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Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.

Artificial intelligence generated content (AIGC) has emerged as a promising technology to improve the efficiency, quality, diversity and flexibility of the content creation process by adopting a variety of generative AI models. Deploying AIGC services in wireless networks has been expected to enhance the user experience. However, the existing AIGC service provision suffers from several limitations, e.g., the centralized training in the pre-training, fine-tuning, and inference processes, especially their implementations in wireless networks with privacy preservation. Federated learning (FL), as a collaborative learning framework where the model training is distributed to cooperative data owners without the need for data sharing, can be leveraged to simultaneously improve learning efficiency and achieve privacy protection for AIGC. To this end, we present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content. Furthermore, we conduct a case study of FL-aided AIGC fine-tuning by using the state-of-the-art AIGC model, i.e., stable diffusion model. Numerical results show that our scheme achieves advantages in effectively reducing the communication cost and training latency, and providing privacy protection. Finally, we highlight several major research directions and open issues for the convergence of FL and AIGC.

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep $Q$ Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics.

As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this article, we present the concept of mobile-edge AI-Generated Everything (AIGX). Specifically, we first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we conduct a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance.

As indoor applications grow in diversity, wireless sensing, vital in areas like localization and activity recognition, is attracting renewed interest. Indoor wireless sensing relies on signal processing, particularly channel state information (CSI) based signal parameter estimation. Nonetheless, regarding reflected signals induced by dynamic human targets, no satisfactory algorithm yet exists for estimating the acceleration of dynamic path length change (DPLC), which is crucial for various sensing tasks in this context. Hence, this paper proposes DP-AcE, a CSI based DPLC acceleration estimation algorithm. We first model the relationship between the phase difference of adjacent CSI measurements and the DPLC’s acceleration. Unlike existing works assuming constant speed, DP-AcE considers both speed and acceleration, yielding a more accurate and objective representation. Using this relationship, an algorithm combining scaling with the Fourier transform is proposed to realize acceleration estimation. We evaluate DP-AcE via the acceleration estimation and acceleration-based fall detection with the collected CSI. Experimental results reveal that, using distance as the metric, DP-AcE achieves a median acceleration estimation percentage error of 4.38%. Furthermore, in multi-target scenarios, the fall detection achieves an average true positive rate of 89.56% and a false positive rate of 11.78%, demonstrating its importance in enhancing indoor wireless sensing capabilities.

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services.

Federated learning has emerged as a promising approach for fault diagnosis, as its ability to learn from decentralized data while preserving client privacy for industry. Yet, it also brings the problem of nonidentically and independently distributed (Non-IID) data, which can result in model convergence delay and performance degradation. Recent research aims to alleviate the problem caused by cross-domain without considering by cross-location. However, it is common in industrial production to have devices across different monitoring locations. Furthermore, experimental results indicate that the diagnostic models' performance of the latest techniques is significantly affected. To address the cross-location Non-IID data problem, we propose DecFFD, a personalized federated fault diagnosis framework that decouples global and personalized features. In DecFFD, we design a reconstructor for each client that acts as a supervisor and decoupler to disentangle global and personalized features. We then present a client alignment algorithm to eliminate the differences in global features among clients. In addition, we provide a theoretical analysis of fairness and generalization capability, offering a theoretical guarantee for model convergence. Finally, extensive experiments are conducted on two real-world datasets. Experimental results show that the accuracy of DecFFD outperforms the accuracy that of the state-of-the-art approach by 14.67% and converges at a faster rate.

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Numerical results demonstrate the superiority of the proposed scheme. Finally, we suggest several open directions worth investigating for the future popularity of GIoT.

Driven by the continuous growth of network demands, Large-scale Deterministic Network (LDN) has been proposed to deliver deterministic services with improved latency and jitter, zero packet loss, configurable bandwidth, and high reliability across a broad geographical area. LDN serves as a bridge for businesses to connect and share information across different sectors with high-quality and customized network services. However, the conveyance of massive flows with stringent demands in a large topology poses technical challenges to LDN architecture, scheduling mechanisms, and operation methodologies. To shed light on the possible avenues for these issues, this article provides a comprehensive survey of LDN architecture and enabling technologies. The study begins with an overview of typical LDN architectures, including definitions, standards, and mechanisms, followed by an examination of the key technologies that support the LDN’s performance characteristics and implementation processes. Additionally, a case study of LDN scheduling is presented illustrating its application in heavy-traffic scenarios. Finally, this article outlines the open challenges and future directions in this field, highlighting areas of potential growth and development.

As the Metaverse continues to grow, the need for efficient communication and intelligent content generation becomes increasingly important. Semantic communication focuses on conveying meaning and understanding from user inputs, while AI-Generated Content utilizes artificial intelligence to create digital content and experiences. Integrated Semantic Communication and AI-Generated Content (ISGC) has attracted a lot of attentions recently, which transfers semantic information from user inputs instead of raw data, generates digital content, and renders graphics for Metaverse. In this paper, we introduce a unified framework that captures ISGC’s two primary benefits: integration gain for optimized resource allocation and coordination gain for goal-oriented high-quality content generation to improve immersion from both communication and content perspectives. We also classify existing ISGC solutions, analyze the major components of ISGC, and present several use cases. We then construct a case study based on the diffusion model to identify a near-optimal resource allocation strategy for performing semantic extraction, content generation, and graphic rendering in the Metaverse. Finally, we discuss several open research issues, encouraging further exploring the potential of ISGC and its related applications in the Metaverse.

