The recent development of metasurfaces, which may enable several use cases by modifying the propagation environment, is anticipated to substantially affect the performance of sixth-generation (6G) wireless communications. Metasurface elements can produce passive subwavelength scattering to enable a smart radio environment. Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which refers to reconfigurable intelligent surfaces (RISs) that can transmit and reflect concurrently (STAR), is gaining popularity. In contrast to the widely studied RIS, which can only reflect the wireless signal and serve users on the same side as the transmitter, the STAR-RIS can reflect and refract (transmit), enabling 360° wireless coverage, thus serving users on both sides of the transmitter. This article presents a comprehensive review of the STAR-RIS, focusing on the most recent schemes for diverse use cases in 6G networks, resource allocation, and performance evaluation. We begin by laying the foundation for RIS (passive, active, and STAR-RIS), and then discuss the STAR-RIS protocols, advantages, and applications. In addition, we categorize the approaches within the domain of use scenarios, which include increasing coverage, enhancing physical-layer security (PLS), maximizing sum rate, improving energy efficiency (EE), and reducing interference. Next, we will discuss the various strategies for resource allocation and measures for performance evaluation. We aimed to elaborate, compare, and evaluate the literature regarding setup, channel characteristics, methodology, and objectives. In conclusion, we examine this field’s open research problems and potential future prospects.

Communication at terahertz (THz) frequencies has emerged as a potential answer to the challenge of providing massive capacity and ultra-high data speeds for 6G wireless networks. However, THz waves are vulnerable to obstruction because of their attenuation during propagation and poor diffraction. The development of reconfigurable intelligent surfaces (RIS) technology has shown promise in addressing these issues. The RIS enables the development of smart radio settings with enhanced spectrum coverage and efficiency by regulating the phase shifts of passively reflecting elements. Amidst the vast potential of RIS-based THz communication, a noticeable void exists in comprehensive research, calling for a systematic survey to shed light on its principles, use cases, performance evaluation, challenges, and future trajectories. This review investigates the exciting field of RIS-based THz communication in fifth-generation (B5G), sixth-generation (6G), and beyond. Our study provides a comprehensive overview of this emerging technology, covering a variety of use cases, including CE, coverage, security, sum rate, and energy efficiency. We also investigate resource allocation strategies for THz communication using RIS. Additionally, the performance of RIS-based THz systems with multiple-input and multiple-output (MIMO) and massive MIMO and unmanned aerial vehicle (UAV) technologies is analyzed. By analyzing the existing literature, we illuminate the various approaches, methodologies, and future developments in this field. We also emphasize open issues and research opportunities in THz communication based on RIS. This survey is a valuable resource, contributing to understanding the emerging paradigms and research frontiers of RIS-based THz communication.

This survey explores the transformative role of reconfigurable intelligent surface (RIS) technologies in vehicular communication systems. It begins by introducing key RIS concepts and classifying them into passive, active, and hybrid types, with particular focus on beyond diagonal RIS (BD-RIS) and its various architectural designs. The study then delves into the evolution of vehicular communication technologies, followed by channel modeling and strategies to ensure long-term system adaptability. The survey further investigates key technological advancements, such as advanced channel modeling and estimation techniques, strategies for optimizing network sum-rate and energy efficiency (EE), and enhancing security within vehicular networks through RIS. Additionally, it examines the integration of RIS with vehicular edge computing (VEC) frameworks, its potential in unmanned aerial vehicle (UAV) supported networks, and its application in visible light communication (VLC) systems. The analysis also considers the synergy between RIS and mmWave technologies. Then, a comprehensive performance assessment is conducted to evaluate the impact of these advancements in RIS-based vehicular networks. The aim is to thoroughly elaborate, compare, and evaluate the literature concerning network scenarios, RIS details, methodologies, and optimization objectives. Conclusively, the research highlights key findings and charts future research directions, pinpointing imminent technical challenges and emerging areas for further exploration in the rapidly evolving landscape of vehicular communications.

The security of wireless communications is increasingly threatened by eavesdropping and jamming attacks. This paper proposes a novel framework for enhancing secure transmission using hybrid active-passive Reconfigurable Intelligent Surfaces (RIS). By combining active and passive elements, the hybrid RIS can dynamically adjust the amplitude and phase of reflected signals, providing a robust defense against eavesdropping and jamming. The key challenge lies in optimizing the beamforming at the base station (BS) and the hybrid RIS configuration in real time. To tackle this, we employ a Deep Reinforcement Learning (DRL) approach using the Deep Deterministic Policy Gradient (DDPG) algorithm, enabling efficient and dynamic optimization. Simulation results demonstrate that the proposed DRL-based method significantly improves the secrecy rate compared to conventional passive RIS and benchmark methods. Our results indicate that the system can achieve substantial security gains even with a limited number of active RIS elements, making it a viable solution for next-generation wireless 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.