The big data era has accelerated the development of artificial intelligence (AI). The Model-as-a-Service (MaaS) paradigm has been used to address the substantial challenges associated with the organization and development of AI services. However, the successful delivery of complete AI applications is contingent upon the robust collaboration between microservice architectures and AI services. In this case, hybrid orchestration of AI services and microservices is highly necessary, but it still brings challenges. Furthermore, due to the heterogeneity of servers, resource competition, and multi-instance, the difficulty of hybrid orchestration modeling is enlarged. When considering intricate service dependencies among AI services and microservices, the tight coupling of deployment and routing leads to complex joint optimization problems, vastly aggravating the pressure of hybrid orchestration. Nonetheless, extant literature largely failed to address the intricate competitive and collaborative relationships between AI services and microservices, and fine-grained latency analysis with multi-instance modeling in hybrid orchestration problem. Therefore, we study joint deployment and routing for hybrid AI services and microservices in heterogeneous edge. Firstly, we conduct a precise analysis of latency and energy consumption, based on queuing networks and multi-instance models. Secondly, we propose a reinforcement learning method based on potential functions and segmented rewards (PS_SAC) to optimize end-to-end latency and system energy consumption, achieving efficient hybrid orchestration. Finally, through extensive simulation experiments, the algorithm demonstrates significant advantages in reducing latency, improving resource utilization, and lowering system energy consumption.

Accurate channel prediction is essential in massive multiple-input multiple-output (m-MIMO) systems to improve precoding effectiveness and reduce the overhead of channel state information (CSI) feedback. However, existing methods often suffer from accumulated prediction errors and poor generalization to dynamic wireless environments, making it challenging to maintain high prediction accuracy. Large language models (LLMs) have demonstrated remarkable modeling and generalization capabilities in tasks such as time series prediction, making them a promising solution. Nevertheless, a significant modality gap exists between the linguistic knowledge embedded in pretrained LLMs and the intrinsic characteristics of CSI, posing substantial challenges for their direct application to channel prediction. Moreover, the large parameter size of LLMs hinders their practical deployment in real-world communication systems with stringent latency constraints. To address these challenges, we propose a novel channel prediction framework based on semantically aligned large models, referred to as CSI-ALM, which bridges the modality gap between natural language and channel information. Specifically, we design a cross-modal fusion module that aligns CSI representations with the language feature space using a pretrained corpus. Additionally, we maximize the cosine similarity between word embeddings and CSI embeddings to construct semantic cues, effectively leveraging the latent knowledge in LLMs. To reduce complexity and enable practical implementation, we further introduce a lightweight version of the proposed approach, called CSI-ALM-Light. This variant is derived via a knowledge distillation strategy based on attention matrices, which extracts essential features from the teacher model, CSI-ALM, and transfers them to a compact, efficient student model, CSI-ALM-Light. Extensive experimental results demonstrate that CSI-ALM consistently outperforms state-of-the-art deep learning methods across various communication scenarios, achieving substantial performance gains. Moreover, under limited training data conditions—where all models are trained using only 10% of the original training dataset—CSI-ALM-Light, with only 0.34M parameters, attains performance comparable to CSI-ALM and significantly outperforms conventional deep learning approaches. These validate the effectiveness of the proposed approach for accurate and efficient channel prediction in m-MIMO systems.

Nowadays, Generative AI (GenAI) reshapes numerous domains by enabling machines to create content across modalities. As GenAI evolves into autonomous agents capable of reasoning, collaboration, and interaction, they are increasingly deployed on network infrastructures to serve humans automatically. This emerging paradigm, known as the agentic network, presents new optimization challenges due to the demand to incorporate subjective intents of human users expressed in natural language. Traditional generic Deep Reinforcement Learning (DRL) struggles to capture intent semantics and adjust policies dynamically, thus leading to suboptimality. In this paper, we present LAMeTA, a Large AI Model (LAM)-empowered Two-stage Approach for intent-aware agentic network optimization. First, we propose Intent-oriented Knowledge Distillation (IoKD), which efficiently distills intent-understanding capabilities from resource-intensive LAMs to lightweight edge LAMs (E-LAMs) to serve end users. Second, we develop Symbiotic Reinforcement Learning (SRL), integrating E-LAMs with a policy-based DRL framework. In SRL, E-LAMs translate natural language user intents into structured preference vectors that guide both state representation and reward design. The DRL, in turn, optimizes the generative service function chain composition and E-LAM selection based on real-time network conditions, thus optimizing the subjective Quality-of-Experience (QoE). Extensive experiments conducted in an agentic network with 81 agents demonstrate that IoKD reduces mean squared error in intent prediction by up to 22.5%, while SRL outperforms conventional generic DRL by up to 23.5% in maximizing intent-aware QoE.

Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as a blind inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.

In this paper, we establish a two-timescale framework for the joint optimization for the content placement and content delivery problem in satellite-terrestrial edge computing networks (STECN). Our goal is to optimize content placement to improve network performance while ensuring diverse quality of service (QoS) for content delivery. We decouple the problem into two timescales to balance real-time responsiveness and long-term efficiency. Specifically, considering frequent content placement incurs huge traffic cost, we optimize the content placement in order to reduce resource expenses in large timescales. The optimization problem is formulated as an integer linear programming (ILP) problem to improve both traffic efficiency and cache resource utilization. We leverage a heuristic atom search optimization (ASO) approach to address the problem, which yields an optimal strategy with low computational complexity. In small timescales, we model content delivery as a Markov decision process (MDP) to minimize content delivery delays at small timescales while maintaining smooth network traffic. A deep reinforcement learning (DRL) framework is used for policy learning to dynamically adapt to varying network conditions. By considering the correlation between the small and large timescale optimization, we propose a hierarchical solution to jointly address both issues. Finally, extensive simulations confirm the effectiveness and superiority of the proposed scheme.

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose a generic blockchain-based steganography framework (GBSF). The sender generates the required fields such as amount and fees, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design a reversible generative adversarial network (R-GAN) that utilizes the generative adversarial network with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN. To further improve the performance, we propose R-GAN withCounter-intuitive data preprocessing andCustom activation functions, namelyCCR-GAN. The counter-intuitive data preprocessing (CIDP) mechanism is used to reduce decoding errors in covert data, while it incurs gradient explosion for model convergence. The custom activation function named ClipSigmoid is devised to overcome the problem. Theoretical justification for CIDP and ClipSigmoid is also provided. We also develop a mechanism named T2C, which balances capacity and concealment. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field to verify the feasibility. We then apply the proposed schemes to other transaction fields and blockchains to demonstrate the scalability. Finally, we evaluate capacity and concealment for various blockchains and transaction fields and explore the trade-off between capacity and concealment. Experimental results demonstrate that R-GAN and CCR-GAN are able to enhance the channel capacity effectively and outperform state-of-the-art works.

Vision Transformers (ViTs) have emerged as a dominant backbone architecture for a variety of visual tasks; however, their vulnerability to adversarial examples continues to pose a significant challenge. Unlike Convolutional Neural Networks (CNNs), ViTs fundamentally rely on self-attention mechanisms, leading to a distinct architectural design. The limited transferability of existing adversarial attacks on ViTs can be attributed to the neglect of these unique features. To address this, we introduce a novel self-attention-oriented Adversarial Block Skip (ABS) method specifically designed to generate transferable adversarial examples. ABS aims to create a diverse range of structures by applying skip connections to blocks within the transformer encoder, thereby activating the uncertainty of the attention mechanism. This disrupts the global interaction between different features captured by ViTs, thereby confounding the model's decision-making process. The results unequivocally demonstrate that the ABS not only establishes a versatile and efficacious attack mechanism but also supports transfer attacks across a diverse array of ViTs and CNNs. This finding emphasizes the significant generalization capabilities of ViTs within the adversarial landscape, suggesting that their resilience and adaptability under such conditions may surpass previous assumptions. Comprehensive empirical evaluations involving various prominent transformer models on the ImageNet dataset substantiate that ABS markedly surpasses existing baseline methods in terms of effectiveness. Furthermore, ABS is highly compatible with prevailing adversarial attack frameworks, augmenting their efficacy upon integration. Such versatility renders ABS an indispensable component of the toolkit for executing advanced and effective adversarial attacks in the realm of machine learning security.