Recognizing the tremendous improvements that the integration of generative artificial intelligence (AI) can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks.

Semantic communication has emerged as a promising technique that can integrate the meaning and context of information into a communication process. It goes beyond raw data transmission and enables a sender and a receiver to transmit and recover semantic messages through their respective knowledge bases. However, it is challenging to achieve secure semantic communication due to complicated security and privacy threats in semantic communication. In this article, we first introduce the background and architecture of semantic communication. Then, we sort out multiple potential security threats existing in each step of semantic communication. Following that, we summarize the requirements for secure semantic communication and provide possible defenses for each type of attacks. We also present a case study to illustrate the training process of a reliable semantic encoder/decoder. Finally, we discuss open issues and future directions in this research field.

Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://github.com/HongyangDu/DistributedDiffusion

Advancements in artificial intelligence (AI), and the surge in diverse training data, have facilitated AI generated content (AIGC). Despite high efficiency, the inherent instability of AI models poses challenges in creating user-specific content, especially when creating an avatar for a user. To address this issue, this article integrates wireless perception (WP) with AIGC and introduces WP-AIGC, a unified framework that leverages a user skeleton obtained by WP to guide AIGC, thereby generating the avatar that aligns with the user's actual posture. Specifically, WP-AIGC first employs a novel multi-scale perception technology to sense posture in the physical world and construct the user skeleton. Then, the skeleton and the user's requirements are conveyed to the AIGC, thereby guiding the creation of the avatar. Furthermore, WP-AIGC can adjust the computing resources allocated to perception and AIGC based on user feedback, thereby optimizing the service. Experimental results verify the effectiveness of the service. with limited computing resources, WP-AIGC achieves optimal QoS of 3.75 when four links are involved in perception.

With the phenomenal success of diffusion models and ChatGPT, deep generation models (DGMs) have been experiencing explosive growth. Not limited to content generation, DGMs are also widely adopted in Internet of Things, Metaverse, and digital twin, due to their outstanding ability to represent complex patterns and generate realistic samples. In this article, we explore the applications of DGMs in a crucial task, that is, improving the efficiency of wireless network management. Specifically, we first overview the generative AI, as well as three representative DGMs. Then, we propose a DGM-empowered framework for wireless network management, in which we elaborate on the issues of the conventional network management approaches, why DGMs can address them efficiently, and the step-by-step workflow for applying DGMs in managing wireless networks. Moreover, we conduct a case study on network economics, using the state-of-the-art DGM model, that is, diffusion model, to generate effective contracts for incentivizing the mobile AI-generated content (AIGC) services. Last but not least, we discuss important open directions for further research.

Web 3.0 enables user-generated contents and user-selected authorities. With the help of decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchainse-mantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining on-chain and off-chain service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust settines according to varied semantic demands.

Unmanned aerial vehicles (UAVs) have been widely used in military and civilian fields by carrying out intelligent applications with deep learning technologies, such as battlefield search and rescue; intelligence, surveillance, and reconnaissance (ISR); and smart city monitoring. Due to deeper and more complex models of deep neural networks (DNNs) and limited on-board resources in UAVs, most existing works either rely on cloud-based intelligence by transmitting original data to remote powerful cloud servers and running DNNs inference therein, or involve edge intelligence by executing lightweight DNN models on-board. Unfortunately, the former is faced with unacceptable long transmission latency and possible network instability over air-to-ground links, while the latter is restricted to relatively low accuracy via lightweight models. Although a few recent works propose to collaboratively run complex DNNs inference within a swarm of UAVs to achieve both high accuracy and low latency, they seldom consider the strong confrontation environment of the battle-field, the unreliable air-to-air links among UAVs as well as likely hardware/software breakdowns in UAV airborne computers, which could result in the failure of collaborative inference. This article proposes a novel architecture based on an observation, orientation, decision, and action (OODA) loop, called elastic collaborative edge intelligence (eCoEI), which could fully utilize the resources of computation and storage in a UAV swarm, and enable the swarm to collaboratively perform DNN inference tasks in an elastic manner in the face of any lost links. This article also designs a prototype system of the proposed eCoEI and conducts a proof-of-concept evaluation to validate its feasibility as well as effectiveness. Experimental results show that the eCoEI has an outstanding ability to deal with single point of failure and network fluctuations within a UAV swarm for collaborative edge intelligence. Finally, the main technical challenges are pointed out in the eCoEI, and an outlook on future research directions is provided.

Metaverse can obscure the boundary between the physical and virtual worlds. Specifically, for the human-centric Metaverse in vehicular networks, i.e., the vehicular Metaverse, vehicles are no longer isolated physical spaces but interfaces that extend the virtual worlds to the physical world. Accessing the human-centric Metaverse via autonomous vehicles (AVs), drivers and passengers can immerse in and interact with 3D virtual objects overlaying views of streets on head-up displays (HUD) via augmented reality (AR). The seamless, immersive, and interactive experience rather relies on real-time multi-dimensional data synchronization between physical entities, i.e., AVs, and virtual entities, i.e., Metaverse billboard providers (MBPs). However, mechanisms to allocate and match synchronizing AV and MBP pairs to roadside units (RSUs) in a synchronization service market, which consists of the physical and virtual submarkets, are vulnerable to adverse selection. In this paper, we propose an enhanced second-score auction-based mechanism, named EPViSA, to allocate physical and virtual entities in the synchronization service market of the vehicular Metaverse. The EPViSA mechanism can determine synchronizing AV and MBP pairs simultaneously while protecting participants from adverse selection and thus achieving high total social welfare. We propose a synchronization scoring rule to eliminate the external effects from the virtual submarkets. Then, a price scaling factor is introduced to enhance the allocation of synchronizing virtual entities in the virtual submarkets. Finally, rigorous analysis and extensive experiments demonstrate EPViSA can achieve at least 96% of the social welfare compared to the omniscient benchmark while ensuring strategy-proof and adverse selection free through a simulation testbed.

