Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) are critical for effective diagnosis and treatment planning. This paper proposes a novel framework for brain tumor segmentation and classification using deep learning. The segmentation model is based on a modified U-Net architecture, called Residual Feature Pyramids U-Net with Attention (RFAU-Net), which incorporates residual blocks to enhance training depth, attention mechanisms to focus on relevant features, and a feature pyramid module to improve segmentation of small and complex tumor regions. To address class imbalance and pixel degradation during training, we introduce a combined loss function (CL) that integrates Weighted Focal Loss (WFL), assigning higher weights to minority classes and reducing the influence of majority classes. The model is evaluated on two publicly available datasets, achieving state-of-the-art performance with a segmentation accuracy of 97%, a Dice Similarity Coefficient (DSC) of 92.5%, and an Intersection over Union (IoU) of 92%. For tumor classification, we employ a Multi-Headed Convolutional Neural Network (MHCNN), achieving 99.8% accuracy in classifying the MGMT methylation status. These results demonstrate the superiority of the RFAU-Net model over traditional U-Net and RESU-Net architectures, particularly in handling small tumor regions and class imbalance. Additionally, a user-friendly web API is developed to classify brain tumors into MGMT methylated and unmethylated categories, enabling efficient integration of this model into clinical practice for improved diagnosis and treatment of gliomas.

Device-enhanced mobile edge computing (MEC) is an emerging technology designed to handle intensive and delay-sensitive tasks through device-to-device (D2D) communication. In this paper, we present a terminal-edge collaborative computing network to investigate device-enhanced distributed tasks scheduling (DDTS) with specific application deployment. Our optimization focuses on offloading choices, bandwidths, and computing frequencies, aiming to minimize execution costs, including processing delay and energy consumption. We decouple the joint multiple goals optimization problem into several sub-problems which are solved by math optimization methods except the NP-hard offloading choices sub-problem. This NP-hard problem is modeled as a multitask scheduling game (MTSG), which we demonstrate to be a potential game with at least one Nash equilibrium solution. However, considering further the dynamic nature of real-world application deployment and the complexity of large-scale games, the problem evolves into a stochastic game with a Markov policy (SGMP). Thus, we propose a multi-agent DDTS algorithm based on a dueling double deep Q-network (D3QN) to approximate an optimal solution. Extensive experiments confirm the feasibility and efficiency of our approach.

In wireless communication and edge computing networks, fractional programming (FP) and multiplicative programming (MP) are fundamental methodologies widely employed in solving non-convex optimization problems. Prior work introduced a remarkable method to solve non-convex functions with multiple ratios by deriving novel tight upper bounds, which we formalize as the UpperBound transform in this paper. However, the UpperBound transform faces critical limitations, particularly when directly extended to MP problems involving non-negative functions or discrete optimization variables. In this paper, we introduce a generalized transform termed the UP transform to overcome these limitations.We rigorously prove that the UP transform guarantees convergence to a Karush-Kuhn-Tucker (KKT) point for a broader class of MP problems, including scenarios where variables can be zero or discrete. We comprehensively illustrate the UP transform’s utility through two practical applications: partial task offloading in mobile edge computing, optimizing computation and energy efficiency; and user association coupled with resource allocation in heterogeneous networks, addressing mixed discrete-continuous optimization challenges. Comparative evaluations against conventional methods demonstrate superior convergence speed, efficiency, solution quality, and reduced computational complexity of the proposed UP transform based algorithms.

Semantic communication (SemCom) has recently gained attention for its ability to achieve high transmission efficiency with minimal data distortion under limited communication resources. However, the strong correlation between source data and channel input leaves SemCom schemes vulnerable to eavesdropping. Additionally, advances in quantum computing threaten traditional cryptographic methods such as RSA due to Shor’s algorithm. To address these risks, a secure SemCom framework with post-quantum protection is essential. This paper presents a post-quantum secure semantic communication (PQSC) framework by integrating learning with errors (LWE) encryption (widely regarded as quantum-resistant) into a VQ-VAE-based SemCom system. The proposed PQSC framework not only resists quantum attacks but also defends against chosen-plaintext attacks. Experiments show that PQSC consistently outperforms baseline methods across various datasets, channel conditions, and SNR levels. To simulate practical wireless environments, we implement channel coding and modulation using Nvidia Sionna, a GPU-accelerated library for physical layer research. We further examine the trade-off between compression efficiency and computational cost. A downlink use case is modeled to analyze recovery quality, energy consumption, and latency. Our mathematical analysis offers insights into system design and parameter selection for real-world deployment.

This paper addresses the security and privacy issues of the global models in Federated Learning by proposing a new approach, called PDFL, which tackles the challenges of poisoning attacks and privacy leakage in FL rounds. PDFL is based on secure multi-party computation and performs privacy-preserving cluster analysis on encrypted data from participants in order to identify malicious poisoning attackers. This approach involves a two-server mechanism and integrates four privacy-preserving protocols based on two-party computation (2PC): SecJudge for normalizing gradients, SecCosine for computing the cosine similarity values among gradients, SecClu for countering poisoning attacks, and SecAgg for secure aggregation by the server. These protocols are designed to achieve low computational costs, preserve client data privacy, and mitigate poisoning attacks from the potentially malicious clients. We provide a theoretical proof that our four sub-protocols and the PDFL scheme are both safe and reliable, demonstrating that PDFL can ensure the privacy and security of the participating data. Additionally, we conduct extensive simulation experiments to evaluate the accuracy, efficiency, computational overhead, and communication overhead associated with the PDFL scheme. Experimental results show the potential of the PDFL scheme in significantly enhancing the ability to identify malicious poisoning attackers in federated learning systems accurately and efficiently, hence making PDFL a promising solution for addressing privacy and security concerns in this domain.

As mobile devices continue to drive advanced applications, edge computing has emerged as a crucial solution to overcome their inherent computational constraints, especially in deploying and training large language models (LLMs). Despite progress in edge computing, significant challenges remain in achieving efficient LLM training while addressing computational demands, energy consumption, and model stability. This paper presents an enhanced collaborative training framework that integrates mobile users with edge servers to optimize resource allocation. We extend the framework by incorporating model stability into the optimization objectives, mitigating performance instability often observed during distributed LLM fine-tuning. A multi-objective optimization problem is formulated to minimize energy consumption, delay, and instability, with a novel fractional programming technique and Iterative Rank Penalization (IRP) method proposed to improve the resource allocation and user-to-edge server associations. Compared to traditional methods like Semidefinite Relaxation, IRP achieves higher accuracy and computational efficiency. Extensive simulations demonstrate that our approach outperforms existing methods in reducing energy consumption and delay, and improving LLM stability across various mobile edge computing environments.

With the evolution of artificial intelligence-generated content (AIGC) techniques and the development of space-air-ground integrated networks (SAGIN), there will be a growing opportunity to enhance mobile user experiences with customized AIGC applications. This is enabled by combining parameter-efficient fine-tuning (PEFT) with mobile edge computing. In this paper, we formulate the optimization problem of maximizing the parameter training efficiency of the SAGIN system over wireless networks under limited resource constraints. We propose theParameter training efficiencyAwareResourceAllocation (PARA) technique to jointly optimize user association, data offloading, and communication and computational resource allocation. Detailed derivations are presented to solve this difficult sum of ratios problem based on quadratically constrained quadratic programming (QCQP), semidefinite programming (SDP), graph theory, and fractional programming (FP) techniques. Our proposed PARA technique is effective in finding a stationary point of this non-convex problem. The simulation results demonstrate that the proposed PARA method outperforms other baselines.

In the rapidly evolving landscape of the Metaverse, enhanced by blockchain technology, the efficient processing of data has emerged as a critical challenge, especially in wireless communication systems. Addressing this challenge, our paper introduces the innovative concept of data processing efficiency (DPE), aiming to maximize processed bits per unit of resource consumption in blockchain-empowered Metaverse environments. To achieve this, we propose the DPE-Aware User Association and Resource Allocation (DAUR) algorithm, a tailored optimization framework for blockchain-enabled Metaverse wireless communication systems characterized by joint computing and communication resource constraints. The DAUR algorithm transforms the nonconvex problem of maximizing the sum of DPE ratios into a solvable convex optimization problem. It alternates the optimization of key variables, including user association, work offloading ratios, task-specific computing resource distribution, bandwidth allocation, user power usage ratios, and server computing resource allocation ratios. Our extensive numerical results demonstrate the DAUR algorithm's effectiveness in DPE.

The growing demand for intelligent mobile applications has made the deployment and operation of Deep Neural Networks (DNNs) on mobile Edge Devices (EDs) increasingly essential. However, the highly dynamic nature of edge environments and the limited computational resources of EDs result in significant energy consumption and compromised inference quality. To address these issues, we propose the Demand-Aware Evolutionary Reinforcement Learning (DA-ERL) framework, a novel approach for optimizing Partitioned Collaborative Inference (PCI) across multiple EDs and Mobile Edge Computing (MEC) servers. At the core of DA-ERL is a Demand-Aware Spatio-Temporal Graph Convolutional Network (DA-STGCN). This new architecture creates a predictive state representation by uniquely integrating two channels: a Spatial Graph Channel using Graph Convolutional Networks to model the network topology, and a Temporal Prediction Channel using Temporal Convolutional Networks to capture the evolution of system dynamics. Moreover, we design and formulate a task dynamic demand index to model the dynamic task characteristics, which guides the agent's learning policy. Furthermore, we train DA-ERL within a Cross-Entropy Method (CEM) based evolutionary framework that leverages elite-guided exploration to enhance sample efficiency in complex search spaces. Extensive simulations demonstrate that the proposed DA-ERL framework significantly outperforms conventional methods, achieving a 23.4% reduction in system cost while maintaining a near-perfect task completion rate in high-density scenarios.

With the rapid advancement of large models and mobile edge computing, transfer learning through fine-tuning has become essential for adapting models to downstream tasks. Traditionally, users must share their data with model owners, which is costly and raises privacy risks. In addition, fine-tuning large-scale models is computationally intensive and often impractical for many users. To address these challenges, we propose a model that combines offsite-tuning with physical-layer security. Local data owners are given a lightweight adapter and a compressed emulator extracted from the original model. They fine-tune the adapter locally and securely send it back to the model owner through a confidential channel for integration, ensuring privacy and resource conservation. Our work focuses on optimizing computational resource allocation between data owners and the large model owner at the edge, while also optimizing the adapter compression ratio to improve efficiency. We integrate a secrecy uplink channel to maximize the defined utility while minimizing system costs such as energy consumption and delay. The optimization process employs the Dinkelbach algorithm, fractional programming, successive convex approximation, branch-and-bound algorithm, and alternating optimization. Experimental results validate the superiority of our algorithm over several baseline methods.

The rapid proliferation of emerging applications, such as the Metaverse and Artificial Intelligence Generated Content (AIGC), demands extensive computational resources, ultra-low latency, and scalable user support. Traditional terrestrial Mobile Edge Computing (MEC) systems cannot fully satisfy these stringent requirements due to limited coverage and constrained resources. This paper proposes a Satellite-Terrestrial Integrated Network-based Mobile Edge Computing (SMEC) system explicitly positioned within an innovative edge-satellite-cloud continuum, incorporating terrestrial edge servers, satellite servers, and terrestrial cloud servers into a unified resource allocation framework. Unlike previous works, which typically oversimplify system complexities, our heterogeneous SMEC architecture explicitly models diverse user-server interactions, satellite energy constraints, and realistic multi-objective trade-offs among latency, energy consumption, and user experience, which, however, introduces challenging non-convex and discrete variables. We formulate this realistic and complex resource allocation problem as a Mixed-Integer Non-Convex Problem (MINCP) and propose the Connection-constrained SMEC Resource Allocation (CSRA) algorithm. CSRA innovatively integrates Block Coordinate Descent (BCD), Successive Convex Approximation (SCA), and advanced Fractional Programming (FP) techniques with significant algorithmic enhancements to accelerate the convergence speed and reduce the computational overhead. Simulation results demonstrate that the CSRA algorithm significantly outperforms benchmark methods, underscoring its practical effectiveness and methodological robustness.

AI foundation models have recently demonstrated impressive capabilities across a wide range of tasks. Fine-tuning (FT) is a method of customizing a pre-trained AI foundation model by further training it on a smaller, targeted dataset. In this paper, we initiate the study of the Privacy-Preserving Parameter-Efficient FT (P3EFT) framework, which can be viewed as the intersection of Parameter-Efficient FT (PEFT) and Privacy-Preserving FT (PPFT). PEFT modifies only a small subset of the model’s parameters to achieve FT (i.e., adapting a pre-trained model to a specific dataset), while PPFT uses privacy-preserving technologies to protect the confidentiality of the model during the FT process. There have been many studies on PEFT or PPFT, but very few on their fusion, which motivates our work on P3EFT to achieve both parameter efficiency and model privacy. To exemplify our P3EFT, we present thePrivTunerscheme, which incorporates Fully Homomorphic Encryption (FHE) enabled privacy protection into LoRA (short for “Low-Rank Adapter”), a popular PEFT solution published in ICLR 2021 [1]. Intuitively speaking, PrivTuner allows the model owner and the external data owners to collaboratively implement PEFT with encrypted data. After describing PrivTuner in detail, we further investigate its energy consumption and privacy protection. Then, we consider a PrivTuner system over wireless communications and formulate a joint optimization problem to adaptively minimize energy while maximizing privacy protection, with the optimization variables including FDMA bandwidth allocation, wireless transmission power, computational resource allocation, and privacy protection. A resource allocation algorithm is devised to solve the problem. Experiments demonstrate that our algorithm can significantly reduce energy consumption while adapting to different privacy requirements.

The Metaverse emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the Metaverse to offer a highly immersive user experience. As mobility is emphasized in the Metaverse context, VR devices reduce their weights at the sacrifice of local computation abilities. In this paper, for a system consisting of a Metaverse server and multiple VR users, we consider two cases of (i) the server generating frames and transmitting them to users, and (ii) users generating frames locally and thus consuming device energy. As Metaverse emphasizes on the accessibility for all users anywhere and anytime, the users can have totally different characteristics, devices and demands. In this paper, the channel access arrangement (including the decisions on frame generation location), and transmission powers for the downlink communications from the server to the users are jointly optimized by our proposed user-centric Deep Reinforcement Learning (DRL) algorithm, namely User-centric Critic with Heterogenous Actors (UCHA). Comprehensive experiments demonstrate that our UCHA algorithm leads to remarkable results under various requirements and constraints.

Virtual Reality (VR) and its reliance on 360° videos are pivotal in delivering a seamless, immersive experience. In the era of emerging 6G technology, where diverse mobile devices are increasingly prevalent, optimizing Quality of Experience (QoE) becomes critical. This is especially true in applications that integrate both VR and non-VR modes. The focus on 6G highlights its capacity to cater to these varying requirements, ensuring high-quality video transmission across different platforms and user experiences. This paper introduces two novel algorithms: Separated Input Differentiated Output (SIDO) and Merged Input Differentiated Output (MIDO). These algorithms are designed to optimize resolution and power allocations in downlink wireless communication, catering to both non-VR and VR users within a chunk-based structure. By encapsulating diverse parameters like subjective perceptual video quality, chunk success rate, and cybersickness into our comprehensive QoE model, we present an approach to address challenges inherent to 360° video optimization. Our deep reinforcement learning algorithms, SIDO and MIDO, further refine the optimization process. Extensive experiments reveal the efficacy of our methodologies. This work, at its core, aims to bridge the gap between technical optimization and user experience, ensuring seamless integration of users.

The growing demand for intelligent mobile applications has made the deployment and operation of Deep Neural Networks (DNNs) on mobile Edge Devices (EDs) increasingly essential. However, the limited computational resources of EDs often result in significant energy consumption and compromised inference quality. To address these challenges, we propose a Partitioned Collaborative Inference (PCI) system that reduces on-device model inference costs by distributing the inference process across multiple EDs and MEC servers. To dynamically model the relationships between computing nodes, inference tasks, and resources, we employ Graph Neural Networks to construct the current state representation of the system. Furthermore, we develop a Cross-Entropy Method (CEM) based Evolutionary Reinforcement Learning algorithm, which leverages negative temporal difference (TD) error as a population fitness metric to generate elite individuals. The elite produces high-quality samples to improve learning efficiency, thereby obtaining optimal partitioned collaborative inference decisions and resource allocation in highly dynamic and complex search spaces. Extensive simulations demonstrate that the proposed approach significantly outperforms existing methods and benchmark schemes, achieving a 57. 5% increase in the inference task completion rate and a 65.7% reduction in system costs.

