA establishes an explicit mapping among state intervals, local parameters, and attractor properties. Through coordinated translation, regional constraint, and parameter assignment, the proposed criterion enables attractor states to be edited according to prescribed dynamical and spatial requirements within a unified system structure. This mechanism supports the controllable construction of coexisting attractors through regional gain assignments and interval settings, enabling configurable combinations of dynamical states and spatial arrangements. The resulting nonlinear behaviors are analyzed numerically and verified on an STM32 platform. Furthermore, configurable chaotic sequences generated by the edited attractors are combined with SPECK64/128 and bidirectional diffusion to construct a lightweight image encryption scheme. Experimental results demonstrate favorable security, efficiency, and applicability to resource-constrained IoV devices.
SN - 2227-7390
ER -