As the next-generation wireless communication system, sixth-generation (6G) technologies are emerging, enabling various mobile edge networks that can revolutionize wireless communication and connectivity. By integrating generative artificial intelligence (GAI) with mobile edge networks, generative mobile edge networks possess immense potential to enhance the intelligence and efficiency of wireless communication networks. In this article, we propose the concept of generative mobile edge networks and overview widely adopted GAI technologies and their applications in mobile edge networks. We then discuss the potential challenges faced by generative mobile edge networks in resource-constrained scenarios. To address these challenges, we develop a universal resource-efficient generative incentive mechanism framework, in which we design resource-efficient methods for network overhead reduction, formulate appropriate incentive mechanisms for the resource allocation problem, and utilize generative diffusion models (GDMs) to find the optimal incentive mechanism solutions. Furthermore, we conduct a case study on resource-constrained mobile edge networks, employing model partitioning for efficient AI task offloading, and proposing a GDM-based Stackelberg model to motivate edge devices to contribute computing resources for mobile edge intelligence. Finally, we propose several open directions that could contribute to the future popularity of generative mobile edge networks.

The prevalence of Artificial Intelligence and Multi-access Mobile Edge Computing (MMEC) technologies has laid a solid foundation for next-generation Internet of Things (IoT) applications, e.g., industrial automation and smart healthcare fields. However, the explosive data and ubiquitous services significantly exacerbate the consumption and unreliability of constrained edge resources. In this paper, we investigate a joint resource orchestration problem for IoT-MMEC networks with different service performances. We first develop an identifier space mapping model to represent the matching relationship between access attributes and space resources, which respectively denote the computing task description and the set of allocated resources. To obtain an optimal resource partition policy for dependable and low-budget auxiliary calculation, we formulate a mixed-integer nonlinear programming problem. Then, we devise an identifier-driven resource orchestration scheme, which decouples the problem into computation offloading and resource allocation subproblems. Based on the expected utility function theory and access attributes, we apply a mixed deep neural network inference model to infer the offloading location, for realizing the resource supply-demand balance. To derive the optimal resource allocation scheme, we exploit the quantum genetic algorithm and multi-path offloading factor, which can explore a large search space to find potential solutions while exploiting the best solutions. Finally, the experimental simulations validate our theoretical analysis, and the results indicate that the proposed scheme can achieve lower consumption and enhance offloading reliability.

With the drive to create a decentralized digital economy, Web 3.0 has become a cornerstone of digital transformation, developed on the basis of computing power networks, distributed data storage, and blockchain. With the rapid realization of quantum devices, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with the advantages of quantum computing and communication. Therefore, in this paper, we introduce a quantum blockchain-driven Web 3.0 framework that provides information-theoretic security for decentralized data transferring and payment transactions. First, we present the framework of quantum blockchain-driven Web 3.0 with futureproof security during the transmission of data and transaction information. Next, we discuss the potential applications and challenges of implementing quantum blockchain in Web 3.0. Finally, we describe a use case for quantum non-fungible tokens (NFTs) and propose a quantum deep learning-based optimal auction for NFT trading to maximize the achievable revenue for sufficient liquidity in Web 3.0. In this way, the proposed framework can achieve proven security and sustainability for the next-generation decentralized digital society.

Aerial acoustic communication in a peer-to-peer manner attracts much attention recently thanks to its ubiquitous device support. These benefits have led Google's nearby platform to adopt this communication paradigm to establish copresence between nearby devices. However, state-of-the-art research cannot reliably work under dynamic channels and long communication distance. In particular, current proposals have to trade-off between communication distance and transmission rate, when the Bit-Error-Rate (BER) is within a certain threshold. Meanwhile, the performance of existing works is vulnerable to channel dynamics such as the near-far problem, device diversity, and multipath effects. To this end, we propose HiRA, a reliable High thRoughput aerial Acoustic communication system. In HiRA, we design a loose orthogonality condition for chirp carriers, which allows us to overlap multiple loose orthogonal carriers in a single time slot or data symbol, thus notably boosting the transmission rate. To make it reliable, we design a range and Doppler aware beacon detection algorithm and rate adaptation mechanism. Evaluation results show that HiRA achieves $20\times$ improvements over existing methods in data rate: a maximum of 500 bps data rate can be achieved with a BER of 0.64% at 11.5 m. And it can reliably work under dynamic channels but still keep BER under a desired threshold.

