The developing Sixth-Generation (6G) network aims to establish seamless global connectivity for billions of humans, machines, and devices. However, the rich digital service and the explosive heterogeneous connection between various entities in 6G networks can not only induce increasing complications of digital identity management, but also raise material concerns about the security and privacy of the user identity. In this paper, we design a user-centric identity management that returns the sole control to the user self and achieves identity sovereignty toward 6G networks. Specifically, we propose a blockchain-based Identity Management (IDM) architecture for 6G networks, which provides a practical method to secure digital identity management. Subsequently, we develop a fully privacy-preserving identity attribute management scheme by using zero-knowledge proof to protect the privacy-sensitive identity attribute. In particular, the scheme achieves an identity attribute hiding and verification protocol to support users in obtaining and applying their identity attributes without revealing concrete data. Finally, we analyze the security of the proposed architecture and implement a prototype system to evaluate its performance. The results demonstrate that our architecture ensures effective user digital identity management in 6G networks.

Delay Alignment Modulation (DAM) is an innovative broadband modulation technique well-suited for millimeter Wave (mmWave) and Terahertz (THz) massive Multiple-Input Multiple-Output (MIMO) communication systems. Leveraging the high spatial resolution and sparsity of multi-path channels, DAM effectively mitigates Inter-Symbol Interference (ISI) by aligning all multi-path components through a combination of delay pre-compensation (or post-compensation) and path-based beamforming. As such, ISI is eliminated while preserving multi-path power gains. In this paper, we investigate multi-user double-side DAM, which incorporates both delay pre-compensation at the transmitter and post-compensation at the receiver, in contrast to prior works that primarily focus on single-side DAM with only delay pre-compensation. Firstly, we derive the constraint on the number of introduced delays and formulate the corresponding delay pre/post-compensation vectors tailored for multi-user double-side DAM, given a specific number of delay compensations. Furthermore, we demonstrate that when the number of Base Stations (BSs)/User Equipment (UE) antennas is sufficiently large, single-side DAM—where delay compensation is performed only at the BS/UE—is preferable to double-side DAM, since the former results in less ISI to be spatially eliminated. Next, we propose two low-complexity path-based beamforming strategies based on the eigen-beamforming transmission and ISI-Zero Forcing (ZF), respectively. On this basis, we further analyze the achievable sum rates. Simulation results verify that with a sufficiently large number of BS/UE antennas, single-side DAM is adequate for ISI elimination. Moreover, compared to the benchmarking scheme of Orthogonal Frequency Division Multiplexing (OFDM), multi-user BS-side DAM achieves higher spectral efficiency and lower Peak-to-Average Power Ratio (PAPR).

Computing Power Network (CPN) is a new paradigm that integrates communication, computing, and storage resources to provide services for tasks. However, tasks composed of non-independent subtasks have a preference for the resources required at each stage, which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services. Motivated by this, this paper jointly optimizes the full-service cycle of tasks, including transmission, task partitioning, and offloading. First, the transmission bandwidth is dynamically configured based on delay sensitivity of tasks. Second, with the real-time information from edge resource clusters and state resource clusters in the network, the optimal partitioning for a computation task is derived. Third, personalized resource allocation schemes are customized for computation and storage tasks respectively. Finally, the impact of resource parameter configuration on the latency violation probability of CPN is revealed. Moreover, compared with the benchmark schemes, our proposed scheme reduces the network latency violation probability by up to 1.17 × in the same network setting.