Ensuring secure and efficient data processing in mobile edge computing (MEC) systems is a critical challenge. While quantum key distribution (QKD) offers unconditionally secure key exchange and homomorphic encryption (HE) enables privacy-preserving data processing, existing research fails to address the comprehensive trade-offs among QKD utility, HE security, and system costs. This paper proposes a novel framework integrating QKD, transciphering, and HE for secure and efficient MEC. QKD distributes symmetric keys, transciphering bridges symmetric encryption, and HE processes encrypted data at the server. We formulate an optimization problem balancing QKD utility, HE security, processing and wireless transmission costs. However, the formulated optimization is non-convex and NPhard. To solve it efficiently, we propose the Quantum-enhanced Homomorphic Encryption resource allocation (QuHE) algorithm. Theoretical analysis proves the proposed QuHE algorithm’s convergence and optimality, and simulations demonstrate its effectiveness across multiple performance metrics.

With the growing need for precise, context-aware Legal Information Retrieval (LIR) tools, this study explores the potential of Retrieval-Augmented Generation (RAG) for retrieving and synthesizing legal content. Using a dataset of legal documents spanning various legal sub-domains, we implement and evaluate the performance of various RAG configurations. By benchmarking each approach using precision and recall, we identify solutions that are suitable for professional deployment. Our results provide insight into how RAG addresses key challenges in legal information retrieval, such as mitigating hallucinations and improving the quality of results. By exploring different RAG techniques and their impact on performance, this research provides a pathway to integrate effective AI-driven solutions into professional legal environments.

Dry beans are a widely consumed crop with distinct species, each possessing unique characteristics. Accurate classification is essential for quality control and efficient crop management. This study explores the multiclass classification of dry beans using various machine learning techniques, focusing on the impact of preprocessing methods-MinMax Scaler, Standard Scaler, and Robust Scaler-on model performance. Extensive experiments were conducted, with particular emphasis on the Light Gradient Boosting Machine (LGBM) classifier. Results indicate that LGBM consistently outperforms alternative models, including Multilayer Perceptron (MLP), Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Extra Tree. When utilizing the MinMax Scaler, the LGBM classifier achieved an accuracy of 96 %, precision of 96 %, recall of 95.80 %, and an F1-score of$\mathbf{9 5. 5 7 \%}$. These findings highlight LGBM's effectiveness in accurately classifying dry beans while demonstrating the critical role of preprocessing techniques in optimizing model performance. Among the tested scalers, the MinMax Scaler consistently produced the highest-performing models, whereas the Standard Scaler exhibited slightly reduced performance on specific metrics. The Robust Scaler showed comparable results to the MinMax Scaler, reinforcing its suitability for handling outliers. These insights emphasize the importance of selecting an appropriate preprocessing technique based on dataset characteristics. The integration of the LGBM classifier with optimized preprocessing methods presents a powerful approach for dry bean classification, enabling precise quality assessment and informed crop management. These findings contribute to advancing machine learning applications in agriculture, offering practical guidance for researchers and industry professionals in optimizing classification models for agricultural data analysis.

In the digital age, credit card fraud detection has emerged as a critical area requiring robust security measures to protect consumers and financial institutions alike. This paper introduces HE-GCN, a novel framework that leverages Homomorphic Encryption (HE) and Graph Convolutional Networks (GCNs) to enhance the accuracy of fraudulent transaction identification while preserving sensitive data privacy. Utilizing a dataset comprising 284,807 transactions from European cardholders in September 2013, we address the challenge of class imbalance, with fraudulent transactions representing a mere 0.172% of the total. Our results demonstrate that the HE-GCN model effectively captures the underlying patterns in the encrypted data, achieving an impressive accuracy of 99.92% after 50 training epochs. The training dynamics reveal a rapid convergence, showcasing the model’s capability to generalize well despite the encrypted environment. Furthermore, our evaluation metrics highlight a perfect classification performance for normal transactions, while also indicating areas for improvement in detecting fraud cases, with a precision of 0.85 and a recall of 0.68. The findings underscore the potential of HE-GCN as a powerful tool for real-world applications, ensuring that privacy-preserving fraud detection can be achieved without compromising accuracy.

The rapid adoption of photovoltaic (PV) solar cells necessitates robust methods for anomaly detection and fault diagnosis to ensure optimal performance and longevity. This paper presents a comprehensive approach to identifying anomalies in PV solar cells using a combination of deep learning and machine learning techniques. We utilize a comprehensive dataset comprising 1191 validated images across two classes to train our model. We employ a Vision Transformer (ViT) model, specifically the ViT-B16 architecture, to extract high-dimensional features from PV cell images, achieving a training accuracy of 100% and a validation accuracy of 88.89%. Additionally, we explore the efficacy of Decision Tree classifier, which demonstrate exceptional performance with testing accuracies of 99.16%. Our results, validated through t-SNE visualizations and confusion matrices, highlight the potential of these methods in accurately detecting and classifying anomalies in PV systems. This study not only advances the field of PV anomaly detection but also provides a scalable and efficient framework for real-world applications.

Nowadays, Generative Artificial Intelligence (GenAI) tools or trainable agents can craft synthetic media (hereafter referred to as deepfakes) in the form of realistic texts, images, videos, and audios, incorporating events or things that never occurred in real life. These GenAI tools empower marketers and malicious actors to create deepfakes, both authorized and weaponized multimedia, which allows them to include celebrities without appearing in front of cameras or creating seductive phishing scams. Although GenAI tools can reduce the cost of content construction, they enable new risky opportunities (e.g., deepfake phishing and cyberbullying) that negatively impact netizens’ learning and (dis)trust behaviors in cyberspace. To address such risks, this study proposes a Multi-Criteria-Multi-Decision-Makers (MCMDM)-based Deepfake Risk Assessment Framework (DeepFakeR-MF) to evaluate determinant factors that impact the cyber (dis)trust behaviors of netizens in deepfakes. Moreover, DeepFakeR-MF deploys a combination of a novel optimized spherical fuzzy analytic hierarchy process method and a game theory-based MCMDM approach to prioritize and recommend alternative strategies that can be taken by five management sectors (e.g., industrial enterprises, governmental organizations, media outlets, social non-profit, and educational institutes) to mitigate GenAI-associated risks. Then, we collect 100 experts’ judgments by analyzing their responses to our questionnaire and prioritize the importance of determinant factors considering their preferences. To validate the prioritized factors on the performance of DeepFakeR-MF, we conduct a sensitivity analysis applying Monte Carlo statistical modeling. Finally, our results confirm that DeepFakeR-MF provides effective strategic alternatives for policymakers, educators, media professionals, engineers, and netizens, hopefully reducing the socio-economic risks of deepfakes.

ABSTRACT Context The rise of artificial intelligence of things (AloT) has enabled smart cities and industries, and UAV‐assisted edge computing networks are an important technology to support the above scenarios. UAV‐assisted refers to leveraging UAVs as a dynamic, flexible infrastructure to assist edge network data processing and communication tasks. Multiple UAVs can use their own resources, and collaborate edge servers to train artificial intelligence (Al) models. Objective Compared with cloud‐based collaborative computing scenarios, UAV‐assisted edge collaborative learning can reduce training and inference delays and improve user satisfaction. However, UAV‐assisted edge networks scenario brings new challenges in terms of transmission burden and energy consumption. Method This paper proposes a prototype‐based joint optimization and training software system. The system consists of an optimization module and a training module. The optimization module first models an optimization problem including energy consumption and prototype error. Then it solves the optimization problem by problem transformation and plans the location of each UAV given the objects' position. After UAVs fly to the designated area and complete data collection, UAVs and the edge server train a model according to the proposed prototype‐based collaborative training module. Our training module enables multiple UAVs and an edge server to collaboratively train a model by lightweight prototype transmission and prototype aggregation. We also prove the convergence of the proposed collaborative training method. Results Results show our method reduces prototype error and energy consumption by at least 12.31% and improves model accuracy by 3.62% with a little communication burden. Conclusion Finally, we verify system performance through experiments.

Metaverse is considered to be the evolution of the next-generation networks, providing users with experience sharing at the intersection between physical and digital. Augmented reality (AR) is one of the primary supporting technologies in the Metaverse, which can seamlessly integrate real-world information with virtual world information to provide users with an immersive interactive experience. Extraordinarily, AR has brought new opportunities for assisting safe driving. Nevertheless, achieving efficient execution of AR tasks and increasing system revenue are the main challenges faced by the Metaverse’s AR in-vehicle applications. To address these challenges, in this paper, we are the first to propose an efficient resource allocation framework for AR-empowered vehicular edge Metaverse to improve system utility. For this purpose, we formulate an optimization problem featuring multidimensional control to concurrently maximize data utility at the Metaverse operator side and minimize energy consumption at the vehicles’ side, which jointly considers the computational resource allocation on the Metaverse server, and AR vehicles’ CPU frequency, transmit power, and computation model size. Notwithstanding, the major impediment is how to design an efficient algorithm to obtain the solutions of the optimization. Wherefore, we do this by decoupling the optimization variables. We first derive the optimal computation model size by the binary search, followed by obtaining the optimal power allocation by the bisection method and finding a closed-form solution to the optimal CPU frequency of AR vehicles, and finally, attain the optimal allocation of computational resource on the server by the Lagrangian dual method. To estimate the performance of our proposed scheme, we establish three baseline schemes as a comparison, and simulation results manifest that our proposed scheme can balance the operator’s reward and the energy consumption of vehicles.

Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of specific users’ data from trained global Federated Learning (FL) models. A straightforward FU method involves removing the unlearned user-specified data and subsequently obtaining a new global FL model from scratch with all remaining user data, a process that unfortunately leads to considerable overhead. To enhance unlearning efficiency, a widely adopted strategy employs clustering, dividing FL users into clusters, with each cluster maintaining its own FL model. The final inference is then determined by aggregating the majority vote from the inferences of these sub-models. This method confines unlearning processes to individual clusters for removing the training data of a particular user, thereby enhancing unlearning efficiency by eliminating the need for participation from all remaining user data. However, current clustering-based FU schemes mainly concentrate on refining clustering to boost unlearning efficiency but without addressing the issue of the potential information leakage from FL users’ gradients, a privacy concern that has been extensively studied. Typically, integrating secure aggregation (SecAgg) schemes within each cluster can facilitate a privacy-preserving FU. Nevertheless, crafting a clustering methodology that seamlessly incorporates SecAgg schemes is challenging, particularly in scenarios involving adversarial users and dynamic users. In this connection, we systematically explore the integration of SecAgg protocols within the most widely used federated unlearning scheme, which is based on clustering, to establish a privacy-preserving FU framework, aimed at ensuring privacy while effectively managing dynamic user participation. Comprehensive theoretical assessments and experimental results show that our proposed scheme achieves comparable unlearning effectiveness, alongside offering improved privacy protection and resilience in the face of varying user participation.

Privacy protection and poisoning resilience are important concerns for federated learning (FL). During a relatively long period, the corresponding solutions are regarded as orthogonal and investigated separately. Unfortunately, due to the increasingly complex structure and ever-growing parameter dimensions of the models to be trained, the forthright coupling of existing differential privacy and Byzantine resilience techniques has been proved incompatible with FL. This emerging problem prompts us to give serious thought to jointly guaranteeing data privacy and model integrity. Besides, worse still, the multi-task characteristic and data imbalance of heterogeneous FL inevitably introduce huge variances, which make privacy-preserving under acceptable accuracy loss even more complicated, not to mention efficient and agile poisoning resistance. Against this bothersome situation, we propose a community-oriented secure heterogeneous FL (CoS-HFL) framework to provide guaranteed privacy protection and significant model robustness simultaneously. CoS-HFL includes two parts: community-oriented duplex privacy amplification and credit-based poisoning resistance. The former copes with potential leakage threats with both uplink and downlink obfuscations. The latter further actively thwarts poisoning attacks based on credibility evaluation. Furthermore, we conduct experiments on benchmark datasets to highlight the performance of CoS-HFL in terms of privacy amplification, poisoning resistance, and learning accuracy under adversarial and heterogeneous environments.

Compared with synchronous federated learning (FL), asynchronous FL (AFL) has attracted more and more attention in edge computing (EC) fields because of its strong adaptability to heterogeneous application scenarios. However, the non-independent and identically distributed (Non-IID) data across devices and the staleness-aware estimation of unreliable wireless connections and limited edge resources make it much more difficult to achieve better AFL-related applications. To handle this problem, we propose anAdaptiveStaleness-awareMomentumAcceleratedAFL(ASMAFL) algorithm to reduce the resources consumption of heterogeneous wireless communication EC (WCEC) scenarios, as well as decrease the negative impact of Non-IID data for model training. Specifically, we first introduce the staleness-aware parameter and a unified momentum gradient descent (GD) framework to reformulate AFL. Then, we establish global convergence properties of AFL, derive an upper bound on AFL convergence rate, and find that the bound is related to the staleness-aware parameter and Non-IIDness. Next, we formulate the bound into a minimization problem of resource consumption under given model accuracy, and the corresponding staleness-aware parameter of devices will be recomputed after each asynchronous aggregation to eliminate the differences of local models’ contribution to global model aggregation. Finally, extensive experiments are carried out to validate the superiority of ASMAFL in model accuracy, convergence rate, resources consumption, Non-IID issue, etc.

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods.

Extensive studies have revealed that deep neural networks (DNNs) are vulnerable to adversarial attacks, especially black-box ones, which can heavily threaten the DNNs deployed in the real world. Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existing black-box attack methods still suffer from unsatisfactory performance due to the vast number of queries needed to optimize desired perturbations. Besides, the other critical challenge is that adversarial examples built in a noise-adding manner are abnormal and struggle to successfully attack robust models, whose robustness is enhanced by adversarial training against small perturbations. There is no doubt that these two issues mentioned above will significantly increase the risk of exposure and result in a failure to dig deeply into the vulnerability of DNNs. Hence, it is necessary to evaluate DNNs' fragility sufficiently under query-limited settings in a non-additional way. In this paper, we propose the Spatial Transform Black-box Attack (STBA), a novel framework to craft formidable adversarial examples in the query-limited scenario. Specifically, STBA introduces a flow field to the high-frequency part of clean images to generate adversarial examples and adopts the following two processes to enhance their naturalness and significantly improve the query efficiency: a) we apply an estimated flow field to the high-frequency part of clean images to generate adversarial examples instead of introducing external noise to the benign image, and b) we leverage an efficient gradient estimation method based on a batch of samples to optimize such an ideal flow field under query-limited settings. Compared to existing score-based black-box baselines, extensive experiments indicated that STBA could effectively improve the imperceptibility of the adversarial examples and remarkably boost the attack success rate under query-limited settings.

Reversible data hiding in encrypted images (RDHEI) has been recognized as an effective method for overcoming management difficulties within picture archiving and communication system (PACS). However, most existing RDHEI algorithms still encounter notable challenges when applied to the PACS, specifically in terms of their key management, embedding capacity, and security. This paper introduces a novel framework and corresponding algorithm for reversible data hiding in encrypted medical images (RDHEMI) to bridge this gap. The framework employs a unique key for each patient and maintains consistency in the key linked to patient images regardless of changes in doctor, thereby addressing key management challenges. In the proposed algorithm, Huffman tree coding (HTC) integrates Huffman coding with innovative leaf-to-leaf coding, achieving a better compression performance for medical images than move-to-front (MTF) cache and Huffman coding, as medical images contain more smooth areas. Count-encryption (CE) produces encryption keys according to the frequency of encryption occurrences for an image and ensures a peak signal-to-noise ratio under 8 dB for multiple encryptions with the same key, enhancing the algorithm’s resistance to attacks. The experimental results demonstrate that the proposed algorithm achieves high security to counter various attacks and outperforms existing algorithms in terms of the time complexity and embedding capacity, with an improvement of 0.21 bpp.

