Recently, reconfigurable/large intelligent surface (RIS/LIS) has been emerged as an essential key enabler for 6G communication scenarios. The surface can be programmed by software for forming and directing the electromagnetic (EM) wave in any desired direction. Furthermore, cooperative RISs can be dynamically aligned for routing the EM beams through some short line-of-sight (LOS) paths. This gives rise to the birth of a new physical layer networking (PHY0) concept. However, it is still limited to indoor scenarios where the whole walls and ceilings are coated with metasurface material. The routing hops are limited to short distances up to 10 meters per hop. In this paper, an extreme scenario of EM beam routing is explored under the restrictions imposed by the outdoor environment (small number of limited RISs). Specifically, the focusing functionality of RIS is employed. The inherent trade-off between the achieved beam waist and the focal length is leveraged for maximizing the routing hop. So, the placement of the focal plane will be adapted for confining the routed beam between RIS surfaces as being as a guided wave. Main equations, governing near-field focused RIS, are investigated for maximizing focusing gain. The routing problem is formulated as problem of finding a single-source shortest path (SSSP). Simulations are performed on outdoor scenarios under a size-limited RISs network. The impact of the relative orientation between RIS nodes and surface aperture is considered in our simulations. Compared to short indoor hops (limited to a few meters), the maximum hop between RIS nodes is enlarged up to $47\;m$ and $94\;m$ for RIS areas of $2\;m^{2}$ and $4\;m^{2}$ , respectively under optimal relative orientation.

Reconfigurable intelligent surface (RIS) is regarded as one of the main enablers in the context of 6G wireless communications. It converts the conventionally uncontrolled wireless channel into a programmed channel. The current interest in these surfaces is extended from just exploring the capabilities of single RIS to further exploring opportunities of employing multiple RIS cooperatively for enhancing network coverage and capacity. Unfortunately, almost all current work of multiple RIS-assisted networks are limited to either cascade or parallel topologies without regarding the most proper distribution according to the corresponding channel qualities and mutual orientations. So, in this paper, we are aiming to gain both benefits of cascaded and parallel topologies through a hybrid RIS networking structure. While cascade topology minimizes path loss and enhances multiplicative gain, the parallel topology is exploited for enriching scattering signatures in the interested region (cluster). First, cascaded grouping is resolved based on consecutive channel qualities through optimal routing technique. Then, a joint active and passive beamforming (JAPBF) problem is assumed over the grouped parallel routes. The spatial diversity problem is formulated as fractional programming (FP) optimization problem. The superiority of the proposed hybrid network is demonstrated through the performed simulation results represented in maximizing the overall achievable sum rate and exploring the sensitivity to location shift due to receiver mobility.

Reconfigurable intelligent surface (RIS) is a groundbreaking technology that has a significant potential for sixth generation (6G) networks. Its unique capability to control wireless environments makes it an attractive option. However, the spatial diversity increased by assisting users with all deployed RISs, this investigation has two drawbacks the high complexity design, and the received signals by the far RISs are severely attenuated. Therefore, we propose a RIS selection strategy to select the proper RISs as a pre-stage before the joint beamforming between the base station (BS) and RISs to reduce the high complexity of joint beamforming optimization. Furthermore, the joint active and passive beamforming problem based on the selection is formulated. Hence, achieving spatial diversity by examining cooperation between passive beamforming of multi-hop RIS, leads to a challenging problem. To tackle this issue, we design an algorithm for the RIS selection scheme. Also, to relax the non-convexity of the proposed problem, we decouple the problem into solvable subproblems by utilizing the fractional programming (FP) and quadratic transform (QT) optimization methods. Simulation results have demonstrated through different user locations the effectiveness of the selection strategy in performance enhancement by 30% in the sum rate, besides an obvious reduction in the complexity cost than other techniques.

Lung cancer detection using deep learning significantly improves patient outcomes by enabling timely treatment and diagnosis that crucial for survival. Deep learning techniques introduce high efficiency by allowing the analysis of complex medical data, making them valuable in enhancing diagnostic precision. This study presents a proposed system leveraging artificial intelligence (AI) algorithms for the early detection of lung cancer, addressing the significant global mortality associated with the disease. In addition, the system utilizes nail image analysis for curvature assessment and user-provided data such as smoking history and genetic predisposition. A complementary dataset was created to enable rapid and accurate detection. Advanced AI models, Explainable AI (XAI) using the SHAP model, were employed to extract important details from the data, enhancing detection accuracy. The system also identifies additional diagnostic markers, such as clubbing nails, a key indicator of lung cancer. The results show the superiority of the proposed system with a detection accuracy of 96.0% and a loss rate of 0.07.

With the exponential growth of electronically transmitted and stored data, ensuring data privacy and security has become a fundamental challenge for organizations and enterprises. Traditional encryption methods have limitations, such as vulnerability to advanced attacks and high computational complexity, that lead to the exploration of complementary strategies like deception techniques for enhanced protection. These methods aim to mislead unauthorized users by presenting protected data as if it were authentic, but the attack resilience is still insufficient. Multi-stage deception (MSD) methods leverage multiple deception strategies, such as complement, swapping, and stack reversal, to improve data protection levels and resistance against decryption attempts. Combining these techniques addresses gaps in single-stage approaches and offers a more robust defense. The proposed MSD method incorporates a classification of encryption and deception techniques and introduces a novel evaluation approach targeting critical performance factors. A tailored pseudocode algorithm is designed to optimize deception for various attribute types, validated through simulations. Simulation results reveal that the MSD method achieves a 100% value change in the first stage and 92% in the second stage, with an overall accuracy exceeding 95% . These findings demonstrate the method’s effectiveness in elevating data protection levels while maintaining low computational complexity. The study highlights the potential of multi-stage deception as a powerful tool for safeguarding sensitive information, achieving superior performance in data security. By offering a scalable and adaptable framework, the MSD method addresses emerging challenges in data protection while setting the stage for further advancements.

Reconfigurable Intelligent Surfaces (RIS) have emerged as a pivotal enabling technology with substantial potential for sixth-generation (6G) wireless networks. Due to their unique ability to manipulate wireless communication environments, RIS has garnered significant research interest in recent years. However, as multi-RIS cooperation, whether in parallel or cascaded configurations, is increasingly employed to improve signal guidance between transmitters and receivers, two primary challenges arise: mitigating path loss over wide area RIS deployments and addressing the impact of relative orientation angles between surfaces. To address these challenges, this letter introduces a novel approach that integrates multiple RIS units into a single structure, termed stacked RIS. This design aims to overcome the limitations associated with separately deployed RIS units. The sum rate is analyzed by optimizing the incorporation of active and combined passive beamforming for the base station and stacked RIS compared to multi-separated RIS with different configurations. Simulation results demonstrate the superior performance of stacked RIS over its multi-separated counterpart, highlighting its ability to reduce the required surface area while mitigating deployment constraints and minimizing the effects of relative orientation angles between surfaces.