Accurate prediction of environmental temperature is pivotal for promoting sustainable crop growth. At present, the most effective temperature sensing and prediction system is the Agricultural Internet of Things (AIoT), which deploys a large number of sensors to collect meteorological data and transmits them to the cloud server for prediction. However, this procedure is computationally and communicationally expensive for resource-constrained AIoT. Recently, Semantic Communication (SC) has shown potential in efficient data transmission, but existing methods overlook the repetitive semantic information whilst sensing data, bringing additional overheads. With the resource-constraint nature of AIoT in mind, we propose the Semantic Communication-enabled Cognitive Agriculture Framework (SC-CAF) for delivering accurate temperature predictions. The proposed SC-CAF incorporates an intelligent analysis layer that performs the temperature prediction and model training and distribution, while a semantic layer transmitting the semantic information extracted from raw data based on the download model, ultimately to reduce communication overheads in AIoT. Furthermore, we propose a novel model called the Light Temperature Semantic Communication (LTSC) by adopting skip-attention and semantic compressor to avoid unnecessary computation and repetitive information, thereby addressing the semantic redundancy issues in sensing data. We also develop a Semantic-based Model Compression (SCMC) algorithm to alleviate the computation and bandwidth burden, enabling AIoT to explore the extensive usage of SC. Experimental results demonstrate that the proposed SC-CAF achieves the lowest prediction error while reducing Floating Point Operations (FLOPs) by 95.88%, memory requirements by 78.30%, Graphics Processing Unit (GPU) power by 50.77%, and time latency by 84.44%, outperforming notable state-of-the-art methods.

Reconfigurable Intelligent Surface (RIS) is envisioned as a promising technology to improve the system capacity of 6G network, by controlling the electromagnetic wave propagation. Most existing works use the Central Limit Theorem (CLT) to analyze the performance of RIS-assisted systems for large number of reflective elements. However, the assumption of extremely large number of elements may not be practical in the actual situation. In addition, the CLT-based approximation yields an inaccurate scaling law of the outage probability when the transmit Signal-to-Noise Ratio (SNR) tends to infinity. Motivated by these limitations, in this paper, we investigate the performance of RIS-assisted cellular networks with multiple Device-to-Device (D2D) users under the general fading channels, i.e., Nakagami-𝑚 fading channels. We propose a tractable solution to evaluate the outage probability and the ergodic achievable rate, which is accurate for any number of reflective elements, any network topology, as well as any SNR. In addition, the accurate approximations for the high SNR case and the large number of reflective elements case are further derived in simpler closed form. Numerical results verify the accuracy of our analytical results and analyze the performance between CLT and the proposed method.

In response to the rising demand for low-latency, computation-intensive applications in vehicular networks, this paper proposes an adaptive task offloading approach for Vehicle-to-Everything (V2X) environments. Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm with an attention mechanism, the proposed approach optimizes computation offloading and resource allocation, aiming to minimize energy consumption and service delay. In this paper, vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links. The adaptive attention mechanism enables the system to prioritize relevant state information, leading to faster convergence. Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG (AT-MADDPG) algorithm significantly improves performance, achieving notable reductions in both energy consumption and latency compared to baseline algorithms, especially in high-demand, dynamic scenarios.

To tackle the physical layer security challenges in wireless communication, this paper introduces a multi-user architecture that leverages single-channel blind source separation, centered around a Multi-source Signal Mixture Separator (MSMS). This architecture consists of a multi-user encoder, a channel layer, and a separation decoder, allowing it to handle multiple functions simultaneously, including encoding, modulation, signal separation, demodulation, and decoding. The MSMS receiver effectively enables the separation of numerous user signals, making it exceedingly difficult for unauthorized eavesdroppers to extract valuable information from the mixed signals, thus significantly enhancing communication security. The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design. The design of a single receiver reduces system costs and improves spectrum efficiency. The MSMS outperforms traditional Space-time Block Coding (STBC) strategies regarding separation performance, particularly in Block Error Rate (BLER) metrics. Modulation constellation diagrams further analyze the effectiveness of multi-source signal mixture separation. Moreover, this study extends the MSMS framework from a two-user scenario to a three-user scenario, further demonstrating the flexibility and scalability of the proposed architecture.