The emergence of 5G technology has enabled the development of Metaverse applications that provide users with immersive experiences through augmented reality (AR) devices, and the integration of federated learning (FL) with the Metaverse AR (MAR) systems can enable many edge intelligence services in 5G. However, the presence of nonindependent and identically distributed (Non-IID) data across all AR users' devices, coupled with limited edge communication resources, makes it challenging to achieve human-centric Metaverse-related applications such as target detection or image classification that combine virtual content with real-world. To address these challenges, we propose a novel adaptive resource-efficient Metaverse-based FL (AMFL) algorithm for AR applications that mitigates the negative effect of Non-IID data and reduces resource costs as well as improves the quality of experience (QoE). We first analyze the impact of wireless communication factors such as CPU frequency, bandwidth, and transmission power on FL training performance by a toy example in the MAR systems. Based on this analysis, furthermore, we establish a Non-IID degree, model accuracy, and resource consumption-related QoE maximization problem under given resource budgets, which is a stochastic optimization problem with strongly coupled variables, including bandwidth, CPU frequency, and transmission power. Guided by the theoretical analysis, to solve this issue, AMFL employs a deep reinforcement learning (DRL)-based method to adaptively allocate resources. Numerical results demonstrate that AMFL can significantly improve the QoE by up to 30.28%, and reduce communication round and energy costs by up to 81.08% and 72.20%, respectively, even under the worst Non-IID case, compared to benchmarks.

Play-to-earn (P2E) games have been gaining popularity as they enable players to earn in-game tokens which can be translated to real-world profits. With the advancements in augmented reality (AR) technologies, AR play-to-earn games become compute-intensive. In-game graphical scenes need to be offloaded from mobile devices to an edge server for computation. In this work, we consider an optimization problem where the Mobile edge computing Service Provider (MSP)’s objective is to reduce downlink transmission latency of in-game graphics, the latency of uplink data transmission, and the worst-case (greatest) battery charge expenditure of user equipments (UEs), while maximizing the worst-case (lowest) UE resolution-influenced in-game earning potential through optimizing the downlink UE-Mobile edge computing Base Station (UE-MBS) assignment, downlink, and the uplink transmission power selection. The downlink and uplink transmissions are executed asynchronously. We propose a Multi-Asynchronous-Agent, Loss-Sharing (MALS) reinforcement learning model to tackle the asynchronous and asymmetric problem. We then compare the MALS model with other baseline models and show its superiority over other methods. Finally, we conduct multi-variable optimization weighting analyses and show the viability of using our proposed MALS algorithm to tackle joint optimization problems.

Digital Twinning of physical world scenes onto the Metaverse is necessary for augmented reality (AR)-assisted driving. In AR-assisted driving, physical environment scenes are first captured by AR vehicles and are uploaded to the Metaverse for the construction of the Metaverse Map. However, the development of AR-assisted driving applications invites adversaries. These attackers may place adversarial patches on physical objects, seeking to contort the Metaverse Map. As real-time, accurate detection of adversarial patches is compute-intensive, these physical world scenes have to be offloaded to the Metaverse Map Base Station (MMBS) for computation. Therefore, we considered a scenario where AR vehicles capture physical world scenes and upload these scenes in real-time to the MMBSs. We formulated an optimization problem where the MMSP’s objective is to maximize adversarial patch detection mean Average Precision (mAP), while minimizing the computed AR scene uplink transmission latency and minimizing the worst-case (largest) AR vehicle’s uplink transmission battery charge consumption, through optimizing the AR vehicle-MMBS allocation, AR vehicle uplink scene resolution selection, and AR vehicle uplink power output selection. We proposed a Heterogeneous Action (HA) algorithm to tackle the proposed problem. Extensive experiments show our HA models outperforms baseline models when compared against key metrics.

In this paper, we investigate hybrid precoder design to enhance the physical layer security of the millimeter-wave (mmWave) system with multiple eavesdroppers. Differing from traditional spatial domain-based schemes, we introduce an extra signal domain dimension and propose a multi-component-based security mechanism for the hybrid precoding system model. The model integrates both signal and spatial domains for designing baseband and analog precoders, ensuring that the received signal at the legitimate user complies specific signal domain characteristics. Consequently, all received energy becomes available for signal reconstruction, whereas the distorted signal at eavesdroppers inevitably forms inter-component interference. By introducing general multi-fractional Fourier transform (GMFRFT) signal domain into the model, we propose a specific GMFRFT-based scheme for subarray architectures. The secrecy performance is analyzed, and power allocation methods between multiple components are designed. Additionally, to optimize the utilization of spatial domain resources, we propose a GMFRFT-based scheme with dynamic subarrays, along with a low-complexity subarray partitioning algorithm. Furthermore, a GMFRFT-based scheme for fully connected architectures is investigated. It is verified to be equivalent to a lower-complexity weighted fractional Fourier transform (WFRFT)-based analog beamforming scheme, the efficacy of which hinges on the adherent secrecy provided by WFRFT signals. Both theoretical analysis and numerical results demonstrate the effectiveness of the proposed schemes.

Semantic communication is a new paradigm that aims at providing more efficient communication for the next-generation wireless network. It focuses on transmitting extracted, meaningful information instead of the raw data. However, deep learning-enabled image semantic communication models often require a significant amount of time and energy for training, which is unacceptable, especially for mobile devices. To solve this challenge, our paper first introduces a distributed image semantic communication system where the base station and local devices will collaboratively train the models for uplink communication. Furthermore, we formulate a joint optimization problem to balance time and energy consumption on the local devices during training while ensuring effective model performance. An adaptable resource allocation algorithm is proposed to meet requirements under different scenarios, and its time complexity, solution quality, and convergence are thoroughly analyzed. Experimental results demonstrate the superiority of our algorithm in resource allocation optimization against existing benchmarks and discuss its impact on the performance of image semantic communication systems.

The convergence of blockchain, Metaverse, and non-fungible tokens (NFTs) brings transformative digital opportunities alongside challenges like privacy and resource management. Addressing these, we focus on optimizing user connectivity and resource allocation in an NFT-centric and blockchain-enabled Metaverse in this paper. Through user work-offloading, we optimize data tasks, user connection parameters, and server computing frequency division. In the resource allocation phase, we optimize communication-computation resource distributions, including bandwidth, transmit power, and computing frequency. We introduce the trust-cost ratio (TCR), a pivotal measure combining trust scores from users’ resources and server history with delay and energy costs. This balance ensures sustained user engagement and trust. The DASHF algorithm, central to our approach, encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a novel fractional programming technique from a recent IEEE JSAC paper [2]. The most challenging part of DASHF is to rewrite an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via carefully designed transformations, in order to be solved by SDR and the Hungarian algorithm. Extensive simulations validate the DASHF algorithm’s efficacy, revealing critical insights for enhancing blockchain-Metaverse applications, especially with NFTs.

Semantic communication (SemCom) is an emerging way that aims to improve communication efficiency based on the semantics of content, which relies on the knowledge base (KB) and is usually dedicated to specific tasks or datasets. To improve the adaptability of SemCom systems on unknown datasets, we propose a post-deployment Fine-Tunable Semantic Communication (FTSC) system for image transmission. Towards an adaptive and efficient SemCom system, our research consists of the framework design of FTSC and its system optimization study. Firstly, the generalizability study is conducted based on a two-layer hierarchical vector quantized-variational autoencoder (VQ-VAE-2). Unlike traditional SemCom that can work on limited pretrained datasets, FTSC adapts to varied input data post-deployment, enhancing practicality in diverse communication scenarios. This system incorporates two novel fine-tuning methods: Decoder Fine-Tuning (DFT) and Latent Space-based Decoder Fine-Tuning (LSDFT). DFT updates the decoder for new images post-deployment without transmitting gradients, while LSDFT eliminates the need for raw image transmission during fine-tuning. Secondly, we study the system optimization of the proposed FTSC framework to improve the efficiency of communication resource allocation with the concern of recovery quality, time delay, and energy cost in downlink transmissions. Extensive experiments demonstrate the superiority of FTSC over Joint Photographic Experts Group (JPEG) and Joint Source-Channel Coding (JSCC) across various datasets and noise levels, and both DFT and LSDFT significantly enhance image recovery on unfamiliar datasets compared to pre-trained models.

Federated Learning (FL) has been widely used to train shared machine learning models while addressing the privacy concerns. When deployed in wireless networks, bandwidth resources limitation is a key issue, thereby necessitating device scheduling and bandwidth allocation. It is challenging to carry out device scheduling due to the large combinatorial search space. Besides, the heterogeneous computing capabilities and uncertain channel states of wireless devices complicate the design of a bandwidth allocation method. In this paper, we propose a joint device scheduling and bandwidth allocation framework for implementing FL in wireless networks. Specifically, deep reinforcement learning (DRL) is employed to conduct device scheduling. To this end, the state space, action space, and reward function of DRL are carefully defined for a typical FL system. Long short-term memory (LSTM) is adopted as the DRL agent to analyze the sequential input data. Given the scheduled devices of each global iteration, the proposed bandwidth allocation method aims to minimize the weighted sum of the time delay and energy consumption. Numerical experiments on both independent and identically distributed (IID) and non-IID datasets demonstrate that the proposed framework enables FL to reach the desired accuracy with low time delay and energy consumption.

The Metaverse has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. Federated learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. Due to privacy concerns and the limited computation resources on mobile devices, we incorporate FL into MAR systems of the Metaverse to train a model cooperatively. Besides, to balance the trade-off between energy, execution latency and model accuracy, thereby accommodating different demands and application scenarios, we formulate an optimization problem to minimize a weighted combination of total energy consumption, completion time and model accuracy. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, CPU frequency and video frame resolution for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm has better performance (in terms of energy consumption, completion time and model accuracy) under different weight parameters compared to existing benchmarks.

Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial examples (AEs), which brings a huge security risk to the application of DNNs, especially for the AI models developed in the real world. To impede the process of fully exploiting the vulnerabilities of existing DNNs and further improving their robustness in the face of such malicious inputs, many attack methods have been proposed to build AEs. Despite the significant progress that has been made recently, existing attack methods still suffer from the unsatisfactory performance of escaping from being detected by naked human eyes due to the formulation of AE heavily relying on a noise-adding manner. Such mentioned challenges will significantly increase the risk of exposure and result in an attack to be failed. Therefore, in this paper, we propose the Salient Spatially Transformed Attack (SSTA), a novel framework to craft imperceptible AEs, which enhance the stealthiness of AEs by estimating a smooth spatial transform metric on a most critical area to generate AEs instead of adding external noise to the whole image. Compared to SOTA baselines, extensive experiments indicated that SSTA could effectively improve the imperceptibility of the AEs while maintaining a 100% attack success rate.

The concept of the Metaverse has garnered growing interest from both academic and industry circles. The decentralization of both the integrity and security of digital items has spurred the popularity of play-to-earn (P2E) games, where players are entitled to earn and own digital assets which they may trade for physical-world currencies. However, these computationally-intensive games are hardly playable on resource-limited mobile devices and the computational tasks have to be offloaded to an edge server. Through mobile edge computing (MEC), users can upload data to the Metaverse Service Provider (MSP) edge servers for computing. Nevertheless, there is a trade-off between user-perceived in-game latency and user visual experience. The downlink transmission of lower-resolution videos lowers user-perceived latency while lowering the visual fidelity and consequently, earnings of users. In this paper, we design a method to enhance the Metaverse-based mobile augmented reality (MAR) in-game user experience. Specifically, we formulate and solve a multi-objective optimization problem. Given the inherent NP-hardness of the problem, we present a low-complexity algorithm to address it, mitigating the trade-off between delay and earnings. The experiment results show that our method can effectively balance the user-perceived latency and profitability, thus improving the performance of Metaverse-based MAR systems.

As mobile devices increasingly become focal points for advanced applications, edge computing presents a viable solution to their inherent computational limitations, particularly in deploying large language models (LLMs). However, despite the advancements in edge computing, significant challenges remain in efficient training and deploying LLMs due to the computational demands and data privacy concerns associated with these models. This paper explores a collaborative training framework that integrates mobile users with edge servers to optimize resource allocation, thereby enhancing both performance and efficiency. Our approach leverages parameter-efficient fine-tuning (PEFT) methods, allowing mobile users to adjust the initial layers of the LLM while edge servers handle the more demanding latter layers. Specifically, we formulate a multi-objective optimization problem to minimize the total energy consumption and delay during training. We also address the common issue of instability in model performance by incorporating stability enhancements into our objective function. Through novel fractional programming technique, we achieve a stationary point for the formulated problem. Simulations demonstrate that our method reduces the energy consumption as well as the latency, and increases the reliability of LLMs across various mobile settings.

In the rapidly evolving landscape of the Metaverse, enhanced by blockchain technology, the efficient processing of data has emerged as a critical challenge, especially in wireless communication systems. Addressing this need, our paper introduces the innovative concept of data processing efficiency (DPE), aiming to maximize processed bits per unit of resource consumption in blockchain-empowered Metaverse environments. To achieve this, we propose the DPE-Aware User Association and Resource Allocation (DAUR) algorithm, a tailored solution for these complex systems. The DAUR algorithm transforms the challenging task of optimizing the sum of DPE ratios into a solvable convex optimization problem. It uniquely alternates the optimization of key variables like user association, work offloading ratios, task-specific computing resource distribution, bandwidth allocation, user power usage ratios, and server computing resource allocation ratios. Our extensive numerical results demonstrate the DAUR algorithm's effectiveness in DPE.

With the rapid advancement of large models and mobile edge computing, transfer learning, particularly through fine-tuning, has become crucial for adapting models to downstream tasks. Traditionally, this requires users to share their data with model owners for fine-tuning, which is not only costly but also raises significant privacy concerns. Furthermore, fine-tuning large-scale models is computationally intensive and often impractical for many users. To tackle these challenges, we introduce a system that combines offsite-tuning with physical-layer security, which provides local data owners with a lightweight adapter and a compressed emulator. Data owners then fine-tune the adapter locally and securely send it back to the model owners through a confidential channel for integration, ensuring privacy and resource conservation. Our paper focuses on optimizing computational resource allocation among data owners and the large model owner deployed on edge, and on the compression ratio of adapters. We incorporate a secrecy uplink channel to maximize the utility that we defined while minimizing system costs like energy consumption and delay. The optimization uses the Dinkelbach algorithm, fractional programming, successive convex approximation and alternating optimization. Experiments demonstrate our algorithm's superiority over baseline methods.

Hyperspectral image classification methods based on graph convolution network have received extensive attention. However, the traditional distance metric is difficult to fully represent the spectral variability and uncertainty in hyperspectral images. In order to alleviate this problem, a multi-scale fuzzy graph convolutional network is constructed for hyperspectral image classification. In detail, the SLIC algorithm is used to perform superpixel segmentation of hyperspectral images. Each superpixel is regarded as a graph node, and a fuzzy measurement mechanism is introduced to measure the similarity between two nodes to describe the uncertainty between pixels in the hyperspectral image, so as to construct a fuzzy graph convolution. Subsequently, the fuzzy graph convolution is extended to multi-scale to capture the rich contextual information within the hyperspectral image. In the training process, the pixel-level features are integrated into the superpixel-level graph update process to establish the connection between the pixel level and the superpixel level. Finally, experimental results on two publicly available hyperspectral image datasets show that the proposed network outperforms other representative peers.

In the upcoming 6G era, vehicular networks are shifting from simple Vehicle-to-Vehicle (V2V) communication to the more complex Vehicle-to-Everything (V2X) connectivity. At the forefront of this shift is the incorporation of Large Language Models (LLMs) into vehicles. Known for their sophisticated natural language processing abilities, LLMs change how users interact with their vehicles. This integration facilitates voice-driven commands and interactions, departing from the conventional manual control systems. However, integrating LLMs into vehicular systems presents notable challenges. The substantial computational demands and energy requirements of LLMs pose significant challenges, especially in the constrained environment of a vehicle. Additionally, the time-sensitive nature of tasks in vehicular networks adds another layer of complexity. In this paper, we consider an edge computing system where vehicles process the initial layers of LLM computations locally, and offload the remaining LLM computation tasks to the Roadside Units (RSUs), envisioning a vehicular ecosystem where LLM computations seamlessly interact with the ultra-low latency and high-bandwidth capabilities of 6G networks. To balance the trade-off between completion time and energy consumption, we formulate a multi-objective optimization problem to minimize the total cost of the vehicles and RSUs. The problem is then decomposed into two sub-problems, which are solved by sequential quadratic programming (SQP) method and fractional programming technique. The simulation results clearly indicate that the algorithm we have proposed is highly effective in reducing both the completion time and energy consumption of the system.

In the rapidly evolving landscape of large language models (LLMs) and mobile edge computing for 6G, the need for efficient service delivery to mobile users with constrained computational resources has become paramount. Addressing this, our paper delves into a collaborative framework for model training where user data and model adapters are shared with servers to optimize performance. Within this framework, users initially update the first several layers of the adapters while freezing the other layers of them, leveraging their local datasets. Once this step is complete, these partially trained parameters are transmitted to servers. The servers, equipped with more robust computational capabilities, then update the subsequent layers. After this training, they send the enhanced parameters back to the users. This collaborative training approach ensures that mobile users with limited computational capacities can still benefit from advanced LLM services without being burdened by exhaustive computations. Central to our methodology is the DASHF algorithm, which encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a pioneering fractional programming technique from a recent IEEE JSAC paper [1]. The crux of DASHF is its capability to reformulate an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via meticulously crafted transformations, making it solvable by SDR and the Hungarian algorithm. Through extensive simulations, we demonstrate the effectiveness of the DASHF algorithm, offering significant insights for the advancement of collaborative LLM service deployments.

