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.

The swift progress of metasurface technology, enabling meticulous manipulation of the propagation environment, is anticipated to bring a transformative impact on sixth-generation (6G) wireless communications efficiency. Utilizing metasurface elements presents a promising opportunity for achieving passive scattering at sub-wavelength scales, facilitating intelligent radio settings’ advancement. Active Reconfigurable Intelligent Surfaces (ARIS) have gained significant interest in emergent metasurface technology. In contrast to passive RIS, which exhibits a certain degree of performance enhancement but encounters restrictions arising from the “double fading" phenomenon in the phase response, ARIS emerges as a highly promising alternative to counter such restrictions. This study provides a complete examination of ARIS, particularly emphasizing current improvements and its various uses within the context of 6G wireless networks. The review commences by laying a robust foundation in RIS technology, covering the various types and modes of RIS. Following this, we will explore the benefits and practical implementations of ARIS. Through a systematic examination, we categorize different approaches within ARIS-enabled use cases. These scenarios include optimizing the sum rate and signal-to-noise ratio, attaining maximum secrecy rate, energy minimization, and ensuring channel estimation. Additionally, we provide a summary and lessons learned along with a summary table for each category to describe, contrast, and evaluate the existing literature regarding setup, channel characteristics, methodologies, and objectives. We highlight the crucial role of ARIS in defining the landscape of wireless communications in the 6G era by outlining the open research problems in this emerging area and exploring the attractive future prospects.

The connected and autonomous vehicles (CAV) applications and services-based traffic make an extra burden on the already congested cellular networks. Offloading is envisioned as a promising solution to tackle cellular networks’ traffic explosion problem. Notably, vehicular traffic offloading leveraging different vehicular communication network (VCN) modes is one of the potential techniques to address the data traffic problem in cellular networks. This paper surveys the state-of-the-art literature for vehicular data offloading under a communication perspective, i.e., vehicle to vehicle (V2V), vehicle to roadside infrastructure (V2I), and vehicle to everything (V2X). First, we pinpoint the significant classification of vehicular data/traffic offloading techniques, considering whether data is to download or upload. Next, for better intuition of each data offloading’s category, we sub-classify the existing schemes based on their objectives. Then, the existing literature on vehicular data/traffic is elaborated, compared, and analyzed based on approaches, objectives, merits, demerits, etc. Finally, we highlight the open research challenges in this field and predict future research trends.

The Internet of Things (IoT) is undergoing significant advancements, driven by the emergence of Backscatter Communication (BC) and Artificial Intelligence (AI). BC is an energy-saving and cost-effective communication method where passive Backscatter Devices (BD) communicate by modulating ambient Radio-Frequency (RF) carriers. AI has the potential to transform our way of communicating and interacting and represents a powerful tool for enabling the next generation of IoT devices and networks. By integrating AI with BC, we can create new opportunities for energy-efficient and low-cost communication and open the door to a range of innovative applications that were previously not possible. This paper brings these two technologies together to investigate the current state of AI-powered BC. We begin with an introduction to BC and an overview of the AI algorithms employed in BC. Then, we delve into the recent advances in AI-based BC, covering key areas such as backscatter signal detection, channel estimation, and jammer control to ensure security, mitigate interference, and improve throughput and latency. We also explore the exciting frontiers of AI in BC using B5G/6G technologies, including backscatter-assisted relay and cognitive communication networks, backscatter-assisted MEC networks, and BC with Reconfigurable Intelligent Surfaces (RIS), UAV, and vehicular networks. Finally, in the discussion section, we summarize the solutions, provide lessons learned and challenges, and present new research opportunities in AI-powered BC. This survey provides a comprehensive overview of the potential of AI-powered BC and its insightful impact on the future of IoT.

Uncrewed aerial vehicles (UAVs) have become essential in advancing multiaccess edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RISs) with UAV-based MEC systems further elevates these capabilities, delivering significant gains in computational power, energy efficiency (EE), and physical-layer security (PLS). However, managing the complexity of RIS within UAV networks requires sophisticated optimization strategies. This survey offers a comprehensive analysis of the fundamentals of RIS, UAVs, and MEC, followed by an in-depth examination of RIS configurations in UAV-based MEC systems, including static, dynamic, and hybrid models. We evaluate the benefits and challenges of RIS integration, such as improved communication, enhanced computational efficiency, optimized energy use, better task management, and strengthened security. In addition, the survey explores the latest advancements in RIS-assisted UAVs for MEC, focusing on boosting computational capacity, minimizing delay, maximizing EE, and enhancing security. To provide a thorough exploration of these topics, detailed summary tables are included, offering a comparative analysis of methodologies, performance metrics, and scenarios from recent studies. Furthermore, the survey presents key lessons learned from current research and identifies future research directions crucial for fully realizing the potential of RIS-enhanced UAV-based MEC systems in next-generation networks.

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.