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation, which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units (RSU) or unmanned aerial vehicles (UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning (MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization (MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers (e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Federatedlearning (FL) is an emerging distributed learning paradigm widely used in vehicular networks, where vehicles are enabled to train the deep model for the server while keeping private data locally. However, the annotation of private data by vehicular users is very difficult since the high costs and professional needs, and one solution is that roadside infrastructures could provide label mapping to the data according to the geographical coordinates. In this scenario where vehicles and roadside infrastructures hold the data and labels, respectively, traditional FL is not applicable since it needs each participant to have both data and labels. In this article, we propose a federated split learning (FSL) paradigm that split the deep model into two submodels which are trained separately in the vehicles and the roadside infrastructures. The vehicles and the roadside infrastructures exchange the intermediate data (i.e., smashed data and cut layer gradients) in training local submodels and upload the local gradients to the global server for aggregation into the global model. Specifically, we first adopt three types of privacy attacks to demonstrate that attackers could recover the private data and labels according to the shared intermediate data and uploaded local gradients. We then propose a differential privacy (DP)-based defense mechanism to defend the privacy attacks by perturbing the intermediate data. Furthermore, we design a contract-based incentive mechanism that encourages vehicles and roadside infrastructures to enhance training performance by adjusting their privacy strategies. The simulation results illustrated that the proposed defense mechanism can remarkably emasculate the performance of attacks and the proposed incentive mechanism is efficient in the FSL paradigm for vehicular networks.

Image transmission over wireless communications can be used in a variety of applications, such as smart cities, surveillance systems, and Metaverse construction. In this paper, we propose a task-oriented semantic information transmission (SIT) framework with rate-splitting multiple access (RSMA) for image transmission. As such, only the semantic information of interest is transmitted to each user, and RSMA is adopted to improve transmission efficiency. We also design the quality of experience (QoE) for the framework as a performance metric, which can be used for transmission-parameter optimization. Specifically, we first optimize power allocation with the top- $N$ common message selection strategy. To further improve system performance, we jointly optimize power allocation and common message selection. Simulation results show that the proposed task-oriented SIT framework with RSMA outperforms the space-division multiple access (SDMA)-based benchmark, which reflects the effectiveness of the proposed framework. Furthermore, the results show that optimizing power allocation can improve performance significantly as compared with fixing power allocation, and the joint optimization of power allocation and common message selection has an obvious performance gain over optimizing only power allocation, which demonstrates the effectiveness of the designed optimization algorithms.

The rapid development of Artificial Intelligence-Generated Content (AIGC) has brought daunting challenges in the areas of service latency, security, and trustworthiness. Recently researchers have presented the edge AIGC paradigm, effectively optimizing the service latency by distributing AIGC services to edge devices. However, AIGC products are still unprotected and vulnerable to tampering and plagiarization. Moreover, as a kind of online non-fungible digital property, the free circulation of AIGC products is hindered by the lack of trustworthiness in open networks. For the first time, in this article, we present a blockchain-empowered framework to manage the lifecycle of edge AIGC products. Specifically, leveraging fraud proofability, we first propose a protocol to protect the ownership and copyright of AIGC, called Proof-of-AIGC. Then, we design an incentive mechanism to guarantee the legitimate and timely execution of the funds-AIGC ownership exchanges among anonymous users. Furthermore, we implement a multi-weight subjective logic-based reputation scheme with which AIGC producers can determine which edge service provider is trustworthy and can reliably handle their services. Through the use of test data, we demonstrate the superiority of the proposed approach. Last but not least, we discuss important open directions for further research.

Artificial intelligence-generated content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models - for example, the stochastic nature of diffusion models - the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users' satisfaction as well as the quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. It is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, that is, overloading-avoidance, randomness, round-robin policies, and the upper-bound schemes.

The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. However, decentralized data sharing may not achieve the expected efficiency if there are IoV users who only want to consume the shared data but are not willing to contribute their own data to the community, resulting in incomplete information observed by other vehicles and infrastructure, which can introduce additional transmission latency. Therefore, in this article, by modeling the data sharing ecosystem as a data trading market, we propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets and minimize transmission latency. Our proposed mechanism takes into account the dynamic nature of IoV markets, which can experience frequent fluctuations in supply and demand. We propose a time-sensitive Key-Policy Attribute-Based Encryption (KP-ABE) mechanism coupled with Named Data Networking (NDN) to protect data in IoVs, which adds a layer of security to our proposed solution. Additionally, we design a decentralized market for efficient data sharing in IoVs, where continuous double auctions are adopted. The proposed mechanism based on multi-agent deep reinforcement learning can learn the supply-demand equilibrium in markets, thus improving the efficiency and sustainability of markets. Theoretical analysis and experimental results show that our proposed learning-based incentive mechanism outperforms baselines by 10% in determining the equilibrium of supply and demand while reducing transmission latency by 20%.

In the intelligent logistics and warehouses, the autonomous mobile device (AMD) holds a key position as it is equipped with the ability to carry out functions like material transportation and inventory inspection. Nevertheless, the effective execution of these functions necessitates the location of the AMD. Given the increasing proliferation of networks like WiFi and 5G, leveraging these signals to achieve AMD localization is a desirable solution. Therefore, this paper proposes a channel state information (CSI) based system for through-the-wall (TTW) passive AMD detection and localization, named T-DeLo. T-DeLo first establishes a reference channel and utilizes it to cancel the strong signal interference (SSI) and phase errors, ensuring that the reflections introduced by the AMD can be estimated. Built upon this core, it uses the proposed novel two-dimensional matrix pencil algorithm to estimate jointly the path length change rate (PLCR) and time of flight (ToF) of the AMD induced reflections, in the TTW scenario. Unlike existing algorithms, this algorithm aggregates multiple measurements to improve the estimation performance under conditions of low signal-to-noise ratio (SNR). Finally, leveraging the estimated ToF and PLCR, T-DeLo realizes TTW AMD detection and localization via statistical and geometric analysis, respectively. In the TTW glass and brick wall scenarios, the extensive experimental evaluation shows that the AMD detection accuracy of T-DeLo is 0.964 and 0.952, while the median localization errors are 1.65 m and 2.05 m, respectively, laying a solid foundation for practical and ubiquitous AMD passive detection and localization.