This review paper explores the powerful convergence of AI foundation models and 6G wireless communication networks, emphasizing their symbiotic relationship and transformative potential. It investigates the advancements within 6G networks, highlighting key areas such as federated learning, blockchain integration, and mobile edge computing. The paper discusses how AI foundation models can enhance 6G communications and vice versa, outlining applications such as the Internet of Vehicles (IoVs) and the Metaverse, aiming to open challenges and future research directions, and underscoring the profound impact of integrating these technologies.

AI-Generated Content (AIGC), as a novel manner of providing Metaverse services in the forthcoming Internet paradigm, can resolve the obstacles of immersion requirements. Concurrently, edge computing, as an evolutionary paradigm of computing in communication systems, effectively augments real-time interactive services. In pursuit of enhancing the accessibility of AIGC services, the deployment of AIGC models (e.g., diffusion models) to edge servers and local devices has become a prevailing trend. Nevertheless, this approach faces constraints imposed by battery life and computational resources when tasks are offloaded to local devices, limiting the capacity to deliver high-quality content to users while adhering to stringent latency requirements. So there will be a tradeoff between the utility of AIGC models and offloading decisions in the edge computing paradigm. This paper presents a joint optimization scheme for offloading decisions, computation time, and diffusion steps of the diffusion models in the reverse diffusion stage. Moreover, we take the average error into consideration as the metric for evaluating the quality of the generated results. Experimental outcomes definitively show that the algorithm put forward outperforms baseline methods in terms of joint optimization performance.

The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm inte-grating 6G Mobile Edge Computing (MEC) with foundation models, specifically designed to enhance local task performance on user equipment (UE). Central to our approach is the innovative Emulator-Adapter architecture, segmenting the foundation model into two cohesive modules. This design not only conserves computational resources but also ensures adaptability and fine-tuning efficiency for downstream tasks. Additionally, we introduce an advanced resource allocation mechanism that is fine-tuned to the needs of the Emulator-Adapter structure in decentralized settings. To address the challenges presented by this system, we employ a hybrid multi-agent Deep Reinforcement Learning strategy, adept at handling mixed discrete-continuous action spaces, ensuring dynamic and optimal resource allocations. Our comprehensive simulations and validations underscore the practical viability of our approach, demonstrating its robustness, efficiency, and scalability. Collectively, this work offers a fresh perspective on deploying foundation models and balancing computational efficiency with task proficiency.

Federated Learning (FL) is a promising machine learning approach for Internet of Things (IoT), but it has to address network congestion problems when the population of IoT devices grows. Hierarchical FL (HFL) alleviates this issue by distributing model aggregation to multiple edge servers. Nevertheless, the challenge of communication overhead remains, especially in scenarios where all IoT devices simultaneously join the training process. For scalability, practical HFL schemes select a subset of IoT devices to participate in the training, hence the notion of device scheduling. In this setting, only selected IoT devices are scheduled to participate in the global training, with each of them being assigned to one edge server. Existing HFL assignment methods are primarily based on search mechanisms, which suffer from high latency in finding the optimal assignment. This paper proposes an improved K-Center algorithm for device scheduling and introduces a deep reinforcement learning-based approach for assigning IoT devices to edge servers. Experiments show that scheduling 50% of IoT devices is generally adequate for achieving convergence in HFL with much lower time delay and energy consumption. In cases where reduction in energy consumption (such as in Green AI) and reduction of messages (to avoid burst traffic) are key objectives, scheduling 30% IoT devices allows a substantial reduction in energy and messages with similar model accuracy.

The Metaverse will provide numerous immersive applications for human users, by consolidating technologies like extended reality (XR), video streaming, and cellular networks. Optimizing wireless communications to enable the human-centric Metaverse is important to satisfy the demands of mobile users. In this paper, we formulate the optimization of the system utility-cost ratio (UCR) for the Metaverse over wireless networks. Our human-centric utility measure for virtual reality (VR) applications of the Metaverse represents users’ perceptual assessment of the VR video quality as a function of the data rate and the video resolution and is learned from real datasets. The variables jointly optimized in our problem include the allocation of both communication and computation resources as well as VR video resolutions. The system cost in our problem comprises the energy consumption and delay and is non-convex with respect to the optimization variables. To solve the non-convex optimization, we develop a novel fractional programming technique, which contributes to optimization theory and has broad applicability beyond our paper. Our proposed algorithm for the system UCR optimization is computationally efficient and finds a stationary point to the constrained optimization. Through extensive simulations, our algorithm is demonstrated to outperform other approaches.

With the emergence of the sixth-generation (6G) communication technologies, the Internet of Vehicles (IoV) is rapidly developing with the coordination between intelligent networked vehicles, road infrastructures, and the cloud. However, the openness and dynamic nature of the IoV raise significant security and privacy concerns, highlighting the need for efficient authentication schemes. Conventional authentication schemes are no longer suitable for 6G-enabled IoV due to high latency, single point of failure, and heavy management costs. Additionally, existing literature on multi-domain authentication mainly investigates vehicle mobility, ignoring the challenges posed by vehicle heterogeneity. To fill this gap, we propose a multi-domain authentication scheme with conditional privacy preservation (MACPP) that considers administrative domains (AD) and geographic domains (GD) in the IoV. In MACPP, we design a novel identity-based signature scheme without requiring bilinear pairing for efficient authentication. Additionally, we propose a blockchain-assisted pseudonym management scheme (BAPM) to further improve system security by designing a dynamical sparse Merkle tree structure (DSMT). We demonstrate that the proposed MACPP satisfies the security requirements through an in-depth security analysis. Moreover, the experimental results demonstrate the effectiveness and efficiency of both MACPP and BAPM.

Existing black-box attacks have demonstrated promising potential in creating adversarial examples (AE) to deceive deep learning models. Most of these attacks need to handle a vast optimization space and require a large number of queries, hence exhibiting limited practical impacts in real-world scenarios. In this paper, we propose a novel black-box attack strategy, Conditional Diffusion Model Attack (CDMA), to improve the query efficiency of generating AEs under query-limited situations. The key insight of CDMA is to formulate the task of AE synthesis as a distribution transformation problem, i.e., benign examples and their corresponding AEs can be regarded as coming from two distinctive distributions and can transform from each other with a particular converter. Unlike the conventionalquery-and-optimizationapproach, we generate eligible AEs with direct conditional transform using the aforementioned data converter, which can significantly reduce the number of queries needed. CDMA adopts the conditional Denoising Diffusion Probabilistic Model as the converter, which can learn the transformation from clean samples to AEs, and ensure the smooth development of perturbed noise resistant to various defense strategies. We demonstrate the effectiveness and efficiency of CDMA by comparing it with nine state-of-the-art black-box attacks across three benchmark datasets. On average, CDMA can reduce the query count to a handful of times; in most cases, the query count is only ONE. We also show that CDMA can obtain > 99% attack success rate for untargeted attacks over all datasets and targeted attack over CIFAR-10 with the noise budget of ϵ = 16.

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continues to thrive in this new reality. Existing FL protocol designs have been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this article, we conduct a comprehensive survey on privacy and robustness in FL over the past five years. Through a concise introduction to the concept of FL and a unique taxonomy covering: 1) threat models; 2) privacy attacks and defenses; and 3) poisoning attacks and defenses, we provide an accessible review of this important topic. We highlight the intuitions, key techniques, and fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions toward robust and privacy-preserving FL, and their interplays with the multidisciplinary goals of FL.

Wireless powered mobile edge computing (WPMEC) has been proposed to fulfill the computational energy requirements of massive power-constrained Internet-of-Things (IoT) devices. However, the transmission rate for task offloading and the efficiency of energy transfer are compromised when the wireless connections between the mobile devices and hybrid access point (HAP) are hostile. We proposed to employ the reconfigurable intelligent surface (RIS), an emerging technology that is capable of enhancing wireless connections. Specifically, we consider optimizing a weighted sum of energy and time while guaranteeing the energy harvested from the wireless power transfer (WPT) stage can cover the energy consumption of each mobile device and maintain each device a customized transmission rate to increase the edge server's earning maximally. First, we proposed a RIS-aided wireless powered MEC system design and introduced the earning of the edge server as the metric to evaluate the performance of the system. Then we formulated an optimization problem and proposed an iterative algorithm to maximize the earning received by the edge server. Finally, our numerical results show that RIS can significantly reduce the time and energy cost of the WP-MEC system.

As the essential technical support for Metaverse, Mobile Augmented Reality (MAR) has attracted the attention of many researchers. MAR applications rely on real-time processing of visual and audio data, and thus those heavy workloads can quickly drain the battery of a mobile device. To address such problem, edge-based solutions have appeared for handling some tasks that require more computing power. However, such strategies introduce a new trade-off: reducing the network latency and overall energy consumption requires limiting the size of the data sent to the edge server, which, in turn, results in lower accuracy. In this paper, we design an edge-based MAR system and propose a mathematical model to describe it and analyze the trade-off between latency, accuracy, server resources allocation and energy consumption. Furthermore, an algorithm named LEAO is proposed to solve this problem. We evaluate the performance of the LEAO and other related algorithms across various simulation scenarios. The results demonstrate the superiority of the LEAO algorithm. Finally, our work provides insight into optimization problem in edge-based MAR system for Metaverse.

Semantic communication is deemed as a revolution of Shannon's paradigm in the six-generation (6G) wireless networks. It aims at transmitting the extracted information rather than the original data, which receivers will try to recover. Intuitively, the larger extracted information, the longer latency of semantic communication will be. Besides, larger extracted information will result in more accurate reconstructed information, thereby causing a higher utility of the semantic communication system. Shorter latency and higher utility are desirable objectives for the system, so there will be a trade-off between utility and latency. This paper proposes a joint optimization algorithm for total latency and utility. Moreover, security is essential for the semantic communication system. We incorporate the secrecy rate, a physical-layer security method, into the optimization problem. The secrecy rate is the communication rate at which no information is disclosed to an eavesdropper. Experimental results demonstrate that the proposed algorithm obtains the best joint optimization performance compared to the baselines.

In this paper, we aim to explore the use of uplink semantic communications with the assistance of UAV in order to improve data collection effiicency for metaverse users in remote areas. To reduce the time for uplink data collection while balancing the trade-off between reconstruction quality and computational energy cost, we propose a hybrid action reinforcement learning (RL) framework to make decisions on semantic model scale, channel allocation, transmission power, and UAV trajectory. The variables are classified into discrete type and continuous type, which are optimized by two different RL agents to generate the combined action. Simulation results indicate that the proposed hybrid action reinforcement learning framework can effectively improve the efficiency of uplink semantic data collection under different parameter settings and outperforms the benchmark scenarios.

In this paper, we investigate the semantic-aware efficient sampling policy for remote state estimation in a digital twin (DT) empowered smart factory with multiple wireless sensing devices and an edge server. In this setting, wireless sensing devices must continuously sample the factory states and transmit semantic-aware sensing data to the server. Using the received sensing data, the server builds a realtime DT mapping remotely that analyzes and predicts the events in the factory. Since the DT requires continuous data transmission, maintaining the DT inevitably consumes significant amounts of limited wireless resources. To address this issue, we reduce the required amount of data transmission by making wireless devices only send the semantic-aware sensing data that indicates the occurrence of events, otherwise stay idle. In particular, we first invoke the age of incorrect information (AoII) to measure the semantic of the sensing data, which represents the freshness of the concerned events. Next, we formulate an optimization problem that minimizes the long-term AoII of remote state estimation through the devices deciding whether to sample the factory states at each time slot. To solve this problem, we first transform the original problem into a state-wise constrained Markov decision programming (CMDP) and then propose a soft actor-critic (SAC) based algorithm to learn a sampling policy to take sample actions within the sampling rate constraint, while considering packet error. Simulation results show that, the proposed algorithm can reduce the number of samples by up to 44% compared to the error-based sampling scheme, with the same estimation accuracy.

Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime. As such, the use cases for users will be much more diverse, leading to a mix of 360-degree videos with two types: non-VR and VR 360° videos. This paper presents a novel Quality of Service model for heterogeneous 360° videos with different requirements for frame rates and cybersickness. We propose a frame-slotted structure and conduct frame-wise optimization using self-designed differentiated deep reinforcement learning algorithms. Specifically, we design two structures, Separate Input Differentiated Output (SIDO) and Merged Input Differentiated Output (MIDO), for this heterogeneous scenario. We also conduct comprehensive experiments to demonstrate their effectiveness.

Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world. Deep learning models, however, are shown to be vulnerable to adversarial attacks. Though there are many studies on the model robustness in the context of standard supervised learning, protecting continual learning from adversarial attacks has not yet been investigated. To fill in this research gap, we are the first to study adversarial robustness in continual learning and propose a novel method called Task-Aware Boundary Augmentation (TABA) to boost the robustness of continual learning models. With extensive experiments on CIFAR-10 and CIFAR-100, we show the efficacy of adversarial training and TABA in defending adversarial attacks.

Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted driving, physical environment scenes are first captured by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central Metaverse Map Service Provider (MMSP) will aggregate information from all IoVs to develop a central Metaverse Map. Information from the Metaverse Map can then be downloaded into individual IoVs on demand and be delivered as AR scenes to the driver. However, the growing interest in developing AR assisted driving applications which relies on digital twinning invites adversaries. These adversaries may place physical adversarial patches on physical world objects such as cars, signboards, or on roads, seeking to contort the virtual world digital twin. Hence, there is a need to detect these physical world adversarial patches. Nevertheless, as real-time, accurate detection of adversarial patches is compute-intensive, these physical world scenes have to be offloaded to the Metaverse Map Base Stations (MMBS) for computation. Hence in our work, we considered an environment with moving Internet of Vehicles (IoV), uploading real-time physical world scenes to the MMBSs. We formulated a realistic joint variable optimization problem where the MMSPs' objective is to maximize adversarial patch detection mean average precision (mAP), while minimizing the computed AR scene up-link transmission latency and IoVs' up-link transmission idle count, through optimizing the IoV-MMBS allocation and IoV up-link scene resolution selection. We proposed a Heterogeneous Action Proximal Policy Optimization (HAPPO) (discrete-continuous) algorithm to tackle the proposed problem. Extensive experiments shows HAPPO outperforms baseline models when compared against key metrics.

The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the possibility for highly immersive and interactive socialization. Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, and especially so in the context of socialization. As the virtual world 3D scenes to be rendered are of high resolution and frame rate, these scenes will be offloaded to an edge server for computation. Besides, the metaverse is user-center by design, and human users are always the core. In this work, we introduce a multi-user VR computation offloading over wireless communication scenario. In addition, we devised a novel user-centered deep reinforcement learning approach to find a near-optimal solution. Extensive experiments demonstrate that our approach can lead to remarkable results under various requirements and constraints.

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

Metaverse has become a buzzword recently. Mobile augmented reality (MAR) is a promising approach to providing users with an immersive experience in the Metaverse. However, due to limitations of bandwidth, latency and computational resources, MAR cannot be applied on a large scale in the Metaverse yet. Moreover, federated learning, with its privacy-preserving characteristics, has emerged as a prospective distributed learning framework in the future Metaverse world. This paper proposes a federated learning assisted MAR system via non-orthogonal multiple access for the Metaverse. Additionally, to optimize a weighted sum of energy, latency, and model accuracy, a resource allocation algorithm is devised by setting appropriate transmission power, CPU frequency, and video frame resolution for each user. Experimental results demonstrate that our proposed algorithm achieves an overall good performance compared to a random algorithm and a greedy algorithm.

Utility-oriented communications consider communication quality metrics traditionally used in bit-oriented communication, such as latency, bit error rate (BER), quality of experience (QoE), and quality of service (QoS), and achieves the ideal utility by encompassing evaluation methods for communication quality in existing communication paradigms such as semantic similarity in semantic communication, task completion in Task-Oriented communication (TOC) and quality of collaboration in Goal-Oriented communications (GOC). We present a utility-oriented communication system for leverages 6G-enabled technologies to support real-time synchronization of IoT-collected data to the metaverse, ensuring rapid and accurate updates of digital twins and optimizing network performance.

