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Kalman observers in estimating the states of chaotic neurons for image encryption under MQTT for IoT protocol

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Abstract

Chaotic systems based on artificial neurons present high randomness levels that are desired for applications like data encryption. In this paper, the chaotic systems based on the Hopfield, Cellular, Aihara, and the Rulkov neural models are synchronized and implemented on Raspberry Pi, which allows connectivity to a Machine to Machine (M2M) broker that is available under MQTT for IoT protocol. The process of encryption synchronizes two chaotic systems called publisher and subscriber that are controlled by an M2M broker. The publisher sends data that can be recovered by the subscriber, whose state observers are used to estimating the time series of the chaotic neuron to decrypt the data, increasing at the same time the security of the encrypted message. We show that the classical Kalman filter, its extended version, and the recent novelty, the unscented Kalman filter, all of them are powerful techniques in estimating the states of chaotic neurons. The randomness is evaluated by NIST tests, and the image encryption process is evaluated by performing correlation, histogram, variance, entropy, NPCR, and UACI tests.

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Authors and Affiliations

  1. Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No.1, Tonantzintla, 72840, Puebla, Mexico

    Jonathan Daniel Díaz-Muñoz, Esteban Tlelo-Cuautle, Juan Manuel Ramírez Cortés & José de Jesús Rangel-Magdaleno

  2. CONACYT Research-Fellow-Electronics Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No.1, Tonantzintla, 72840, Puebla, Mexico

    Israel Cruz-Vega

Authors
  1. Jonathan Daniel Díaz-Muñoz
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  2. Israel Cruz-Vega
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  3. Esteban Tlelo-Cuautle
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Correspondence to Esteban Tlelo-Cuautle.

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Díaz-Muñoz, J.D., Cruz-Vega, I., Tlelo-Cuautle, E. et al. Kalman observers in estimating the states of chaotic neurons for image encryption under MQTT for IoT protocol. Eur. Phys. J. Spec. Top. 231, 945–962 (2022). https://doi.org/10.1140/epjs/s11734-021-00319-2

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