The Industrial Internet of Things (IIoT) has been introduced in an era of increasingly broad potentials in the medical industry. In recent years, IIoT-based healthcare applications have grown in popularity, with the majority of them relying on Wireless Body Area Network (WBAN) for flexibility. There have been a few recent works that have investigated SDN-based fog architecture for constructing smart healthcare systems. However, the best fog node from the fog layer must be identified and limit the transmission of unnecessary data. To address this issue, the Intelligent Software-defined Fog Architecture (i-Health) is developed in this work. Based on the prior data pattern of each patient, the controller will decide whether to send the data to the fog layer. Furthermore, we introduced the Fog Ranking Service (FRS) and Fog Probing Service (FPS) to select the best fog node. The performance comparison reveals that the proposed i-Health outperforms existing benchmark approaches.

The rapidly growing multimedia image data driven by real-time messaging technologies is particularly evident in applications such as autonomous vehicle tracking, smart cities, surveillance systems and many more. Considering images, data privacy and security are of paramount importance. Yet, many existing methods need to pay more attention to the specific challenges posed by chaotic maps, such as limited parameter coverage and insufficient chaotic behaviour. We present a novel method for image encryption that combines a cyclic block function during the confusion phase and a DNA-based Linear Feedback Shift Register (LFSR) in the diffusion phase to render the final cipher image. This process involves diagonal cyclic shifting and swapping of pixel blocks to minimize pixel correlation. DNA cryptography-based LFSR is particularly efficacious in high-quality pseudorandom number generation due to its robust statistical effects. Besides that, DNA-based operations improve the encryption speed, making the process more efficient. The proposed cryptosystem is validated through several methods, including histogram analysis, correlation assessment, entropy measurement, key sensitivity evaluation, and χ2 testing. Our algorithm offers superior security and efficiency, outperforming established schemes in terms of security and robustness.