The increasing popularity of applications like the Metaverse has led to the exploration of new, more effective ways of communication. Semantic communication, which focuses on the meaning behind transmitted information, represents a departure from traditional communication paradigms. As mobile devices become increasingly prevalent, it is important to explore the potential of edge computing to aid the semantic encoding/decoding process, which requires significant computing power and storage capabilities. However, establishing knowledge bases (KBs) for domain-oriented communication can be time-consuming. To address this challenge, this paper proposes a semantic caching model in edge computing system that caches domain-specialized general models and user-specific individual models. This approach has the potential to reduce the time and resources required to establish individual KBs while accurately capturing the semantics behind users' messages, ultimately leading to more efficient and accessible semantic communication.

The Metaverse, an emerging digital space, is expected to offer various services mirroring the real world. Wireless communications for mobile Metaverse users should be tailored to meet the following user characteristics: 1) emphasizing application-specific perceptual utility instead of simply the transmission rate, 2) concerned with energy efficiency due to the limited device battery and energy intensiveness of some applications, and 3) caring about security as the applications may involve sensitive personal data. To this end, this paper incorporates application-specific utility, energy efficiency, and physical-layer security (PLS) into the studied optimization in a wireless network for the Metaverse. Specifically, after introducing utility-energy efficiency (UEE) to represent each Metaverse user's application-specific objective under PLS, we formulate an optimization to maximize the network's weighted sum-UEE by deciding users' transmission powers and communication bandwidths. The formulated problem belongs to the sum-of-ratios optimization, for which prior studies have demonstrated its difficulty. Nevertheless, our proposed algorithm 1) obtains the global optimum for the weighted sum-UEE optimization, via a transform to parametric convex optimization problems, 2) applies to any utility function which is concave, increasing, and twice differentiable, and 3) achieves a linear time complexity in the number of users (the optimal complexity in the order sense). Simulations confirm the superiority of our algorithm over other approaches. We explain that our technique for solving the sum-of-ratios optimization is applicable to other optimization problems in wireless networks and mobile computing.

The rapid development of intelligent machine learning and 5G/6G has led to smart terminal devices for various applications like auto-driving, AR, and smart farms. However, these applications have strict Quality of Service (QoS) requirements, exceeding the capabilities of mobile devices due to limited resources. Mobile edge computing offers a promising solution by enabling task offloading to nearby edge servers with better computing capabilities and a stable energy supply. For mobile edge systems, minimizing overall cost while ensuring QoS for mobile users is a challenging problem to solve effectively.In this paper, we first formulate the performance model for mobile edge systems with real data from deep learning scenarios. Then, we formulate the resource management in mobile edge system problem as a non-convex fractional programming problem with multiple coupled variables to minimize the latency and energy consumption while meeting QoS requirements. To solve the non-convex problem, a novel fractional programming technique is proposed to decouple the variables by considering fractional transformation to greatly reduce the complexity. Then we achieve the jointly optimal solution of the CPU frequency and offloading strategy with successive convex approximation, and Karush-Kuhn-Tucker (KKT) conditions. The experimental results show that our proposed algorithm can achieve cost-effective solutions while meeting QoS requirements over baselines.

This paper introduces the novel utility-oriented communications (UOC) concept and identifies its importance for 6G wireless technology. UOC encompasses existing communication paradigms and includes emerging human-centric and task-oriented communications concepts. The authors investigate semantic communications and semantic information transfer for vehicular metaverse as a case study of UOC. Consider the Internet of Vehicles (IoV) users access real-time virtual world updates from the base station (BS) wirelessly using semantic communication, and an intelligent reflecting surface (IRS) is deployed to impair co-channel interference. This paper formulates an optimization problem where a novel utility expression for semantic communications is incorporated. The proposed system model jointly considers latency and power in wireless communication and the utility of semantic communication. The proposed alternative optimization algorithm balances system efficiency and economics and outperforms existing optimization algorithms under the same channel conditions.

The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactive and immersive socialization experience between people is one of the promises of the Metaverse. In spite of the rapid advancements in current technologies, the computation required for a smooth, seamless and immersive socialization experience in the Metaverse is overbearing, and the accumulated user experience is essential to be considered. The computation burden calls for computation offloading, where the integration of virtual and physical world scenes is offloaded to an edge server. This paper introduces a novel Quality-of-Service (QoS) model for the accumulated experience in multi-user socialization on a multichannel wireless network. This QoS model utilizes deep reinforcement learning approaches to find the near-optimal channel resource allocation. Comprehensive experiments demonstrate that the adoption of the QoS model enhances the overall socialization experience.

In this article, we present a random access (RA) scheme for federated learning (FL) over massive multiple-input–multiple-output (MIMO) systems to tackle the issue of some local devices not being able to compute their local models. This scheme adopts a multichannel model and allows devices to randomly select their uploading channels, and then the base station (BS) aggregates the local models received from channels directly based on the over-the-air computation. We call this scheme as RA-based FL over massive MIMO (RAFL-MIMO). Furthermore, to enable more devices to be involved in the FL process, we propose to utilize an access class barring (ACB) method to select the uploading devices and formulate an optimization problem of the ACB factor. We also derive the expected asymptotic convergence rate of the proposed RAFL-MIMO scheme to analytically show that the proposed RAFL-MIMO scheme can improve the performance of FL. Simulation results based on${L2}$-norm linear regression, and MNIST handwritten digits identification, Cifar-10 photograph classification show that the proposed RAFL-MIMO scheme significantly outperforms the case of the RAFL-MIMO without the ACB factor.

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Technology advancements in wireless communications and high-performance Extended Reality (XR) have empowered the developments of the Metaverse. The demand for the Metaverse applications and hence, real-time digital twinning of real-world scenes is increasing. Nevertheless, the replication of 2D physical world images into 3D virtual objects is computationally intensive and requires computation offloading. The disparity in transmitted object dimension (2D as opposed to 3D) leads to asymmetric data sizes in uplink (UL) and downlink (DL). To ensure the reliability and low latency of the system, we consider an asynchronous joint UL-DL scenario where in the UL stage, the smaller data size of the physical world images captured by multiple extended reality users (XUs) will be uploaded to the Metaverse Console (MC) to be construed and rendered. In the DL stage, the larger-size 3D virtual objects need to be transmitted back to the XUs. We design a novel multi-agent reinforcement learning algorithm structure, namely Asynchronous Actors Hybrid Critic (AAHC), to optimize the decisions pertaining to computation offloading and channel assignment in the UL stage and optimize the DL transmission power in the DL stage. Extensive experiments demonstrate that compared to proposed baselines, AAHC obtains better solutions with satisfactory training time.

Although reconfigurable intelligent surface (RIS) technology has manifested great potentials in improving wireless network’s power saving, most existing literature restricts to pure physical (PHY) layer beamforming design and neglects the impact of media access control (MAC) layer’s data traffic flows. Simultaneously, current RIS technology suffers from defects — the severe fading loss and the limitation of half-space coverage. This paper aims to perform a cross-layer design via jointly optimizing MAC layer scheduling and PHY layer RIS beamforming to reduce power consumption. Besides, we propose a novel double-faced-active (DFA)-RIS architecture to promote RIS’ capability. The proposed design task leads to a highly challenging stochastic problem to minimize long-term power consumption while stabilizing queues. Inspired by Lyapunov control theory, we propose an online optimization strategy to resolve this challenge. Via exploiting alternative directional method of multipliers (ADMM), we develop an analytic-based solution to solve the online sub-problems highly efficiently without resorting to any numerical solvers. Our strategy theoretically guarantees all queues’ stability and achieves a tunable trade-off between the power expenditure and queue lengths. Extensive numerical results are presented to demonstrate the effectiveness of our proposed cross-layer design and the DFA-RIS’ advantage over other cutting-the-edge RIS architectures.

In order to remain competitive, Internet companies collect and analyse user data for the purpose of the improvement of user experiences. Frequency estimation is a widely used statistical tool, which could potentially conflict with the relevant privacy regulations. Privacy preserving analytic methods based on differential privacy have been proposed, which require either a large user base or a trusted server. Although the requirements for such solutions may not be a problem for larger companies, they may be unattainable for smaller organizations. To address this issue, we propose a distributed privacy-preserving sampling-based frequency estimation method which has high accuracy even in the scenario with a small number of users while not requiring any trusted server. This is achieved by combining multi-party computation and sampling techniques. We also provide a relation between its privacy guarantee, output accuracy, and the number of participants. Distinct from most existing methods, our methods achievecentralizeddifferential privacy guarantee without the need of any trusted server. We established that, even for a small number of participants, our mechanisms can produce estimates with high accuracy and hence they provide smaller companies with more opportunity for growth through privacy-preserving statistical analysis. We further propose an architectural model to support weighted aggregation in order to achieve a higher accuracy estimate to cater for users with varying privacy requirements. Compared to the unweighted aggregation, our method provides a more accurate estimate. Extensive experiments are conducted to show the effectiveness of the proposed methods.

Machine learning (ML) has been widely recognized as an enabler of the global trend of digital transformation. With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has received much attention of the machine learning community, from academic researchers to industry practitioners to government regulators. However, organizations are faced with the dilemma that, on the one hand, they are encouraged to share data to enhance ML performance, but on the other hand, they could potentially be breaching the relevant data privacy regulations. Practical PPML typically allows multiple participants to individually train their ML models, which are then aggregated to construct a global model in a privacy-preserving manner, e.g., based on multi-party computation or homomorphic encryption. Nevertheless, in most important applications of large-scale PPML, e.g., by aggregating clients’ gradients to update a global model for federated learning, such as consumer behavior modeling of mobile application services, some participants are inevitably resource-constrained mobile devices, which may drop out of the PPML system due to their mobility nature (Yang et al., 2019). Therefore, the resilience of privacy-preserving aggregation has become an important problem to be tackled because of its real-world application potential and impacts. In this paper, we propose a scalable privacy-preserving aggregation scheme that can tolerate dropout by participants at any time, and is secure against both semi-honest and active malicious adversaries by setting proper system parameters. By replacing communication-intensive building blocks with a seed homomorphic pseudo-random generator, and relying on the additive homomorphic property of Shamir secret sharing scheme, our scheme outperforms state-of-the-art schemes by up to$6.37\times $in runtime and provides a stronger dropout-resilience. The simplicity of our scheme makes it attractive both for implementation and for further improvements.

Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to implement machine learning while addressing data privacy concerns, typically involves a large number of wireless mobile devices to collect model training data. Under such circumstances, FL is expected to meet stringent training latency requirements in the face of limited resources such as demand for wireless bandwidth, power consumption, and computation constraints of participating devices. Due to practical considerations, FL selects a portion of devices to participate in the model training process at each iteration. Therefore, the tasks of efficient resource management and device selection will have a significant impact on the practical uses of FL. In this paper, we propose a spectrum allocation optimization mechanism for enhancing FL over a wireless mobile network. Specifically, the proposed spectrum allocation optimization mechanism minimizes the time delay of FL while considering the energy consumption of individual participating devices; thus ensuring that all the participating devices have sufficient resources to train their local models. In this connection, to ensure fast convergence of FL, a robust device selection is also proposed to help FL reach convergence swiftly, especially when the local datasets of the devices are not independent and identically distributed (non-iid). Experimental results show that (1) the proposed spectrum allocation optimization method optimizes time delay while satisfying the individual energy constraints; (2) the proposed device selection method enables FL to achieve the fastest convergence on non-iid datasets.

Vehicular Edge Computing (VEC) is a promising new paradigm that has attracted much attention in recent years, which can enhance the storage and computing capabilities of vehicular networks to provide users with low latency and high-quality services. Due to the open access and unreliable wireless channels, some appropriate security measures should be implemented in the VEC to ensure information security. However, the operation of the security mechanism dominates supererogatory computing resources, thus affecting the performance of VEC systems. The scarcity of computation and energy resources of the vehicles conflicts with the requirement of tasks for time delay and information security. In this paper, taking the driving velocity and position of the vehicles, the number of lanes, the model and density of the attackers, and security strength into consideration, we formulate a max-min optimization problem to jointly optimize offloading decision, transmit power, task computation frequency, encryption computation frequency, edge computation frequency, and block length to obtain optimal secure information capacity and local computation delay. The formulated optimization problem is a mixed integer nonlinear programming (MINLP), which is intractable. We apply the generalized benders decomposition (GBD)-based method to solve it. The simulation results show that our proposed algorithms have convergence and effectiveness and achieve fairness among vehicles on the road.

Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patch detection by introducing an uncertainty-based adversarial patch localizer which localizes adversarial patch on an image, permitting post-processing patch-avoidance or patch-reconstruction. We quantify our prediction uncertainties with the development of Detection of Uncertainties in the Exceedance of Threshold (DUET) algorithm. This algorithm provides a framework to ascertain confidence in the adversarial patch localization, which is essential for safety-sensitive applications such as self-driving cars and medical imaging. We conducted experiments on localizing adversarial patches and found our proposed DUET model outperforms baseline models. We then conduct further analyses on our choice of model priors and the adoption of Bayesian Neural Networks in different layers within our model architecture. We found that isometric gaussian priors in Bayesian Neural Networks are suitable for patch localization tasks and the presence of Bayesian layers in the earlier neural network blocks facilitates top-end localization performance, while Bayesian layers added in the later neural network blocks contribute to better model generalization. We then propose two different well-performing models to tackle different use cases.

With the development of blockchain and communication techniques, the Metaverse is considered as a promising next-generation Internet paradigm, which enables the connection between reality and the virtual world. The key to rendering a virtual world is to provide users with immersive experiences and virtual avatars, which is based on virtual reality (VR) technology and high data transmission rate. However, current VR devices require intensive computation and communication, and users suffer from high delay while using wireless VR devices. To build the connection between reality and the virtual world with current technologies, mobile augmented reality (MAR) is a feasible alternative solution due to its cheaper communication and computation cost. This paper proposes an MAR-based connection model for the Metaverse, and proposes a communication resources allocation algorithm based on outer approximation (OA) to achieve the best utility. Simulation results show that our proposed algorithm is able to provide users with basic MAR services for the Metaverse, and outperforms the benchmark greedy algorithm.

Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.

Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and communication optimization problem to minimize total model parameter communication and computation delay, by optimizing local iteration counts and edge iteration counts. To solve the problem, an iterative algorithm is proposed. After that, a time-minimized UE-to-edge association algorithm is presented where the maximum latency of the system is reduced. Simulation results show that the global model converges faster under optimal edge server and local iteration counts. The hierarchical federated learning latency is minimized with the proposed UE-to-edge association strategy.

Deep-learning-based image-recognition systems have been widely deployed on mobile devices in today’s world. In recent studies, however, deep learning models are shown vulnerable to adversarial examples. One variant of adversarial examples, called the adversarial patch, draws researchers’ attention due to its strong attack abilities. Though adversarial patches achieve high attack success rates, they are easily being detected because of the visual inconsistency between the patches and the original images. Besides, it usually requires a large amount of data for adversarial patch generation in the literature, which is computationally expensive and time consuming. To tackle these challenges, we propose an approach to generate inconspicuous adversarial patches with one single image. In our approach, we first decide the patch locations based on the perceptual sensitivity of victim models, then produce adversarial patches in a coarse-to-fine way by utilizing multiple-scale generators and discriminators. The patches are encouraged to be consistent with the background images with adversarial training while preserving strong attack abilities. Our approach shows the strong attack abilities in white-box settings and the excellent transferability in black-box settings through extensive experiments on various models with different architectures and training methods. Compared to other adversarial patches, our adversarial patches hold the most negligible risks to be detected and can evade human observations, which is supported by the illustrations of saliency maps and results of user evaluations. Finally, we show that our adversarial patches can be applied in the physical world.

Intelligent Internet of Things (IoT) systems based on deep neural networks (DNNs) have been widely deployed in the real world. However, DNNs are found to be vulnerable to adversarial examples, which raises people’s concerns about intelligent IoT systems’ reliability and security. Testing and evaluating the robustness of IoT systems become necessary and essential. Recently, various attacks and strategies have been proposed, but the efficiency problem remains unsolved properly. Existing methods are either computationally extensive or time consuming, which is not applicable in practice. In this article, we propose a novel framework, called attack-inspired generative adversarial networks (AI-GAN) to generate adversarial examples conditionally. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. We apply AI-GAN on different data sets in white-box settings, black-box settings, and targeted models protected by state-of-the-art defenses. Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly. Moreover, for the first time, AI-GAN successfully scales to complex data sets, e.g., CIFAR-100 and ImageNet, with about 90% success rates among all classes.

