Sixth generation (6G)-enabled smart cities with intelligent reconfigurable surfaces (IRSs), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA) technologies have the potential to transform consumer electronics through ultra-fast wireless connectivity, effective resource utilization, and intelligent urban services. This paper presents a new optimization scheme in NOMA multi-UAV (M-UAV) networks by utilizing IRSs. The proposed scheme focuses on ground user equipment (GUE) UAV association, UAV power allocation and IRS passive beamforming (BF), with the objective of enhancing the system’s spectral efficiency (SE) under the assumption of imperfect successive interference cancellation (i-SIC). To tackle the non-linearity of the problem, we first perform GUE UAV association for available power allocation and BF. Then, we decouple the joint transmit power and BF design problem into two parts. Subsequently, an efficient solution is achieved by employing a combination of sequential quadratic programming, successive convex approximation, and difference-of-convex programming techniques. Through comprehensive numerical evaluations, we validate the effectiveness of the mathematical solution and demonstrate the superiority of our proposed optimization scheme over benchmark schemes. The results showcase notable performance improvements in terms of SE, thereby highlighting the potential benefits that can be achieved by leveraging the proposed approach in NOMA M-UAV networks.

The widespread use of the Internet of Things (IoT) has led to significant breakthroughs in various fields but has also exposed critical vulnerabilities to evolving cybersecurity threats. Current Intrusion Detection Systems (IDSs) often fail to provide real-time detection, scalability, and interpretability, particularly in high-speed optical network environments. This research introduces XIoT, which is a novel explainable IoT attack detection model designed to address these challenges. Leveraging advanced deep learning methods, specifically Convolutional Neural Networks (CNNs), XIoT analyzes spectrogram images transformed from IoT network traffic data to detect subtle and complex attack patterns. Unlike traditional approaches, XIoT emphasizes interpretability by integrating explainable AI mechanisms, enabling cybersecurity analysts to understand and trust its predictions. By offering actionable insights into the factors driving its decision making, XIoT supports informed responses to cyber threats. Furthermore, the model’s architecture leverages the high-speed, low-latency characteristics of optical networks, ensuring the efficient processing of large-scale IoT data streams and supporting real-time detection in diverse IoT ecosystems. Comprehensive experiments on benchmark datasets, including KDD CUP99, UNSW NB15, and Bot-IoT, demonstrate XIoT’s exceptional accuracy rates of 99.34%, 99.61%, and 99.21%, respectively, significantly surpassing existing methods in both accuracy and interpretability. These results highlight XIoT’s capability to enhance IoT security by addressing real-world challenges, ensuring robust, scalable, and interpretable protection for IoT networks against sophisticated cyber threats.

The incredible advancements in data transmission technology have opened up more potentials for data security than ever before. Numerous methods for data protection have been developed during the previous decades, including steganography and cryptography. The security and integrity of medical data have emerged as major barriers for healthcare service systems as the Internet of Things has evolved dramatically in the healthcare business. Communication between two devices securely is a difficult problem. Numerous cryptographic algorithms are already available, including data encryption standard (DES), Rivest–Shamir–Adleman (RSA), and advanced encryption standard (AES). In this paper, we present a hybrid security model for the protection of diagnostic text data contained in medical photographs. The proposed model is built by combining a proposed hybrid encryption system with either a 2D Discrete Wavelet Transform 1 Level (2D-DWT-1L) or a 2D Discrete Wavelet Transform 2 Level (2D-DWT-2L) steganography technique. The suggested model encrypts secret data and hides them using 2D-DWT-3L. As text covers, color and grayscale images are employed. The suggested system’s performance was tested using PSNR, SSIM, MSE, and Correlation. Associated to state-of-the-art approaches, the proposed model masked personal patient data with high capacity, imperceptibility and minimum deterioration in the received stego-image. We use MATLAB to build the proposed mechanism, and measures such as throughput and execution time are used to assess performance.

In this article, we investigated the secrecy performance of a three-hop relay network system with Power Splitting (PS) and Energy Harvesting (EH). In the presence of one eavesdropper, a signal is transferred from source to destination with the help of a relay. The source signal transmits in full-duplex (FD) mood, jamming the relay transfer signals to the destination. The relay and source employ Time Switching (TS) and Energy Harvesting (EH) techniques to obtain the power from the power beacon. In this study, we compared the Secrecy Rate of two Cooperative Schemes, Amplify and Forward (AF) and Decode and Forward (DF), for both designed systems with the established EH and PS system. The Secrecy Rate was improved by 50.5% in the AF scheme and by 44.2% in the DF scheme between the relay and eavesdropper at 40 m apart for the proposed system in EH and PS. This simulation was performed using the Monto Carlo method in MATLAB.