Visual sensors are one of the most essential sensing devices for achieving the traffic situational awareness of vehicles and monitoring equipment, as they can provide richer and more comprehensive information than other sensors. Therefore, many visual signal-based intelligent technologies have been proposed to perform a variety of traffic management tasks autonomously, including object detection, recognition, tracking, vehicle navigation, etc. Nonetheless, poor weather conditions, such as fog, haze, and mist, cause formidable challenges for visual technologies applied in intelligent transportation. To lessen the impacts of poor weather conditions, we propose a dual attention and dual frequency-guided dehazing network for enhancing visibility in real-time. In particular, the proposed model adopts an attention mechanism and a novel frequency information fusion strategy to extract global and local features and adequately recover sharp high-frequency structures and low-frequency details. Extensive experiments have revealed that our technique is superior to the state-of-the-art methods in terms of visibility augmentation and accuracy improvement of high-level visual tasks under hazy conditions.

The Metaverse is considered to be one prototype of the next-generation Internet, which contains people’s expectations for the future world. However, the academic discussion of the Metaverse still mainly focused on the technical system design, and few research studied Metaverse challenges from the perspective of consumers, i.e., Metaverse users. One difficulty is that the analysis from the consumer’s perspective requires interdisciplinary theoretical framework and quantifiable Quality of Experience (QoE) measurements. In this article, pioneering from consumers’ point of view, we explore an interaction between Metaverse system design and consumer behaviors. Specifically, we rethink QoE and propose an framework that encompasses both the Metaverse service providers (MSPs) and consumer considerations. From the macro perspective, we introduce a joint optimization scheme that simultaneously considers the Metaverse system design, consumers’ utility, and profitability of the MSPs. From the micro perspective, we advocate the Willingness-to-Pay (WTP) as an easy-to-implement measurement for future Metaverse system studies. To illustrate the usability of the proposed integrated framework, a use case, i.e., virtual traveling, is presented. We show that our framework can benefit the MSPs in offering competitive and economical service design to consumers while maximizing the profit.

The gradual fusion of intelligent transportation systems with metaverse technologies is giving rise to vehicular metaverses, which blend virtual spaces with physical space. As indispensable components for vehicular metaverses, Vehicular Twins (VTs) are digital replicas of Vehicular Metaverse Users (VMUs) and facilitate customized metaverse services to VMUs. VTs are established and maintained in RoadSide Units (RSUs) with sufficient computing and storage resources. Due to the limited communication coverage of RSUs and the high mobility of VMUs, VTs need to be migrated among RSUs to ensure real-time and seamless services for VMUs. However, during VT migrations, physical-virtual synchronization and massive communications among VTs may cause identity and location privacy disclosures of VMUs and VTs. In this article, we study privacy issues and the corresponding defenses for VT migrations in vehicular metaverses. We first present four kinds of specific privacy attacks during VT migrations. Then, we propose a VMU-VT dual pseudonym scheme and a synchronous pseudonym change framework to defend against these attacks. Additionally, we evaluate average privacy entropy for pseudonym changes and optimize the number of pseudonym distribution based on inventory theory. Numerical results show that the average utility of VMUs under our proposed schemes is 33.8% higher than that under the equal distribution scheme, demonstrating the superiority of our schemes.

Emerging with the support of computing and communications technologies, Metaverse is expected to bring users unprecedented service experiences. However, the increase in the number of Metaverse users places a heavy demand on network resources, especially for Metaverse services that are based on graphical extended reality and require rendering a plethora of virtual objects. To make efficient use of network resources and improve the Quality-of-Experience (QoE), we design an attention-aware network resource allocation scheme to achieve customized Metaverse services. The aim is to allocate more network resources to virtual objects in which users are more interested. We first discuss several key techniques related to Metaverse services, including QoE analysis, eye-tracking, and remote rendering. We then review existing datasets and propose the user-object-attention level (UOAL) dataset that contains the ground truth attention of 30 users to 96 objects in 1, 000 images. A tutorial on how to use UOAL is presented. With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i.e., attention prediction and QoE maximization. Specially, we provide an overview of the designs of two types of attention prediction methods, i.e., interest-aware and time-aware prediction. By using the predicted user-object-attention values, network resources such as the rendering capacity of edge devices can be allocated optimally to maximize the QoE. Finally, we propose promising research directions related to Metaverse services.