In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features, such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, calledBEoTthat has been regarded as a promising enabler for future services and applications. In this article, we present a state-of-the-art review of recent developments in the BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home, and smart grid. Security challenges in the BEoT paradigm are also discussed and analyzed, with some key services, such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input–multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.

Integrating simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technologies into Internet-of-Things (IoT) networks, named SWIPT-enabled cognitive IoT networks, has become an effective approach to resolve the short lifetime of battery-constrained IoT Devices (IoDs) and spectrum scarcity. In this type of networks, IoDs are regarded as secondary users (SUs) being charged with wireless power. To improve the sum throughput of IoDs, we allow IoDs to switch among spectrum sensing, SWIPT and information transmission adaptively. Correspondingly, three-dimensional resources, i.e., time (for performing the three actions), power (including power transmitted from an IoT controller to each IoD and power for receiving information and charging at each IoD) and spectrum, are jointly and adaptively allocated to maximize the sum throughput of IoDs. Since the formulated problem is a mixed-integer nonlinear program (MINLP), we adopt an auxiliary variable to convert the original problem into a tractable problem, which is then solved by an efficient algorithm involving the Lagrangian dual method, the subgradient method and the multiple one-dimensional search algorithm. Simulation results show our adaptive design yields superior performance in terms of the sum throughput of IoDs.

The edge-cloud computing and network slicing have emerged as promising solutions to fulfill the diversity of IoT applications enabled by 5G and beyond. However, edge-cloud computing systems are composed of various hardware facilities, leading to difficulties in hardware control and management. With network slicing, underlying resource sharing among multiple slice users is allowed, leading to potential attacks to the slice formulation processes and malicious usage of network slices that may result in inefficient resource utilization of the system. To address the aforementioned network slice security issue, we first propose a new systematic framework, named software-defined edge-cloud computing (SD-ECC), which applies standard software to control the hardware infrastructure regardless of vendor variations. With SD-ECC, resource slices are formulated by including storage and computational resources provided by edge and cloud servers. Then, we study an optimal slicing-based resource orchestration problem by considering slice-initiated attacks as possible adversaries, which includes both interslice and intraslice resource orchestrations. A secure slicing-based resource orchestration (SS-RO) algorithm is designed by minimizing the delay and resource utilization simultaneously to mitigate the impacts of the slice-initiated attacks, where the Benders decomposition is employed to obtain the interslice orchestration outcome, and a quadratic transformation method is applied to derive the intraslice orchestration solution. The experimental results demonstrate that the proposed SS-RO algorithm outperforms baseline schemes in terms of the ratio of accepted attacking tasks, energy consumption, and system throughput.

Many applications of wireless sensor networks (WSNs) require deploying sensors in hostile environments, where an adversary may eavesdrop communications. To secure communications in WSNs, the$q$-composite key predistribution scheme has been proposed in the literature. In this paper, we investigate secure$k$-connectivity in WSNs operating under the$q$-composite scheme, in consideration of the unreliability of wireless links. Secure$k$-connectivity ensures that any two sensors can find a path in between for secure communication, even when$k-1$sensors fail. We present conditions on how to set the network parameters such that the network has secure$k$-connectivity asymptotically almost surely. The result is given in the form of a sharp zero–one law.

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for the accurate and efficient processing of on-board data in vehicular edge computing (VEC). However, FL involves the training and transmission of model parameters, which consumes the vehicles’ precious energy resources and takes up much time. It is a departure from many applications with severe real-time requirements in VEC. And the capabilities and data quality of each vehicle are distinct that will affect the performance of training the model. Therefore, it is crucial to select the appropriate vehicles to participate in learning tasks and optimize resource allocation under learning time and energy consumption constraints. In this paper, taking the vehicle position and velocity into consideration, we formulate a min-max optimization problem to jointly optimize the on-board computation capability, transmission power, and local model accuracy to achieve the minimum cost in the worst case of FL. Specifically, we propose a greedy algorithm to select vehicles with higher image quality dynamically, and it keeps the system’s overall cost to a minimum in FL. The formulated optimization problem is a nonlinear programming problem, so we decompose it into two subproblems. For the resource allocation problem, we use the Lagrangian dual problem and the subgradient projection method to approximate the optimal value iteratively. For the local model accuracy problem, we develop an adaptive harmony algorithm for heuristic search. The simulation results show that our proposed algorithms have well convergence and effectiveness and achieve a tradeoff between cost and fairness.

Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collect sensor data for marketing or research purpose. In this article, we consider crowdsourcing these worker's hot travel path. Each worker is required to report his real-time location information, which is sensitive and has to be protected. Encryption-based methods are the most direct way to protect the location, but not suitable for resource-limited devices. Besides, local differential privacy is a strong privacy concept and has been deployed in many software systems. However, the local differential privacy technology needs a large number of participants to ensure the accuracy of the estimation, which is not always the case for crowdsourcing. To solve this problem, we proposed a trie-based iterative statistic method, which combines additive secret sharing and local differential privacy technologies. The proposed method has excellent performance even with a limited number of participants without the need of complex computation. Specifically, the proposed method contains three main components: iterative statistics, adaptive sampling, and secure reporting. We theoretically analyze the effectiveness of the proposed method and perform extensive experiments to show that the proposed method not only provides a strict privacy guarantee, but also significantly improves the performance from the previous existing solutions.

In this paper, we consider an intelligent reflecting surface (IRS)-assisted downlink cognitive radio (CR) system, in which a secondary access point (SAP) communicates with multiple secondary users (SUs) without affecting multiple primary users (PUs) in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only a single PU and multiple SUs is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes.

Power saving and battery-life extension have always been a critical concern for IoT network deployment. One effective solution is to switch wireless devices into sleep mode to save power. This paper considers the power control in an IoT network via jointly activating IoT sensors and designing their transmit beamforming. Besides, inspired by the great potential of reconfigurable intelligent surface (RIS) in energy saving, we additionally introduce RIS to further lower the sensors’ power consumption. The considered problem is highly challenging due to its combinatorial nature, the highly non-convex quality-of-service (QoS) constraint and the hardware restrictions from the RIS. By exploiting the cutting-the-edge majorization minimization (MM) and the penalty dual decomposition (PDD) frameworks, we have successfully developed highly efficient solutions to tackle this problem. Our proposed solutions can achieve nearly identical performance with that of the exhaustive search but with a much lower complexity. Besides, as revealed by the numerical experiments, our proposed sensor activation scheme can switch off a large portion of sensors under mild QoS requirements, which significantly reduces power expenditure. Moreover, the deployment of RIS can bring an additional 45% – 70% power saving compared to the no-RIS case.

This paper investigates the problem of distributed resource management in two-tier heterogeneous networks, where each cell selects its joint device association, spectrum allocation, and power allocation strategy based only on locally-observed information without any central controller. As the optimization problem with devices’ quality-of-service (QoS) constraints is non-convex and NP-hard, we model it as a Markov decision process (MDP). Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

Deep neural networks have been shown vulnerable to adversarial patches, where exotic patterns can result in model’s wrong prediction. Nevertheless, existing approaches to adversarial patch generation hardly consider the contextual consistency between patches and the image background, causing such patches to be easily detected by human observation. Additionally, these methods require a large amount of data for training, which is computationally expensive. To overcome these challenges, we propose an approach to generate adversarial yet inconspicuous patches with one single image. In our approach, adversarial patches are produced in a coarse-to-fine way with multiple scales of generators and discriminators. The selection of patch location is based on the perceptual sensitivity of victim models. Contextual information is encoded during the Min-Max training to make patches consistent with surroundings.

The effective combination of physical layer security communication and intelligent reflecting surface (IRS) technology has recently attracted extensive attention to improve the system security. Unlike existing works that mostly focus on single-carrier systems, we consider an IRS-assisted multi-carrier MIMO wireless physical layer security communication system, which consists of a legitimate transmitter, a legitimate receiver, an IRS node and an eavesdropper. With the aim of maximizing the sum secrecy rate, the precoding matrix and IRS reflecting coefficient matrix were jointly optimized under the constraints on the budget of the transmit power and unit modulus of IRS reflecting coefficients. An alternate optimization (AO) based inexact block coordinate descent (IBCD) algorithm was proposed to tackle the non-convexity of the formulated problem, where the Lagrange multiplier method and complex circle manifold (CCM) method were adopted to solve the subproblems and then closed-form solutions were obtained at each iteration. Finally, the simulation results validate the effectiveness of the proposed beamforming schemes.

Internet of things (IoT) technology is an essential enabler to realize ubiquitous connections and pervasive intelli-gence for the future wireless communication system. The energy self-sustainability based on the wireless power transfer technique and the coexistence with heterogeneous networks will become two predominant attributes of IoT networks. In this paper we consider the system design in a context of coexistence of a wireless powered sensor network and a cellular system, both of which share common spectrum bandwidth and are assisted by intelligent reflecting surface (IRS). Specifically, the wireless sensors exploit the harvested energy from the cellular base station (BS) to transfer information to a data sink. We aim to design a cooperation scheme via jointly optimizing the time allocation of channel use, collaborative beamforming across networks and IRS phase-shifting control to improve the sensing network's throughput while guaranteeing the cellular users' quality of service. This design problem leads to a highly nonconvex and difficult mathematical optimization problem. Via utilizing the penalty-duality-decomposition (PDD) and successive convex approximation (SCA) methods, we have managed to develop an alternative optimization solution. Nu-merical results verify the effectiveness of our algorithm and demonstrate the benefits that come from the cooperative network design.

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies have been proposed, but how to generate adversarial examples perceptually realistic and more efficiently remains unsolved. This paper proposes a novel framework called Attack-Inspired GAN (AI-GAN), where a generator, a discriminator, and an attacker are trained jointly. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. Through extensive experiments on several popular datasets e.g., MNIST and CFAR-10, AI-GAN achieves high attack success rates and reduces generation time significantly in various settings. Moreover, for the first time, AI-GAN successfully scales to complicated datasets e.g., CFAR-100 with around 90% success rates among all classes.

Adversarial training is one of the most effective approaches for deep learning models to defend against adversarial examples. Unlike other defense strategies, adversarial training aims to enhance the robustness of models intrinsically. During the past few years, adversarial training has been studied and discussed from various aspects, which deserves a comprehensive review. For the first time in this survey, we systematically review the recent progress on adversarial training for adversarial robustness with a novel taxonomy. Then we discuss the generalization problems in adversarial training from three perspectives and highlight the challenges which are not fully tackled. Finally, we present potential future directions.

This paper investigates the problem of distributed resource management (i.e., joint device association, spectrum allocation, and power allocation) in two-tier heterogeneous networks without any central controller. Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

Abstract A new random access scheme is proposed to solve the intra‐cell pilot collision for M2M communication in crowded asynchronous massive multiple‐input multiple‐output systems. The proposed scheme utilizes the proposed estimation method of signal parameters to estimate the effective timing offsets, and then active user equipments obtain their timing errors from the effective timing offsets for uplink message transmission. The mean squared error of the estimated effective timing offsets of user equipments and the uplink throughput are analysed. Simulation results show that, compared to the exiting random access scheme for the crowded asynchronous massive multiple‐input multiple‐output systems, the proposed scheme can improve the uplink throughput and estimate the effective timing offsets accurately at the same time.

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber-physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for the smart grid paradigm. In this article, we aim to provide a comprehensive survey on the application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literature addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues.

In Internet-of-Things (IoT)-driven smart-world systems, real-time crowdsourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for our society. Particularly, multiple distributed servers in a decentralized network can realize real-time collaborative statistical estimation by disseminating statistics from their separate databases. Despite no raw data sharing, the real-time statistics could still expose the data privacy of crowdsourcing participants. For mitigating the privacy concern, while the traditional differential privacy (DP) mechanism can be simply implemented to perturb the statistics in each timestamp and independently for each dimension, this may suffer a great utility loss from the real-time and multidimensional crowdsourced data. Also, the real-time broadcasting would bring significant overheads in the whole network. To tackle the issues, we propose a novel privacy preserving and communication-efficient decentralized statistical estimation algorithm (DPCrowd), which only requires intermittently sharing the DP protected parameters with one-hop neighbors by exploiting the temporal correlations in real-time crowdsourced data. Then, with further consideration of spatial correlations, we develop an enhanced algorithm, DPCrowd+, to deal with multidimensional infinite crowd-data streams. Extensive experiments on several data sets demonstrate that our proposed schemes DPCrowd and DPCrowd+ can significantly outperform existing schemes in providing accurate and consensus estimation with rigorous privacy protection and great communication efficiency.

Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging a reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers’ data. Then, manufacturers can predict customers’ requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile-edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers’ activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers’ privacy and improve the test accuracy, we enforce differential privacy (DP) on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under DP protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.

An increasing amount of users' sensitive information is now being collected for analytics purposes. Differential privacy has been widely studied in the literature to protect the privacy of users' information. The privacy parameter bounds the information about the data set leaked by the noisy output. Oftentimes, a data set needs to be used for answering multiple queries, so the level of privacy protection may degrade as more queries are answered. Thus, it is crucial to keep track of privacy budget spending, which should not exceed the given limit of privacy budget. Moreover, if a query has been answered before and is asked again on the same data set, we may reuse the previous noisy response for the current query to save the privacy cost. In view of the above, we design an algorithm to reuse previous noisy responses if the same query is asked repeatedly. In particular, considering that different requests of the same query may have different privacy requirements, our algorithm can set the optimal reuse fraction of the old noisy response and add new noise to minimize the accumulated privacy cost. Furthermore, we design and implement a blockchain-based system for tracking and saving differential-privacy cost. As a result, the owner of the data set will have full knowledge about how the data set has been used and be confident that no new privacy cost will be incurred for answering queries once the specified privacy budget is exhausted.

The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users' location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this article, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, machine learning (ML) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.

In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple user pairs in two-hop networks is investigated. Different from the existing studies on IRS that mainly focused on tuning the reflection coefficients of all elements, we consider the implementation oftruereflection resource management (RRM) through the identification of the best triggered module subset. More precisely, the implementation oftrueRRM builds on the premise of our proposed modular IRS structure consisting of multiple independent and controllable modules. In the context of modular IRS structure, we investigate the signal-to-interference-plus-noise ratio (SINR)-based max-min problem subject to per source terminals (STs) power budgets and module size constraint, via joint triggered module subset identification, transmit power allocation, and the corresponding passive beamforming. Whereas this problem is NP-hard due to the module size constraint, which can be addressed by the convex sparsity-inducing approximation to the hard module size constraint using mixed$\ell _{1,F}\text {-norm}$, where it yields a suitable semidefinite relaxation. Using techniques from separable convex programming, we provide a two-block alternating direction method of multipliers (ADMM) algorithm for the approximated problem. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters. Further energy efficiency (EE) performance comparison demonstrates the necessity and meaningfulness of the introduced modular IRS structure. Specifically, for a given network setting, there is an optimal value of the number of triggered modules for system, when the EE is considered.

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless communication systems, enhances the spectral and energy efficiency by intelligently adjusting the propagation conditions between base stations (BSs) and mobile equipments (MEs). An RIS consists of many low-cost passive reflecting elements that are optimized to improve the quality of the received signal. In this paper, we study the problem of power control at the BS and RIS optimization for application to physical-layer broadcasting. Our goal is to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BS and the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposed optimization methods, we derive lower bounds to quantify the average transmit power at the BS as a function of the number of MEs, the number of RIS elements, and the number of antennas at the BS. The simulation results demonstrate that the average transmit power at the BS is close to the lower bound in an RIS-aided system, and is significantly lower than the average transmit power in conventional schemes without an RIS.

This article investigates intelligent reflecting surface (IRS)-aided multicell wireless networks, where an IRS is deployed to assist the joint processing coordinated multipoint (JP-CoMP) transmission from multiple base stations (BSs) to multiple cell-edge users. By taking into account the fairness among cell-edge users, we aim at maximizing the minimum achievable rate of cell-edge users by jointly optimizing the transmit beamforming at the BSs and the phase shifts at the IRS. As a compromise approach, we transform the non-convex max-min problem into an equivalent form based on the mean-square error method, which facilities the design of an efficient suboptimal iterative algorithm. In addition, we investigate two scenarios, namely the single-user system and the multiuser system. For the former scenario, the optimal transmit beamforming is obtained based on the dual subgradient method, while the phase shift matrix is optimized based on the Majorization-Minimization method. For the latter scenario, the transmit beamforming matrix and phase shift matrix are obtained by the second-order cone programming and semidefinite relaxation techniques, respectively. Numerical results demonstrate the significant performance improvement achieved by deploying an IRS. Furthermore, the proposed JP-CoMP design significantly outperforms the conventional coordinated scheduling/coordinated beamforming coordinated multipoint (CS/CB-CoMP) design in terms of max-min rate.

