Cell-free massive multiple-input–multiple-output (CF-mMIMO) has emerged as a key architectural candidate for sixth-generation (6G) wireless networks, in which many distributed access points cooperate to serve users without cell boundaries. When combined with integrated sensing and communication (ISAC), this infrastructure evolves from a pure connectivity layer into a spatially distributed sensing–communication fabric capable of high-rate data delivery and fine-grained environmental perception. This survey provides a structured overview of CF-mMIMO– ISAC systems. We first revisit the fundamentals of CF-mMIMO and ISAC and clarify their synergies and inherent tensions. We then synthesize recent progress along several core design axes: joint maximization of communication sum-rate and sensing signal-to-noise ratio (SNR); physical-layer security and privacy-aware sensing; energy-efficient operation with stringent latency and age-of-information requirements; performance evaluation and scalability under realistic hardware and fronthaul constraints; and integration with enabling technologies such as reconfigurable intelligent surfaces (RISs), movable antennas, orthogonal time–frequency space (OTFS) modulation, and unmanned aerial vehicle (UAV) platforms. Across these themes, we compare optimization-based and learning-based methods, emphasizing how they reshape the rate–sensing trade-off, how sensitive they are to channel state information (CSI) assumptions, and how system-level coordination influences scalability. Finally, we distill cross-cutting lessons and outline open problems in distributed joint sensing–communication design. The survey is intended as both a technical reference and a roadmap for designing CF-mMIMO ISAC frameworks in 6G and beyond.

The integration of reconfigurable intelligent surfaces (RIS) into unmanned aerial vehicle (UAV) networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services—spanning logistics to smart surveillance—their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, blackuce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced energy efficiency (EE)-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, quality of service (QoS) guarantees, secrecy rate improvement, latency blackuction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), vehicle-to-everything (V2X) communication, and wireless power transfer (WPT). Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.

Unmanned aerial vehicles (UAVs) have become essential in advancing multi-access edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RIS) 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.

Developing wireless communication technologies is an ongoing process to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. Using non-orthogonal multiple access (NOMA) and backscatter communication (BC) has surfaced as an advantageous approach for enhancing energy efficiency (EE), maximizing sum rates, ensuring security, and optimizing resource allocation. NOMA permits multiple users to share time and frequency resources even without the requirement of antenna arrays, whereas BC employs ambient RF signals for low-power communication. By integrating the advantages of NOMA and BC, NOMA-based BC provides a solution for future energy-efficient and low-power networks. Despite its potential, there is a lack of a comprehensive overview of NOMA-BC, necessitating a systematic survey that covers its principles, applications, challenges, and future directions. This survey aims to bridge the gap by exploring NOMA-BC within B5G and 6G networks. We delve into its technical aspects, performance optimization techniques, and real-world applications to enhance understanding and knowledge. First, we cover topics such as enhancing EE, maximizing the sum rates, ensuring security, and analyzing performance. Our primary goal is to provide researchers and practitioners with valuable insights that enable them to grasp the capabilities and benefits of NOMA-BC. To achieve this, we comprehensively analyze the performance of various schemes by presenting detailed summary tables. These analyses cover a range of scenarios, methods, and objectives, focusing on emerging B5G technologies such as reconfigurable intelligent surfaces (RIS), visible light communication (VLC), and unmanned aerial vehicle (UAV) communication. By examining NOMA-BC’s effectiveness within these contexts, we aim to provide a holistic view of its potential and applicability in diverse technological domains. Moreover, our survey identifies and discusses open research challenges and proposes future directions to guide researchers toward unexplored areas and facilitate advancements in NOMA-BC.

In order to realize an intelligent IoT system, various resource-constrained IoT devices today play an important role in data transmission and processing. However, the novel data protection requirements appear since constrained resources like computation power or energy of IoT devices cannot support classic data protection methods like encryption algorithms. To tackle the above issues, we propose a lightweight Blockchain-Based Trust Management (BBTM) scheme in Resource-constrained Intelligent IoT Systems. The BBTM initially establishes a genesis block at the onset of network operation. Messages transmitted between nodes are recorded as transactions, forming blocks that are linked to the blockchain. As the network operates, nodes’ behaviors are monitored, and their trust values are updated in real-time. When two nodes encounter each other, a credibility formula based method is used to determine trustworthiness based on historical behaviors. Nodes that pass the trust verification utilize asymmetric encryption for message transmission. The primary aim of this scheme is to develop a lightweight, blockchain based secure communication module for Intelligent IoT Systems. It encrypts information using asymmetric cryptography and manages trust during node interactions to ensure security and privacy by blocking malicious or selfish nodes. Compared with the existing solutions, the simulation results show that this scheme can effectively block the attacks from malicious nodes without overly consuming network resources. At the same time, trust verification and encrypted information transmission are effectively managed in resource-constrained intelligent IoT systems.

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 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 (BDs) 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 article 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 (RISs), 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.

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.

Summary Mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough. In this paper, we introduce two key metrics, namely, social relationship and social activity. The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction‐based social‐aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete‐time semi‐Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state‐of‐the‐art algorithms.

Vehicular cloud network (VCN) is deemed as the most promising platform for providing transportation safety, road optimization, and valued‐added application services. Because VCN is of distinguishing feature with super‐large scale and unstable communication, it is a challenging task to study efficient authentication scheme for VCN without losing security and conditional privacy‐preserving. To meet the challenge, a new efficient message authentication scheme is proposed in this paper. A batch message verification and signature aggregation are included in the proposed scheme to improve the authentication efficiency and decrease the communication cost. Compared with the similar conditional privacy‐preserving authentication schemes, the proposed scheme has superior performance in computation and communication cost. Simulation analysis further proves that the proposed scheme has better advantages in reducing the verification loss rate and message delay in the application of VCN.

With the advent of social networks and cloud computing, the amount of multimedia data produced and communicated within social networks is rapidly increasing. In the meantime, social networking platforms based on cloud computing have made multimedia big data sharing in social networks easier and more efficient. The growth of social multimedia, as demonstrated by social networking sites such as Facebook and YouTube, combined with advances in multimedia content analysis, underscores potential risks for malicious use, such as illegal copying, piracy, plagiarism, and misappropriation. Therefore, secure multimedia sharing and traitor tracing issues have become critical and urgent in social networks. In this article, a joint fingerprinting and encryption (JFE) scheme based on tree-structured Haar wavelet transform (TSHWT) is proposed with the purpose of protecting media distribution in social network environments. The motivation is to map hierarchical community structure of social networks into a tree structure of Haar wavelet transform for fingerprinting and encryption. First, fingerprint code is produced using social network analysis (SNA). Second, the content is decomposed based on the structure of fingerprint code by the TSHWT. Then, the content is fingerprinted and encrypted in the TSHWT domain. Finally, the encrypted contents are delivered to users via hybrid multicast-unicast. The proposed method, to the best of our knowledge, is the first scalable JFE method for fingerprinting and encryption in the TSHWT domain using SNA. The use of fingerprinting along with encryption using SNA not only provides a double layer of protection for social multimedia sharing in social network environment but also avoids big data superposition effect. Theory analysis and experimental results show the effectiveness of the proposed JFE scheme.