With rapid development of emerging technologies for Internet of Things (IoT), digital twins (DT) have been proposed to support a wide variety of applications. A mobile network is expected to be integrated with DT to form a DT mobile network (DTMN). Unfortunately, DTMN still faces security threats, which have attracted great research attention. Current defense mechanisms are mostly static, i.e., responding after attacks happening. To solve the aforementioned problem, moving target defense (MTD) has been proposed as an innovative solution. However, there exist three major challenges when applying MTD into DTMN. Firstly, less emphasis was paid to collaborative scheduling between multiple MTD schemes, which can improve the security of DTMN. Secondly, MTD schemes require lots of network resources, but few works focus on the time allocation of multiple MTD schemes to reduce network resource consumption. Thirdly, existing defense strategies only rely on current information, but do not consider future information. In this paper, we propose a collaborative mutation-based MTD (CM-MTD) in DTMN. We mainly consider two MTD schemes called host address mutation (HAM) and route mutation (RM), respectively, which adjust network properties and invalidate different stages of cyber kill chain. We firstly formulate a semi-Markov decision process (SMDP) to model time-varying security events and dynamic deployment of multiple MTD schemes. Then, security events are predicted by long short-term memory (LSTM), which are regarded as network states in SMDP. Next, infeasible actions that do not satisfy network constraints will be removed from the action space of the SMDP. Lastly, we design a hierarchical deep reinforcement learning algorithm for collaborative scheduling. Simulation results highlight the effectiveness of CM-MTD compared with baseline solutions.

Blockchain-based Federated Learning (FL) technology enables vehicles to make smart decisions, improving vehicular services and enhancing the driving experience through a secure and privacy-preserving manner in Intelligent Transportation Systems (ITS). Many existing works exploit two-layer blockchain-based FL frameworks consisting of a mainchain and subchains for data interactions among intelligent vehicles, which resolve the limited throughput issue of single blockchain-based vehicular networks. However, the existing two-layer frameworks still suffer from a) strong dependency on predetermined and fixed parameters of vehicular blockchains which limit blockchain throughput and reliability; and b) high communication costs incurred by interactions among intelligent vehicles between the mainchain and subchains. To address the above challenges, we first design an adaptive blockchain-enabled FL framework for ITS based on blockchain sharding to facilitate decentralized vehicular data flows among intelligent vehicles. A streamline-based shard transmission mechanism is proposed to ensure communication efficiency almost without compromising the FL accuracy. We further formulate the proposed framework and propose an adaptive sharding mechanism using Deep Reinforcement Learning to automate the selection of parameters of vehicular shards. Numerical results clearly show that the proposed framework and mechanisms achieve adaptive, communication-efficient, credible, and scalable data interactions among intelligent vehicles.

Model update compression is a widely used technique to alleviate the communication cost in federated learning (FL). However, there is evidence indicating that the compression-based FL system often suffers the following two issues, i) the implicit learning performance deterioration of the global model due to the inaccurate update, ii) the limitation of sharing the same compression rate over heterogeneous edge devices. In this paper, we propose an energy-efficient learning framework, named Snowball, that enables edge devices to incrementally upload their model updates in a coarse-to-fine compression manner. To this end, we first design a fine-grained compression scheme that enables a nearly continuous compression rate. After that, we investigate the Snowball optimization problem to minimize the energy consumption of parameter transmission with learning performance constraints. By leveraging the theoretical insights of the convergence analysis, the optimization problem is transformed into a tractable form. Following that, a water-filling algorithm is designed to solve the problem, where each device is assigned a personalized compression rate according to the status of the locally available resource. Experiments indicate that, compared to state-of-the-art FL algorithms, our learning framework can save five times the required energy of uplink communication to achieve a good global accuracy.

The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient information-sharing scheme based on full-duplex device-to-device (D2D) semantic communications to address this issue. Our approach enables users to avoid heavy and repetitive computational tasks, such as artificial intelligence-generated content (AIGC) in the view images of all MR users. Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images. We analyze the performance of full-duplex D2D communications, including the achievable rate and bit error probability, by using generalized small-scale fading models. To facilitate semantic information sharing among users, we design a contract theoretic AI-generated incentive mechanism. The proposed diffusion model generates the optimal contract design, outperforming two deep reinforcement learning algorithms, i.e., proximal policy optimization and soft actor-critic algorithms. Our numerical analysis experiment proves the effectiveness of our proposed methods. The code for this paper is available at https://github.com/HongyangDu/SemSharing.

In the vehicular mixed reality (MR) Metaverse, the discrepancy between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSUs), and virtual simulators can maintain the vehicular MR Metaverse via simulations for sharing data and making driving decisions collaboratively. However, it is challenging and costly to enable large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data via simulations for improving driving safety and traffic control efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and real-world data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets for improved robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs on providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective.

Recently, passive behavioral biometrics (e.g., gesture or footstep) acquired from wireless networks or mobile services have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing technologies require lengthy measurement traces and cannot identify multiple users at the same time. To this end, we propose PURE as a passive multi-person identification system leveraging deep learning enabled footstep separation and recognition. PURE passively identifies a user by identifying the unique “footprints” in its footstep. Different from existing gait-enabled recognition systems incurring a long sensing delay to acquire many footsteps, PURE can recognize a person by as few as only one step, substantially cutting the identification latency. To make PURE adaptive to walking pace variations, environmental dynamics, and even unseen targets, we apply an adversarial learning technique to improve its domain generalisability and identification accuracy. Finally, PURE is robust against replay attack, enabled by the richness of footstep and spatial awareness. We implement a PURE prototype using commodity hardware and evaluate it in typical indoor settings. Evaluation results demonstrate a cross-domain identification accuracy of over 90%.