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

In this article, we develop a residual-sparse FuzzyC-Means (FCM) algorithm for image segmentation, which furthers FCM's robustness by realizing the favorable estimation of the residual (e.g., unknown noise) between an observed image and its ideal version (noise-free image). To achieve a sound tradeoff between detail preservation and noise suppression, morphological reconstruction is used to filter the observed image. By combining the observed and filtered images, a weighted sum image is generated. Tight wavelet frame decomposition is used to transform the weighted sum image into its corresponding feature set. Taking such feature set as data for clustering, we impose an$\ell _0$regularization term on residual to FCM's objective function, thus resulting in residual-sparse FCM, where spatial information is introduced for improving its robustness and making residual estimation more reliable. To further enhance segmentation accuracy of the proposed FCM, we employ morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, a segmented image is reconstructed by using tight wavelet frame reconstruction. Experimental results regarding synthetic, medical, and real-world images show that the proposed algorithm is effective and efficient, and outperforms its peers.

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve secure trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on an Ethereum blockchain and deployed on an Ethereum test network. In addition to implementing transactions between individual users, our scheme also allows transactions among multiple users. The experimental result proves that the cost of our scheme is acceptable.

With a sponsored content scheme of mobile services, a content provider can encourage end users/subscribers to access its contents, e.g., with an advertisement, by paying part of the data price to the network operator. As a result, the content provider and end users are both actively engaged into the sponsored content ecosystem. As such, a key challenge is how to provide proper sponsorship given the content demand from the users and the service fee charged by the network operator. Furthermore, the information asymmetry between the content provider and users makes the sponsorship problem more challenging. In this paper, we propose a Stackelberg game-based framework to tackle this challenge. In the framework, the network operator, as the leader, determines the data price first, and the content provider as well as users, as the followers, make the decisions on sponsorship and content demand based on the data price, respectively. We model the interaction between the content provider and the users as a contract game in the presence of asymmetric information. In the contract game, the content provider designs a contract that contains its sponsorship strategies toward all types of users. We then derive the necessary and sufficient conditions of feasible contracts and obtain an optimal contract to maximize the profit of the content provider. Taking into account the optimal contract of contract game, we also investigate the optimal pricing of the network operator through backward induction. We prove that the Stackelberg equilibrium is unique under a mild condition and present the numerical results to illustrate some important properties of the equilibrium.

In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate.

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF-CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF-CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.

With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ranging from ultra-reliable low latency communications (URLLC) to high transmission data rates. In this context, we present a joint energy-efficient subchannel assignment and power control approach to manage massive access requests while maximizing network energy efficiency (EE) and guaranteeing different QoS requirements. The latency constraint is transformed into a data rate constraint which makes the optimization problem tractable before modelling it as a multi-agent reinforcement learning problem. A distributed cooperative massive access approach based on deep reinforcement learning (DRL) is proposed to address the problem while meeting both reliability and latency constraints on URLLC services in massive access scenario. In addition, transfer learning and cooperative learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results clearly show that the proposed distributed cooperative learning approach outperforms other existing approaches in terms of meeting EE and improving the transmission success probability in massive access scenario.

Privacy preservation is a big concern for various sectors. To protect individual user data, one emerging technology is differential privacy. However, it still has limitations for datasets with frequent queries, such as the fast accumulation of privacy cost. To tackle this limitation, this paper explores the integration of a secured decentralised ledger, blockchain. Blockchain will be able to keep track of all noisy responses generated with differential privacy algorithm and allow for certain queries to reuse old responses. In this paper, a demo of a proposed blockchain-based privacy management system is designed as an interactive decentralised web application (DApp). The demo created illustrates that leveraging on blockchain will allow the total privacy cost accumulated to decrease significantly.

Local differential privacy (LDP) is an emerging privacy standard to protect individual user data. One scenario where LDP can be applied is federated learning, where each user sends in his/her user gradients to an aggregator who uses these gradients to perform stochastic gradient descent. In a case where the aggregator is untrusted and LDP is not applied to each user gradient, the aggregator can recover sensitive user data from these gradients. In this paper, we present a new interactive web demo showcasing the power of local differential privacy by visualizing federated learning with local differential privacy. Moreover, the live demo shows how LDP can prevent untrusted aggregators from recovering sensitive training data. A measure called the exp-hamming recovery is also created to show the extent of how much data the aggregator can recover.

It is known that the capacity of the intelligent reflecting surface (IRS) aided cellular network can be effectively improved by reflecting the incident signals from the transmitter in a low-cost passive reflecting way. Nevertheless, in the actual network operation, the base station (BS) and IRS may belong to different operators, consequently, the IRS is reluctant to help the BS without any payment. Therefore, this paper investigates price-based reflection resource (elements) allocation strategies for an IRS-aided multiuser multiple-input and single-output (MISO) downlink communication systems, in which all transmissions over the same frequency band. Assuming that the IRS is composed with multiple modules, each of which is attached with a smart controller, thus, the states (active/idle) of module can be operated by its controller, and all controllers can be communicated with each other via fiber links. A Stackelberg game-based alternating direction method of multipliers (ADMM) is proposed to jointly optimize the transmit beamforming at the BS and the passive beamforming of the active modules. Numerical examples are presented to verify the proposed algorithm. It is shown that the proposed scheme is effective in the utilities of both the BS and IRS.

This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs). As the number of reflecting elements increases, the reflection resource allocation (RRA) will become urgently needed in this context, which is due to the non-ignorable energy consumption. The goal of this paper, therefore, is to realize the RRA besides the active-passive beamforming design, where RRA is based on the introduced modular IRS architecture. The modular IRS consists with multiple modules, each of which has multiple reflecting elements and is equipped with a smart controller, all the controllers can communicate with each other in a point-to-point fashion via fiber links. Consequently, an optimization problem is formulated to maximize the minimum SINR at DTs, subject to the module size constraint and both individual source terminal (ST) transmit power and the reflecting coefficients constraints. Whereas this problem is NP-hard due to the module size constraint, we develop an approximate solution by introducing the mixed row block l1,F-norm to transform it into a suitable semidefinite relaxation. Finally, numerical results demonstrate the meaningfulness of the introduced modular IRS architecture.

We introduce a novel system setup where a backscatter device operates in the presence of an intelligent reflecting surface (IRS). In particular, we study the bistatic backscatter communication (BackCom) system assisted by an IRS. The phase shifts at the IRS are optimized jointly with the transmit beamforming vector of the carrier emitter to minimize the transmit power consumption at the carrier emitter whilst guaranteeing a required BackCom performance. The unique channel characteristics arising from multiple reflections at the IRS render the optimization problem highly non-convex. Therefore, we jointly utilize the minorization-maximization algorithm and the semidefinite relaxation technique to present an approximate solution for the optimal IRS phase shift design. We also extend our analytical results to the monostatic BackCom system. Numerical results indicate that the introduction of the IRS brings about considerable reductions in transmit power, even with moderate IRS sizes, which can be translated to range increases over the non-IRS-assisted BackCom system.

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLFCPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the the IRS-assisted system rate and transmission protection level compared with existing solutions.

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Simulation results demonstrate that the proposed deep learning based secure beamforming approach can significantly improve the system secrecy performance compared with other approaches.

This paper investigates the spectral efficiency (SE) in reconfigurable intelligent surface (RIS)-aided multiuser multiple-input single-output (MISO) systems, where RIS can reconFigure the propagation environment via a large number of controllable and intelligent phase shifters. In order to explore the SE performance with user proportional fairness for such a system, an optimization problem is formulated to maximize the SE by jointly considering the power allocation at the base station (BS) and phase shift at the RIS, under nonlinear proportional rate fairness constraints. To solve the non-convex optimization problem, an effective solution is developed, which capitalizes on an iterative algorithm with closed-form expressions, i.e., alternatively optimizing the transmit power at the BS and the reflecting phase shift at the RIS. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed alternative algorithm.

As a recently proposed idea for the future wireless systems, intelligent reflecting surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service constraints, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to validate the proposed optimization method. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without the IRS.

Formal analyses of blockchain protocols have received much attention recently. Consistency results of Nakamoto's blockchain protocol are often expressed in a quantity c, which denotes the expected number of network delays before some block is mined. With μ (resp., ν) denoting the fraction of computational power controlled by benign miners (resp., the adversary), where μ+ν =1, we prove for the first time that to ensure the consistency property of Nakamoto's blockchain protocol in an asynchronous network, it suffices to have c to be just slightly greater than 2μ/(ln(μ/ν)). Such a result is both neater and stronger than existing ones. In the proof, we formulate novel Markov chains which characterize the numbers of mined blocks in different rounds.

With the advent of the era of big data, deep learning has become a prevalent building block in a variety of machine learning or data mining tasks, such as signal processing, network modeling and traffic analysis, to name a few. The massive user data crowdsourced plays a crucial role in the success of deep learning models. However, it has been shown that user data may be inferred from trained neural models and thereby exposed to potential adversaries, which raises information security and privacy concerns. To address this issue, recent studies leverage the technique of differential privacy to design private-preserving deep learning algorithms. Albeit successful at privacy protection, differential privacy degrades the performance of neural models. In this paper, we develop ADADP, an adaptive and fast convergent learning algorithm with a provable privacy guarantee. ADADP significantly reduces the privacy cost by improving the convergence speed with an adaptive learning rate and mitigates the negative effect of differential privacy upon the model accuracy by introducing adaptive noise. The performance of ADADP is evaluated on real-world datasets. Experiment results show that it outperforms state-of-the-art differentially private approaches in terms of both privacy cost and model accuracy.

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models' predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.

As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLCs) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this article considers a UAV-assisted VLC using nonorthogonal multiple-access. More specifically, we formulate a joint problem of power allocation and UAV's placement to maximize the sum rate of all users, subject to constraints on power allocation, quality of service of users, and UAV's position. Since the problem is nonconvex and NP-hard in general, it is difficult to be solved optimally. Moreover, the problem is not easy to be solved by conventional approaches, e.g., coordinate descent algorithms, due to channel modeling in VLC. Therefore, we propose using the Harris hawks optimization (HHO) algorithm to solve the formulated problem and obtain an efficient solution. We then use the HHO algorithm together with artificial neural networks to propose a design that can be used in real-time applications and avoid falling into the “local minima” trap in conventional trainers. Numerical results are provided to verify the effectiveness of the proposed algorithm and further demonstrate that the proposed algorithm/HHO trainer is superior to several alternative schemes and existing metaheuristic algorithms.

With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dilemmas in accident scenarios, such as staying the course resulting in hurting pedestrians or swerving leading to hurting passengers. To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision. Although a useful methodology for building ethical AI systems, such an approach can potentially violate the privacy of voters since moral preferences are sensitive information and their disclosure can be exploited by malicious parties resulting in negative consequences. In this paper, we report a first-of-its-kind privacy-preserving crowd-guided AI decision-making approach in ethical dilemmas. We adopt the formal and popular notion of differential privacy to quantify privacy, and consider four granularities of privacy protection by taking voter-/record-level privacy protection and centralized/distributed perturbation into account, resulting in four approaches VLCP, RLCP, VLDP, and RLDP, respectively. Moreover, we propose different algorithms to achieve these privacy protection granularities, while retaining the accuracy of the learned moral preference model. Specifically, VLCP and RLCP are implemented with the data aggregator setting a universal privacy parameter and perturbing the averaged moral preference to protect the privacy of voters' data. VLDP and RLDP are implemented in such a way that each voter perturbs her/his local moral preference with a personalized privacy parameter. Extensive experiments based on both synthetic data and real-world data of voters' moral decisions demonstrate that the proposed approaches achieve high accuracy of preference aggregation while protecting individual voter's privacy.

Per sponsored content policy, a content provider can pay the network operator on behalf of mobile users to lower the data usage fees so as to generate more advertising revenue. Under such a scheme, how to offer proper sponsorship to the users in response to varying data prices becomes an important issue. Furthermore, the information asymmetry between the content provider and users makes the problem more challenging. In this paper, we propose a Stackelberg game based framework to tackle this challenge. In the framework, the network operator determines the data price first as the leader of the game, and the content providers as well as users make the decisions based on the data price as the followers. Specifically, the decision making process of the followers with the presence of asymmetric information is formulated as a contract game. In the contract game, the content provider designs a contract that contains sponsoring strategies toward all types of the users. After obtaining the optimal contract that maximizes the profit of the content provider, we also derive the optimal pricing of the network operator through backward induction. The Stackelberg equilibrium is proved to be unique, and numerical results are presented for performance evaluation.

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.

Respiration rate monitoring is beneficial for the diagnosis of a variety of diseases, such as heart failure and sleep disorders. Radio Frequency (RF) based respiration rate monitoring systems, namely ultra-wideband radar and COTS device, have been proposed without requiring any direct contact with the detected person. However, existing RF based systems either require expensive UWB radio (radar based) or work only in stationary environments (COTS device based). To address the limitations of both radar based and COTS device based systems, in this paper, we propose RespiRadio, a system that can detect a person's respiration rate in dynamic ambient environments via a single TX-RX pair of WiFi cards. The key novelty of RespiRadio is that it overcomes the limit of existing COTS device based respiration rate systems by synthesizing a wider-bandwidth WiFi radio. With the synthesized WiFi radio, we can identify the path reflected by the breathing person and then analyze the periodicity of the signal power measurements only from this path to infer the respiration rate. We experimentally evaluate the performance of RespiRadio in non-static indoor environments and the results demonstrate that the overall estimation error is 0.152 breaths per minute (bpm).

Spatio-temporal monitoring of the Internet of Things (IoT) has enabled the development and proliferation of third-party computing services by extensively exploiting the massive amount of sensing data. In particular, continuously generated data stream are monitored in real-time and exploited to facilitate people's daily lives, such as traffic monitoring and epidemic prevention. In its simplest way of deployment, the direct publishing of various streams could seriously compromise the privacy of participating users. Hence, a more sophisticated scheme is needed to regulate the privately publishing of data streams, which may possibly require control to be applied dynamically. However, most existing solutions are non-adaptive to dynamic changes of the streams due to constraints of predefined parameters, thus are vulnerable to low data utility. In this paper, we present AdaPub, a data-adaptive framework for infinite multidimensional stream real-time publishing with ω-event differential privacy while ensuring high data utility. Without predefining the parameters, AdaPub could learn and update the parameters that reflect the spatio-temporal correlations of the stream in a data-adaptive manner. Specifically, we propose two modules DimParti and AdaCluster which are seamlessly incorporated into AdaPub to simultaneously learn dimension correlations and time correlations in a data-adaptive way, thus greatly improving the data utility of the sanitized streams. Extensive experiments on real-world datasets demonstrate that our solution substantially outperforms state-of-the-art solutions with much lower errors while achieving strong privacy guarantees.

Given an undirected graph G and a seed node s, the local clustering problem aims to identify a high-quality cluster containing s in time roughly proportional to the size of the cluster, regardless of the size of G. This problem finds numerous applications on large-scale graphs. Recently, heat kernel PageRank (HKPR), which is a measure of the proximity of nodes in graphs, is applied to this problem and found to be more efficient compared with prior methods. However, existing solutions for computing HKPR either are prohibitively expensive or provide unsatisfactory error approximation on HKPR values, rendering them impractical especially on billion-edge graphs. In this paper, we present TEA and TEA+, two novel local graph clustering algorithms based on HKPR, to address the aforementioned limitations. Specifically, these algorithms provide non-trivial theoretical guarantees in relative error of HKPR values and the time complexity. The basic idea is to utilize deterministic graph traversal to produce a rough estimation of exact HKPR vector, and then exploit Monte-Carlo random walks to refine the results in an optimized and non-trivial way. In particular, TEA+ offers practical efficiency and effectiveness due to non-trivial optimizations. Extensive experiments on real-world datasets demonstrate that TEA+ outperforms the state-of-the-art algorithm by more than four times on most benchmark datasets in terms of computational time when achieving the same clustering quality, and in particular, is an order of magnitude faster on large graphs including the widely studied Twitter and Friendster datasets.

In many applications of wireless sensor networks (WSNs), sensors are deployed in hostile environments where an adversary can eavesdrop communications. To secure communications in WSNs, the q-composite key predistribution scheme has been widely recognized as a suitable approach. In this paper, we investigate connectivity in secure WSNs operating under the q-composite scheme, in consideration of different link constraints: the unreliability of wireless links and the requirement that two sensors have to be within certain distance to have a link. We formally derive conditions on how to scale the model parameters so that the network is securely connected with high probability when the number of sensors becomes large. The results are given in the form of zero-one laws and provide useful guidelines for designing securely and reliably connected sensor networks.

