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Internet of Things (IoT) is a set of connected smart devices providing and sharing rich data in real-time without involving a human being. However, IoT is a security nightmare because like in the early computer systems, security issues are not considered in the design step. Thereby, each IoT system could be susceptible to malicious users and uses. To avoid these types of situations, many approaches and techniques are proposed by both academic and industrial researches.DNA computing is an emerging and relatively new field dealing with data encryption using a DNA computing concepts. This technique allows rapid and secure data transfer between connected objects with low power consumption. In this paper, authors propose a symmetric cryptography method based on DNA. This method consists in cutting the message to encrypt/decrypt in blocks of characters and use a symmetric key extracted from a chromosome for encryption and decryption. Implemented on the embedded platform of a Raspberry Pi, the proposed method shows good performances in terms of robustness, complexity and attack resistance.

Medical image enhancement is considered as a challenging image-processing framework because the low quality of images resulted after acquisition and transmission seriously affects the clinical diagnosis and observation. In order to improve the image visual quality, a novel medical image enhancement algorithm that is based on the contrast limited adaptive histogram equalization and pelican optimization algorithm is proposed in this work. The primary step of the process is the medical generation using Text-to-image generative model. Then, the estimation of the clip-limit, which controls the enhancing performance. Finally, the operation of enhancing the medical images using our proposed method. As a conclusion, the simulation experiments prove that our proposed algorithm achieves superior performance qualitatively and quantitatively, compared with the state-of-the-art experimental methods. Furthermore, the advantageous characteristic of this algorithm is its applicability in multiple types of images. In this basis, the improvement of the medical images’ quality using our algorithm allows attaining a superior visual impact on the processed image and increase the rate of conformity in the clinical diagnosis.

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