The global surge in Artificial Intelligence (AI) has been triggered by the impressive performance of deep-learning models based on the Transformer architecture. However, the efficacy of such models is increasingly dependent on the volume and quality of data. Data are often distributed across institutions and companies, making cross-organizational data transfer vulnerable to privacy breaches and subject to privacy laws and trade secret regulations. These privacy and security concerns continue to pose major challenges to collaborative training and inference in multi-source data environments. These challenges are particularly significant for Transformer models, where the complex internal encryption computations drastically reduce computational efficiency, ultimately threatening the model’s practical applicability. We hence introduce Secformer, an innovative architecture specifically designed to protect the privacy of Transformer-like models. Secformer separates the encoder and decoder modules, enabling the decomposition of computation flows in Transformer-like models and their efficient mapping to Multi-Party Computation (MPC) protocols. This design effectively addresses privacy leakage issues during the collaborative computation process of Transformer models. To prevent performance degradation caused by encrypted attention modules, we propose a modular design strategy that optimizes high-level components by reconstructing low-level operators. We further analyze the security of Secformer’s core components, presenting security definitions and formal proofs. We construct a library of fundamental operators and core modules using atomic-level component designs as the basic building blocks for encoders and decoders. Moreover, these components can serve as foundational operators for other Transformer-like models. Extensive experimental evaluations demonstrate Secformer’s excellent performance while preserving privacy and offering universal adaptability for Transformer-like models.

Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status. However, the presentation of the data lacks structural information. Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously. Therefore, there is a need for complementary methods to address these deficiencies. To address these limitations, this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system. A dual information network is constructed to assess the degree of operational deviation considering the planning tasks. To validate the effectiveness, discussions are conducted through a modified cosine similarity calculation on theoretical analysis, delay level description, and the ability to identify abnormal dates. Compared to some state-of-the-art methods, the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477. Furthermore, case analyses are invested in regions of China’s Mainland, Europe, and the United States, investigating both the overall and sub-regional network fluctuations. To represent the impact of network fluctuations in sub-regions, a response loss value was developed. The times that are prone to fluctuations are also discussed through the classification of time series data. The research can offer a novel approach to system monitoring, providing a research direction that utilizes individual data combined to represent macroscopic states. Our code will be released at https://github.com/daozhong/STPN.git.

Location-Based Services (LBS) have greatly improved efficiency and functionality in various domains, but privacy and security concerns remain due to the centralized nature of many existing systems. To address these issues, this paper introduces the V-Track system, a decentralized architecture using blockchain technology for reliable vehicle location verification. By integrating GPS devices (SparkFun GPS NEO-M9), IoT-enabled sensors, and a Cosmos blockchain-based ledger (network of interconnected blockchains), V-Track aims to solve centralized LBS problems. Through rigorous simulation experiments, this paper evaluates the performance and security of the V-Track system and demonstrates its potential to provide reliable location verification while preserving user privacy. This paper makes significant contributions by presenting V-Track as a decentralized solution to centralized LBS privacy and security problems, enhancing reliability and trustworthiness through blockchain integration, improving tracking mechanisms with GPS devices and IoT sensors for improved accuracy, and providing a privacy-preserving alternative to centralized LBS through its decentralized design and use of blockchain technology. These advancements hold promise for applications across multiple sectors, including logistics, supply chain management, urban planning, and emerging fields such as autonomous vehicles and augmented reality.

Multi-Agent Systems (MAS), which consist of multiple interacting agents, are crucial in Cyber-Physical Systems (CPS), because they improve system adaptability, efficiency, and robustness through parallel processing and collaboration. However, most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents. Meta-GMVAE, based on Variational Autoencoder (VAE) and set-level variational inference, represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks, increasing adaptability and reducing sample requirements. Inspired by these advancements, we propose a novel Distributed Unsupervised Meta-Learning (DUML) framework based on Meta-GMVAE and a fusion strategy. Furthermore, we present a DUML algorithm based on Gaussian Mixture Model (DUMLGMM), where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm. Simulations on Omniglot and MiniImageNet datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.

The concept of Cyber-Physical Systems (CPS) enables the creation of a complex network that includes sensors integrated into vehicles and infrastructure, facilitating seamless data acquisition and transfer. This review examines the convergence of CPS and Industry 4.0 in the smart transportation sector, highlighting their transformative impact on Intelligent Transportation Systems (ITS) operations. It explores the integration of Industry 4.0 and CPS technologies in intelligent transportation, highlighting their roles in enhancing efficiency, safety, and sustainability. A systematic framework is proposed for developing, implementing, and managing these technologies in the transportation industry. Moreover, the review discusses frequent obstacles during technology integration in transportation and presents future research trends and innovations in intelligent transportation operations post-Industry 4.0 and CPS integration. Lastly, it emphasizes the critical need for standardized protocols and encryption methodologies to enhance the security of communication and data exchange among CPS components in transportation infrastructure.