There is a recent rise of interest-based social networks (e.g., Pinterest and Goodreads), which connect users by relations based on shared interests. In these networks, links between users manifest from selecting common interests from a pool of available interests. For example, two users may establish a link on Pinterest because of both liking dog photos, or on Goodreads due to reading the same novel. In this paper, we introduce a random graph model to represent an interest-based social network, in consideration of users' shared interests as well as their friend relations. More specifically, the graph model is the result of superimposing an Erdös-Rényi graph (representing friendships) over a uniform random d-intersection graph (representing common interests). We present critical conditions of the model parameters so that the network is connected. Our connectivity results are useful to understand interest-based social networks and particularly beneficial for publish-subscribe services in these networks. The formally-proved results are also confirmed via experiments.

Security is an important issue in wireless sensor networks (WSNs), which are often deployed in hostile environments. The q-composite key predistribution scheme has been recognized as a suitable approach to secure WSNs. Although the q-composite scheme has received much attention in the literature, there is still a lack of rigorous analysis for secure WSNs operating under the q-composite scheme in consideration of the unreliability of links. One main difficulty lies in analyzing the network topology whose links are not independent. Wireless links can be unreliable in practice due to the presence of physical barriers between sensors or because of harsh environmental conditions severely impairing communications. In this paper, we resolve the difficult challenge and investigate k-connectivity in secure WSNs operating under the q-composite scheme with unreliable communication links modeled as independent on/off channels, where k-connectivity ensures connectivity despite the failure of any (k - 1) sensors or links, and connectivity means that any two sensors can find a path in between for secure communication. Specifically, we derive the asymptotically exact probability and a zero-one law for k-connectivity. We further use the theoretical results to provide design guidelines for secure WSNs. Experimental results also confirm the validity of our analytical findings.

Differential privacy is a rigorous privacy standard that has been applied to a range of data analysis tasks. To broaden the application scenarios of differential privacy when data records have dependencies, the notion of Bayesian differential privacy has been recently proposed. However, it is unknown whether Bayesian differential privacy preserves three nice properties of differential privacy: sequential composability, parallel composability, and post-processing. In this paper, we provide an affirmative answer to this question; i.e., Bayesian differential privacy still have these properties. The idea behind sequential composability is that if we have m algorithms Y1, Y2,..., Ym, where Yℓis independently εℓ-Bayesian differential private for ℓ = 1,2,..., m, then by feeding the result of Y1into Y2, the result of Y2into Y3, and so on, we will finally have an Σmℓ=;1εℓ-Bayesian differential private algorithm. For parallel composability, we consider the situation where a database is partitioned into m disjoint subsets. The ℓ-th subset is input to a Bayesian differential private algorithm Yℓ, for ℓ= 1, 2,..., m. Then the parallel composition of Y1, Y2,..., Ymwill be maxmℓ=;1=1εℓ-Bayesian differential private. The postprocessing property means that a data analyst, without additional knowledge about the private database, cannot compute a function of the output of a Bayesian differential private algorithm and reduce its privacy guarantee.

Despite much research on probabilistic key predistribution schemes for wireless sensor networks over the past decade, few formal analyses exist that define schemes' resilience to node-capture attacks precisely and under realistic conditions. In this paper, we analyze the resilience of the q-composite key predistribution scheme, which mitigates the node capture vulnerability of the Eschenauer-Gligor scheme in the neighbor discovery phase. We derive scheme parameters to have a desired level of resiliency, and obtain optimal parameters that defend against different adversaries as much as possible. We also show that this scheme can be easily enhanced to achieve the same “perfect resilience" property as in the random pairwise key predistribution for attacks launched after neighbor discovery. Despite considerable attention to this scheme, much prior work explicitly or implicitly uses an incorrect computation for the probability of link compromise under node-capture attacks and ignores the real-world transmission constraints of sensor nodes. Moreover, we derive the critical network parameters to ensure connectivity in both the absence and presence of node-capture attacks. We also investigate node replication attacks by analyzing the adversary's optimal strategy.

We investigate k-connectivity in secure wireless sensor networks under the random pairwise key predistribution scheme with unreliable links. When wireless communication links are modeled as independent on-off channels, this amounts to analyzing a random graph model formed by intersecting a random K-out graph and an Erdös-Rényi graph. We present conditions on how to scale the parameters of this intersection model so that the resulting graph is k-connected with probability approaching to one (resp. zero) as the number of nodes gets large. The resulting zero-one law is shown to improve and sharpen the previous result on the 1-connectivity of the same model. We also provide numerical results to support our analysis.

We present a comprehensive analysis on connectivity and resilience of secure sensor networks under the widely studied q-composite key predistribution scheme. For network connectivity, which ensures that any two sensors can find a path in between for secure communication, we derive the conditions to guarantee connectivity in consideration of: 1) node-capture attacks, where the adversary may capture a set of sensors and compromise keys in their memory; 2) sensor mobility, meaning that sensors can move around so that the network topology may change over time; 3) physical transmission constraints, under which two sensors have to be within each other's transmission range for communication; 4) the boundary effect of network fields; and 5) link unreliability, meaning that links are allowed to be unreliable. In contrast, many prior connectivity analyses of secure sensor networks often ignore the above issues. For resilience, although limited studies have presented formal analysis, it is often assumed that the adversary captures a random set of sensors, whereas this paper allows the adversary to capture an arbitrary set of sensors. We present conditions to ensure unassailability and unsplittability in secure sensor networks under the q-composite scheme. Unassailability ensures that an adversary capturing any set consisting of a negligible fraction of sensors can compromise only a negligible fraction of communication links although the adversary may compromise communications between non-captured nodes, which happen to use keys that are shared by captured nodes. Unsplittability means that when a negligible fraction of sensors are captured, almost all of the remaining nodes are still securely connected. Based on the results of connectivity, unassailability, and unsplittability, we provide useful guidelines for the design of secure sensor networks.

Security is an important issue in wireless sensor networks (WSNs), which are often deployed in hostile environments. The q-composite key predistribution scheme has been recognized as a suitable approach to secure WSNs. Although the q-composite scheme has received much attention in the literature, there is still a lack of rigorous analysis for secure WSNs operating under the q-composite scheme in consideration of the unreliability of links. One main difficulty lies in analyzing the network topology, whose links are not independent. Wireless links can be unreliable in practice due to the presence of physical barriers between sensors or because of harsh environmental conditions severely impairing communications. In this paper, we resolve the difficult challenge and investigate topological properties related to node degree in WSNs operating under the q-composite scheme with unreliable communication links modeled as independent ON/OFF channels. Specifically, we derive the asymptotically exact probability for the property of minimum degree being at least k, present the asymptotic probability distribution for the minimum degree, and demonstrate that the number of nodes with a fixed degree is in distribution asymptotically equivalent to a Poisson random variable. We further use the theoretical results to provide useful design guidelines for secure WSNs. Experimental results also confirm the validity of our analytical findings.

In this paper, we propose an indoor localization system employing ordered sequence of access points (APs) based on received signal strength (RSS). Unlike existing indoor localization systems, our approach does not require any time-consuming and laborious site survey phase to characterize the radio signals in the environment. To be precise, we construct the fingerprint map by cutting the layouts of the interested area into regions with only the knowledge of positions of APs. This can be done offline within a second and has a potential for practical use. The localization is then achieved by matching the ordered AP-sequence to the ones in the fingerprint map. Different from traditional fingerprinting that employing all APs information, we use only selected APs to perform localization, due to the fact that, without site survey, the possibility in obtaining the correct AP sequence is lower if it involves more APs. Experimental results show that, the proposed system achieves localization accuracy < 5m with an accumulative density function (CDF) of 50% to 60% depending on the density of APs. Furthermore, we observe that, using all APs for localization might not achieve the best localization accuracy, e.g. in our case, 4 APs out of total 7 APs achieves the best performance. In practice, the number of APs used to perform localization should be a design parameter based on the placement of APs.

We investigate k-connectivity in secure wireless sensor networks under the random pairwise key predistribution scheme with unreliable links; a network is said to be k-connected if it remains connected despite the failure of any of its (k - 1) nodes or links. With wireless communication links modeled as independent on-off channels, this amounts to analyzing a random graph model formed by intersecting a random K-out graph and an Erdös-Rényi graph. We present conditions on how to scale the parameters of this intersection model so that the resulting graph is k-connected with probability approaching to one (resp. zero) as the number of nodes gets large. The resulting zero-one law is shown to improve and sharpen the previous result on the 1-connectivity of the same model. We also provide numerical results to support our analysis and show that even in the finite node regime, our results can provide useful guidelines for designing sensor networks that are secure and reliable.

The Eschenauer-Gligor (EG) random key predistri-bution scheme has been widely recognized as a typical approach to secure communications in wireless sensor networks (WSNs). However, there is a lack of precise probability analysis on the reliable connectivity of WSNs under the EG scheme. To address this, we rigorously derive the asymptotically exact probability of k-connectivity in WSNs employing the EG scheme with unreliable links represented by independent on/off channels, where k-connectivity ensures that the network remains connected despite the failure of any (k-1) sensors or links. Our analytical results are confirmed via numerical experiments, and they provide precise guidelines for the design of secure WSNs that exhibit a desired level of reliability against node and link failures.

Random key graphs form a class of random intersection graphs that are naturally induced by the random key predistribution scheme of Eschenauer and Gligor for securing wireless sensor network (WSN) communications. Random key graphs have received much attention recently, owing in part to their wide applicability in various domains, including recommender systems, social networks, secure sensor networks, clustering and classification analysis, and cryptanalysis to name a few. In this paper, we study connectivity properties of random key graphs in the presence of unreliable links. Unreliability of graph links is captured by independent Bernoulli random variables, rendering them to be on or off independently from each other. The resulting model is an intersection of a random key graph and an Erdos-Renyi graph, and is expected to be useful in capturing various real-world networks; e.g., with secure WSN applications in mind, link unreliability can be attributed to harsh environmental conditions severely impairing transmissions. We present conditions on how to scale this model's parameters so that: 1) the minimum node degree in the graph is at least k and 2) the graph is k-connected, both with high probability as the number of nodes becomes large. The results are given in the form of zero-one laws with critical thresholds identified and shown to coincide for both graph properties. These findings improve the previous results by Rybarczyk on k-connectivity of random key graphs (with reliable links), as well as the zero-one laws by Yagan on one-connectivity of random key graphs with unreliable links.

We study the secure and reliable connectivity of wireless sensor networks. Security is assumed to be ensured by the random pairwise key predistribution scheme of Chan, Perrig, and Song, and unreliable wireless links are represented by independent ON/OFF channels. Modeling the network by an intersection of a random K-out graph and an Erdos-Rényi graph, we present scaling conditions (on the number of nodes n, the scheme parameter K, and the probability p of a wireless channel being on), such that the resulting graph contains no nodes with a degree less than k with high probability. Results are given in the form of zero-one laws with n getting large, and are shown to improve the previous results by Yagan and Makowski on the absence of isolated nodes (i.e., absence of nodes with degree zero) in the same model. Through simulations, the established zero-one laws are also shown to hold for the property of k-connectivity, i.e., the property that graph remains connected despite the deletion of any k - 1 nodes or edges.

The q-composite key predistribution scheme [2] is used prevalently for secure communications in large-scale wireless sensor networks (WSNs). Prior work [5], [13], [44] explores topological properties of WSNs employing the q-composite scheme for q = 1 with unreliable communication links modeled as independent on/off channels. In this paper, we investigate topological properties related to the node degree in WSNs operating under the q-composite scheme and the on/off channel model. Our results apply to general q and are stronger than those reported for the node degree in prior work even for the case of q being 1. Specifically, we show that the number of nodes with an arbitrary degree asymptotically converges to a Poisson distribution, present the asymptotic probability distribution for the minimum degree of the network, and establish the asymptotically exact probability for the property that the minimum degree is at least an arbitrary value. Numerical experiments confirm the validity of our analytical findings.

We study the secure and reliable connectivity of wireless sensor networks. Security is assumed to be ensured by the random pairwise key predistribution scheme of Chan, Perrig, and Song, and unreliable wireless links are represented by independent on/off channels. Modeling the network by an intersection of a random K-out graph and an Erdös-Rényi graph, we present scaling conditions (on the number of nodes, the scheme parameter K, and the probability of a wireless channel being on) such that the resulting graph contains no node with degree less than k with high probability, when the number of nodes gets large. Results are given in the form of a zero-one law and are shown to improve the previous results by Yağan and Makowski on the absence of isolated nodes (i.e., absence of nodes with degree zero). Via simulations, the established zero-one laws are shown to hold also for the property of k-connectivity; i.e., the property that graph remains connected despite the deletion of any k - 1 nodes or edges.

In this paper, we consider a wireless network with unreliable links and investigate its minimum node degree and k-connectivity. In such network, n nodes are uniformly distributed in a region, and edges are established for any two nodes within a certain distance and with a probabilistically active link in between. On a torus topology, we present a zero-one law for the property of minimum degree being at least k, leading to a zero-law for k-connectivity and an intermediate result towards a one-law. On a square with boundary effect, we establish a one-law for minimum degree. Our results are derived from rigorous analysis and also confirmed by the simulation, and they provide guidelines for the design of wireless networks.

Random key predistribution scheme of Eschenauer and Gligor (EG) is a typical solution for ensuring secure communications in a wireless sensor network (WSN). Connectivity of the WSNs under this scheme has received much interest over the last decade, and most of the existing work is based on the assumption of unconstrained sensor-to-sensor communications. In this paper, we study the k-connectivity of WSNs under the EG scheme with physical link constraints; k-connectivity is defined as the property that the network remains connected despite the failure of any (k - 1) sensors. We use a simple communication model, where unreliable wireless links are modeled as independent on/off channels, and derive zero-one laws for the properties that i) the WSN is k-connected, and ii) each sensor is connected to at least k other sensors. These zero-one laws improve the previous results by Rybarczyk on the k-connectivity under a fully connected communication model. Moreover, under the on/off channel model, we provide a stronger form of the zero-one law for the 1-connectivity as compared to that given by Yağan.

In this paper, we investigate the coverage of mobile heterogeneous wireless sensor networks (WSNs). By the term heterogeneous, we mean that sensors in the network have various sensing radii, which is an inherent property of many applied WSNs. Two sensor deployment schemes are considered-uniform and Poisson schemes. We study the asymptotic coverage under uniform deployment scheme with i.i.d. and 1-dimensional random walk mobility model, respectively. We propose the equivalent sensing radius (ESR) for both cases and derive the critical ESR correspondingly. From the perspective of critical ESR, we show that 1-dimensional random walk mobility can increase coverage under certain delay tolerance, and thus decreases sensing energy consumption. Also, we characterize the role of heterogeneity in coverage and energy performance of WSNs with these two mobility models, and present the discrepancy of the impact of heterogeneity under different models. Under the Poisson deployment scheme, we investigate dynamic k-coverage of WSNs with 2-dimensional random walk mobility model. There are many reasons for designers to require k-coverage rather than 1-coverage. Both k-coverage at an instant and over a time interval are explored and we derive the expectation of fraction of the whole operational region that is k-covered, which also identifies the coverage improvement brought by mobility.

A new class of scheduling policies for multicast traffic are proposed in this paper. By utilizing hierarchical cooperative MIMO transmission, our new policies can obtain an aggregate throughput of $\Omega\big((\frac{n}{k})^{1-\epsilon}\big)$ for any $\epsilon>0$. This achieves a gain of nearly $\sqrt{\frac{n}{k}}$ compared with non-cooperative scheme in \cite{paper:MulticastCapacityXYLi}. Between the two cooperative strategies in our paper, the converge-based one is superior to the other on delay, while the throughput and energy consumption performances are nearly the same. Moreover, to schedule the traffic in a converge multicast manner instead of the simple multicast, we can dramatically reduce the delay by a factor nearly $(\frac{n}{k})^\frac{h}{2}$, where $h>1$ is the number of the hierarchical layers. Our optimal cooperative strategy achieves an approximate delay-throughput tradeoff $D(n,k)/T(n,k)=\Theta(k)$ when $h\rightarrow\infty$. This tradeoff ratio is identical to that of non-cooperative scheme, while the throughput performance is greatly improved. Besides, for certain $k$ and $h$, the tradeoff ratio is even better than that of unicast.