Over-the-air computation (OAC) is a promising technology that can achieve rapid model aggregation by utilizing the wireless waveform superposition feature to harness the interference of multiple-access channel for wireless federated learning (FL). However, OAC-based aggregation for OAC faces critical security challenges due to unfavorable and wireless broadcast properties, such as privacy leaks and eavesdropping attacks. In this paper, we propose to utilize an intelligent reflecting surface (IRS) to support covert OAC-based FL. We first derive the optimal condition for covertness in OAC with IRS and formulate a joint optimization problem to select the maximum covert devices participating in the model aggregation while satisfying the mean squared error (MSE) requirement. We then design a covert difference-of-convex-functions program (CDC) to efficiently determine the transmission power of the device, aggregation beamforming of base station (BS), phase shifts, and reflection amplitudes at the IRS. Simulation results demonstrate that our proposed approach can achieve significant performance gain compared to the baseline algorithms by deploying IRS into covert OAC-based FL.

The seamless and ubiquitous wireless access is crucial to the immersive experiences in the metaverse. Considering the limited communication and computing resources, how to provide metaverse services with high Quality of Experience (QoE) for users is still challenging. In this paper, an innovative QoE model for metaverse services based on the virtual distance and network effect is proposed. Especially, we introduce a novel metric called “meta-distance” to measure virtual distance in the metaverse, which jointly considers the service delay and social distance among metaverse users. To solve the QoE utility maximization problem, we propose a Joint Resource Allocation and Metaverse service Selection (JRAMS) scheme, which is composed of a two-step mechanism. In the first step, referred to as the inner loop of JRAMS, a one-to-many matching game with externalities is used to match base stations and metaverse users with Non-Orthogonal Multiple Access (NOMA) based subchannel allocation. In the second step, referred to as the outer loop of JRAMS, a hedonic coalition formation game is used to solve the metaverse service selection problem. After finite iterations, JRAMS can converge to a stable solution. The simulation results show that compared with baselines, the average QoE utility of JRAMS can be significantly improved.

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data. The code for this paper is available at https://github.com/HongyangDu/SemSensing.

The semantic communication mechanism enables wireless devices in vehicular networks to communicate more effectively with the semantic meaning. However, in high-dynamic vehicular networks, the transmission of semantic information faces challenges in terms of reliability and stability. To address these challenges, a long-term robust resource allocation scheme is proposed under the Device-to-Device (D2D) vehicular (D2D-V) networks, where multiple performance indicators (user satisfaction, queue stability, and communication delay) are considered. Due to the sophisticated probabilistic form with consideration of channel fluctuations, the Bernstein approximation is introduced to acquire the deterministic constraint more efficiently. The robust resource allocation problem is proposed and separated into two independent subproblems by the Lyapunov optimization method, which includes semantic access control in the application layer and power control in the physical layer. After that, the successive convex approximation method and Karush-Kuhn-Tucher conditions are adopted to solve the subproblems, thereby proposing a robust resource allocation algorithm. The simulations reveal the trade-off relationship between user satisfaction, queue stability, and communication delay, which is on the premise of meeting the user SINR requirement. Moreover, the simulations also prove the necessity of considering channel uncertainty in high-speed mobile vehicular communication scenarios.

With the advancement of network and computer technologies, virtual cyberspace keeps evolving, and Metaverse is the main representative. As an irreplaceable technology that supports Metaverse, the sensing information transmission from the physical world to Metaverse is vital. Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse. Leveraging the in-depth understanding of sensing information, we define the semantic bases, through which the semantic encoding of sensing data is achieved for the first time. Consequently, the amount of sensing data that needs to be transmitted is dramatically reduced. Unlike conventional methods that undergo data degradation and require data recovery, our approach achieves the sensing goal without data recovery while maintaining performance. To further improve Metaverse service quality, we introduce contest theory to create an incentive mechanism that motivates users to upload data more frequently. Experimental results show that the average data amount after semantic encoding is reduced to about 27.87% of that before encoding, while ensuring the sensing performance. Additionally, the proposed contest theoretic based incentive mechanism increases the sum of data uploading frequency by 27.47% compared to the uniform award scheme.

At present, the number of incoming devices and mobile data traffic has seen explosive growth, and spectrum resources are difficult to meet the increasing demand. The traditional centralized spectrum management model suffers from low spectrum utilization, high maintenance costs, and data security issues. The decentralized and secure nature of blockchain technology is a natural advantage in solving the problems faced by the current spectrum management model. However, when introducing traditional blockchain technology into spectrum management, it faces problems such as high latency and high resource consumption. In this letter, we design a new blockchain-enabled spectrum trading framework consisting of the access layer, distribution layer, and core layer, and then propose a user benefit-maximum spectrum trading matching scheme. To solve the problem of latency and energy consumption of consensus algorithms in blockchains, the dynamic credit aggregate-signature Byzantine fault tolerance (DCABFT) algorithm based on the Boneh-Lynn-Shacham (BLS) aggregated signature is proposed. Simulation results demonstrate that the proposed scheme can greatly improve the transaction latency and users’ quality of service.