Practical applications of smart cities and the Internet of Things (IoT) have multiplied, posing many difficulties in network performance, dependability, and security. Concerns of accessibility, reliability, sustainability, and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems. Fog computing offers a foundation for various applications, including cognitive support, health and social services, intelligent transportation systems, and pervasive computing and communications. Fog computing can help enhance these apps’ productivity and lower the end-to-end delay experienced by such time-sensitive applications. In this research, we propose a reliable and secure service delivery strategy at the network edge for smart cities. To improve the availability and dependability, along with the security of smart city applications, the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system. Simulation findings suggest a reduction of 40.3% in the delay incurred by each service request for highly dense areas and 60.6% for moderately dense environments. Furthermore, the system has low false-negative rates and high detection and accuracy rates, decreasing service requests 2%.

The Internet of Vehicles (IoV) is an emerging technology that aims to connect vehicles, infrastructure, and other devices to enable intelligent transportation systems. One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities. This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV. The system leverages Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to collect real-time data about the environment and the vehicles. The data is collected to acknowledge the heterogeneity of vehicles and human behavior. The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions. The system takes into account the heterogeneity of vehicles, such as their size, speed, and maneuverability, to optimize collision avoidance strategies. The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems. The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5% using the SVM algorithm. The trial outcomes demonstrated that the new system, incorporating vehicular, weather, and human behavior factors, outperformed previous systems that only considered vehicular and weather aspects. This innovative approach is poised to lead transportation efforts, reducing accident rates and improving the quality of transportation systems in smart cities. By offering predictive capabilities, the proposed model not only helps control accident rates but also prevents them in advance, ensuring road safety.

Smart cities, as a typical application in the field of the Internet of Things, can combine cloud computing to realize the intelligent control of objects and process massive data. While cloud computing brings convenience to smart city services, a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries. Considering the security and privacy of data, data owners transmit sensitive data in its encrypted form to cloud server, which seriously hinders the improvements of potential utilization and efficient sharing. Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption. However, most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners. In this work, by utilizing certificateless encryption and proxy re-encryption, we design an authenticated searchable encryption scheme with constant trapdoors. The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors, and can resist keyword guessing attacks. In addition, data users can generate and upload trapdoors with lower computation and communication overheads. We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.

Advanced technologies like Cyber-Physical Systems (CPS) and the Internet of Things (IoT) have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems (ITS). Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure (V2I) communication, supporting better traffic management, safety, and efficiency. These technological innovations generate complex problems that need to be addressed, uniquely about data routing and Task Scheduling (TS) in ITS. Attempts to solve those problems were primarily based on traditional and experimental methods, and the solutions were not so successful due to the dynamic nature of ITS. This is where the scope of Machine learning (ML) and Swarm Intelligence (SI) has significantly impacted dealing with these challenges; in this line, this research paper presents a novel method for TS and data routing in the CPS-ITS. This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS. This ML has Gated Linear Unit-approximated Reinforcement Learning (GLRL). Greedy Iterative-Particle Swarm Optimization (GI-PSO) has been recommended to develop the Particle Swarm Optimization (PSO) for TS. The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS. This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments. The experiments demonstrate that the proposed GLRL reduces End-to-End Delay (EED) by 12%, enhances data size use from 83.6% to 88.6%, and achieves higher bandwidth allocation, particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%. Furthermore, the GLRL reduced Network Congestion (NC) by 5.5%, demonstrating its efficiency in managing complex traffic conditions across several environments. The model passed simulation tests in three different environments: urban (UE), suburban (SE), and rural (RE). It met the high bandwidth requirements, made task scheduling more efficient, and increased network throughput (NT). This proved that it was robust and flexible enough for scalable ITS applications. These innovations provide robust, scalable solutions for real-time traffic management, ultimately improving safety, reducing NC, and increasing overall NT. This study can affect ITS by developing it to be more responsive, safe, and effective and by creating a perfect method to set up UE, SE, and RE.