A close study about the varied anatomies of the S-Box algorithms published in the literature indicates that no attempt has been made using the notion of 3D. To cement the more cryptographic security, this research article ventures to present a novel S-Box algorithm exploiting the notion of 3D. First of all, the algorithm initializes a 3D scrambled S-Box with the value of -1. After that, a 1D array is initialized with all the numbers of the potential S-Box. Now, the values placed in the 1D array are inserted randomly to the different cells of the 3D scrambled S-Box until all the numbers from the 1D array are not shifted. The way, the S-Box has been developed bears an ample promise to raise the inherent non-linearity of the required S-Box which, in turn, equips the S-Box to defy the potential cryptanalytic threats from the hackers and other adversaries. Lorenz chaotic system has been employed to produce streams of random numbers. Through rigorous analysis and experimentation, we demonstrate the efficacy of our approach in enhancing cryptographic security, offering robust protection against various cyber threats. Our research contributes to the advancement of S-Box design methodologies, providing a promising avenue for strengthening encryption algorithms in contemporary cryptographic systems. Potential real-world applications include secure communications in IoT devices, encryption in smart grid infrastructures, and data protection in medical imaging systems.

Industrial Internet of Things (IIoT), also known as Industry 4.0 is a major revolution in the industry and it is designed for enhancement of productivity and reliability. Security is a prime concern in the implementation of IIoT technology along with other concerns like patch management. An inefficient or insecure security mechanism would permit rogue (illegitimate) devices to access crucial data and resources in the network. In order to reduce the involvement of malicious devices in IIoT networks and ensure the security of crucial data, we suggest a device authentication and session key generation scheme for devices in IIoT networks. The proposed mechanism is based on the bitwise XOR operation, one-way hash function, and concatenation operation. In this scheme, the server and node generate the session key independently, as it cannot be generated by any third entity. The proposed mechanism is scrutinized for its security using AVISPA and ProVerif tools (automatically) and BAN logic (mathematically) and it has been determined that the proposed scheme is safe and immune to any security threats. Performance analysis has proved that it consumes less resources with maximum throughput. Hence it is efficient and secure.

To detect the improper sitting posture of a person sitting on a chair, a posture detection system using machine learning classification has been proposed in this work. The addressed problem correlates to the third Sustainable Development Goal (SDG), ensuring healthy lives and promoting well-being for all ages, as specified by the World Health Organization (WHO). An improper sitting position can be fatal if one sits for a long time in the wrong position, and it can be dangerous for ulcers and lower spine discomfort. This novel study includes a practical implementation of a cushion consisting of a grid of 3 × 3 force-sensitive resistors (FSR) embedded to read the pressure of the person sitting on it. Additionally, the Body Mass Index (BMI) has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures (backward, front, left, and right-leaning) based on the five machine learning algorithms: ensemble boosted trees, ensemble bagged trees, ensemble subspace K-Nearest Neighbors (KNN), ensemble subspace discriminant, and ensemble RUSBoosted trees. The proposed arrangement is novel as existing works have only provided simulations without practical implementation, whereas we have implemented the proposed design in Simulink. The results validate the proposed sensor placements, and the machine learning (ML) model reaches a maximum accuracy of 99.99%, which considerably outperforms the existing works. The proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.

The Internet of Medical Things (IoMT) represents a specialized domain within the Internet of Things, focusing on medical devices that require regulatory approval to ensure patient safety. Trusted composition of IoMT systems aims to ensure high assurance of the entire composed system, despite potential variability in the assurance levels of individual components. Achieving this trustworthiness in IoMT systems, especially when using less-assured, commercial, off-the-shelf networks like Ethernet and WiFi, presents a significant challenge. To address this challenge, this paper advocates a systematic approach that leverages the Architecture Analysis & Design Language (AADL) along with Behavior Language for Embedded Systems with Software (BLESS) specification and implementation. This approach aims to provide high assurance on critical components through formal verification, while using less-assured components in a manner that maintains overall system determinism and reliability. A clinical case study involving an automated opioid infusion monitoring IoMT system is presented to illustrate the application of the proposed approach. Through this case study, the effectiveness of the systemic approach in achieving trusted composition of heterogeneous medical devices over less-assured networks is demonstrated.

The rapid expansion of IoT devices in maternal health monitoring enables continuous data collection and improved clinical assessment; however, it also introduces significant security and privacy concerns due to the sensitivity of maternal health information. This study investigates how artificial intelligence (AI) and machine learning (ML) can enhance both analytical performance and data protection in IoT-based maternal monitoring systems. The proposed framework employs Random Forest, Decision Tree, Support Vector Machine, and a stacking–bagging ensemble to improve maternal risk prediction and anomaly detection. Privacy-preserving techniques are integrated to secure physiological parameters: homomorphic encryption ensures data confidentiality during processing, while differential privacy limits information leakage from model outputs. Experimental results show that the stacking classifier combined with Random Forest achieved the highest accuracy of 82.3%, demonstrating greater robustness than traditional algorithms. Although differential privacy strengthened data protection, it reduced precision and F1-score, highlighting a trade-off between privacy and accuracy. Overall, integrating ensemble learning with privacy-preserving methods improves the security, accuracy, and reliability of IoT-driven maternal health monitoring systems.