In the upcoming 6G era, existing ground networks have evolved toward space-air-ground integrated networks (SAGIN), providing ultra-high data rates, seamless network coverage, and ubiquitous intelligence for communications of applications and services. However, conventional communications in SAGIN still face data confidentiality issues. Fortunately, the concept of Quantum Key Distribution (QKD) over SAGIN is able to provide information-theoretic security for secure communications in SAGIN with quantum cryptography. Therefore, in this article, we propose quantum-secured SAGIN (Q-SAGIN) which are feasible to achieve proven secure communications using quantum mechanics to protect data channels between space, aerial, and ground nodes. Moreover, we propose a universal QKD service provisioning framework to minimize the cost of QKD services under the uncertainty and dynamics of communications in Q-SAGIN. In this framework, fiber-based QKD services are deployed in passive optical networks with the advantages of low loss and high stability. Moreover, the widely covered and flexible satellite-and UAV-based QKD services are provisioned as a supplement during the real-time data transmission phase. Finally, to examine the effectiveness of the proposed concept and framework, a case study of Q-SAGIN in the Metaverse is conducted where uncertain and dynamic factors of the secure communications in Metaverse applications are effectively resolved in the proposed framework.

Web 3.0 is the next-generation Internet that enables participants to read, write, and own contents in a decentralized manner. It is mainly driven by blockchain, semantic communication, edge computing, and artificial intelligence, which can construct value networks to achieve participatory economics based on participatory decision making. Web 3.0 can capture the characteristics of blockchain, semantic extraction, and communication to achieve decentralized semantic sharing and transfer information precisely. However, current Web 3.0 solutions focus on the blockchain while overlooking other new technologies' roles in Web 3.0. To further unleash the advantages of semantic extraction and communication in Web 3.0, in this article, we propose a blockchain-based semantic exchange framework to realize fair and efficient interactions. In this framework, we first attempt to tokenize semantic information into Non-Fungible Token (NFT) for semantic exchange. Then we utilize a Stackelberg game to maximize buying and pricing strategies for semantic trading. We also leverage Zero-Knowledge Proof to share authentic semantic information without publishing it before receiving payments, which can achieve a fair and privacy-preserving trading compared with current NFT marketplaces. A case study about urban planning is given to show clearly the proposed mechanisms. Finally, several challenges and opportunities are identified.

As the next-generation Internet paradigm, Web 3.0 encapsulates the expectations of immersive and interactive experiences in a decentralized manner. Metaverse, a virtual world interacting with the physical world, is becoming one of the most potential applications to push forward with Web 3.0. In the Metaverse, users expect to tailor immersive and interactive experiences by customizing real-time Metaverse services (e.g., augmented/virtual reality and digital twin) in the three-dimensional virtual world. Nevertheless, there are still no unified solutions for the Metaverse services in terms of orchestration and management. Therefore, continuous and seamless upgrading of network systems is essential yet challenging to support Metaverse services. In this article, to provide scalable solutions for tailoring Metaverse services, we propose a new concept, named Metaverse-as-a-Service (MaaS), where users only need to purchase the MaaS models that are needed (e.g., computing, communications, and data resources) to achieve their required Metaverse services. Furthermore, to unify the orchestration and management of MaaS models, we propose a novel architecture, called Slicing4Meta, to customize Metaverse services by integrating MaaS models and the associated multi-dimensional resources. Additionally, we propose the classification for typical Metaverse services based on the quality of experience (QoE) requirements and illustrate how to fulfill the QoE requirements under the Slicing4Meta architecture. We then illustrate a virtual travel study case, in which we examine the relationship between the QoE and the multi-dimensional resources by quantitatively modeling the QoE of Metaverse users. Finally, we discuss some open challenges of Slicing4Meta and propose potential solutions to address the challenges.

Metaverse encapsulates our expectations of the next-generation Internet, while bringing new key performance indicators (KPIs). Although conventional ultra-reliable and low-latency communications (URLLC) can satisfy objective KPIs, it is difficult to provide a personalized immersive experience that is a distinctive feature of the Metaverse. Since the quality of experience (QoE) can be regarded as a comprehensive KPI, the URLLC is evolved towards the next generation URLLC (xURLLC) with a personalized resource allocation scheme to achieve higher QoE. To deploy Metaverse xURLLC services, we study the interaction between the Metaverse service provider (MSP) and the network infrastructure provider (InP), and provide an optimal contract design framework. Specifically, the utility of the MSP, defined as a function of Metaverse users’ QoE, is to be maximized, while ensuring the incentives of the InP. To model the QoE mathematically, we propose a novel metric named Meta-Immersion that incorporates both the objective KPIs and subjective feelings of Metaverse users. Furthermore, we develop an attention-aware rendering capacity allocation scheme to improve QoE in xURLLC. Using a user-object-attention level dataset, we validate that the xURLLC can achieve an average of 20.1% QoE improvement compared to the conventional URLLC with a uniform resource allocation scheme. The code for this paper is available at https://github.com/HongyangDu/AttentionQoE.

With the advantages of high-speed parallel processing, quantum computers can efficiently solve large-scale complex optimization problems in future networks. However, due to the uncertain qubit fidelity and quantum channel noise, distributed quantum computing, which relies on quantum networks connected through entanglement, faces many challenges in exchanging information across quantum computers. In this article, we propose an adaptive distributed quantum computing approach, called DQC2O, to manage quantum computers and quantum networks for solving optimization tasks in future networks. Firstly, we describe the fundamentals of quantum computing and its distributed concept in quantum networks. Secondly, to address the uncertainty of future demands of collaborative optimization tasks and instability over quantum networks, we propose a quantum resource allocation scheme based on stochastic programming for minimizing quantum resource consumption. Finally, based on the proposed approach, we discuss the potential military applications of collaborative optimization in future networks, such as smart grid management, IoT cooperation, and semantic communications. Promising research directions that can lead to the design and implementation of future distributed quantum computing frameworks are also highlighted.