The increasing saturation of terrestrial resources has driven the exploration of low-altitude applications such as air taxis. Low altitude wireless networks (LAWNs) serve as the foundation for these applications, and integrated sensing and communication (ISAC) constitutes one of the core technologies within LAWNs. However, the open nature of low-altitude airspace makes LAWNs vulnerable to malicious channel access attacks, which degrade the ISAC performance. Therefore, this paper develops a game-based framework to mitigate the influence of the attacks on LAWNs. Concretely, we first derive expressions of communication data’s signal-to-interference-plus-noise ratio and the age of information of sensing data under attack conditions, which serve as quality of service metrics. Then, we formulate the ISAC performance optimization problem as a Stackelberg game, where the attacker acts as the leader, and the legitimate drone and the ground ISAC base station act as second and first followers, respectively. On this basis, we design a backward induction algorithm that achieves the Stackelberg equilibrium while maximizing the utilities of all participants, thereby mitigating the attack-induced degradation of ISAC performance in LAWNs. We further prove the existence of the equilibrium. Simulation results show that the proposed algorithm outperforms existing baselines and a static Nash equilibrium benchmark, ensuring that LAWNs can provide reliable service for low-altitude applications.

Vehicular twin networks (VTN) create virtual agents of vehicular entities through digital twin (DT) technology, replacing physical counterparts in connecting and exchanging traffic information in cyberspace, overcoming physical range constraints and extending information sources for enhanced vehicular decision support. However, the inherent openness of VTN renders communication between DTs, vulnerable to security threats, such as tampering and impersonation, especially in scenarios where DTs are distributed across multiple cloud domains. These issues result in erroneous decisions to threaten vehicular safety because DTs may receive compromised information. To address these challenges, this article proposes a secure and revocable access authentication scheme in the cross-domain VTN. In the scheme, DTs should be authorized first to obtain identity-bound symmetric functions before joining the VTN, and then perform secure access authentication and key agreement with others based on chameleon hash functions for both intradomain and cross-domain communication. Moreover, a dynamic revocation mechanism is introduced to remove malicious DTs from VTN. Formal verification using the Tamarin tool demonstrates that the proposed scheme achieves diverse security properties. Performance evaluation further shows that the proposed scheme outperforms most related schemes in terms of computational and communication overhead.

Enhanced by inter-satellite links and satellite direct-to-device capabilities, satellite networks can offer low-latency communication globally. However, limited spectrum resources and the capacity bounds of the Shannon's information theory pose fundamental challenges for supporting bandwidth-intensive multimedia services. Semantic communication (SemCom) offers a promising solution by transmitting compressed semantic representations instead of raw data, thereby alleviating bandwidth pressure. However, it also introduces SemCom-related constraints that render conventional schemes such as contact graph routing inapplicable. To overcome this challenge, we investigate SemCom-compliant path selection and formulate it as a non-NP hard mixed-integer linear programming problem. To address the problem, we develop a graph-based scheme that exploits the special structure of the solution space, the sparsity of SemCom-capable satellites, and the property of Dijkstra's algorithm, thus achieving optimal solutions with polynomial-time complexity. Simulation results on the Starlink constellation confirm that the proposed scheme facilitates SemCom with negligible computational overhead and significant bandwidth reduction. While the bandwidth reduction comes at the cost of increased delay and path hops, these effects are shown to be mitigatable through higher SemCom deployment in a satellite network or by enabling semantic processing at the user side.

Service meshes built upon ubiquitous microservice architectures, as an emerging paradigm, promise to enhance the flexibility, scalability, and portability of energy-consuming and latency-sensitive applications in edge with limited resources. However, due to intricate microservice dependencies, service multiplexing, and parallel distributed instances, microservice deployment and request routing are highly interdependent. To reduce response latency and energy consumption, such collaborative optimization for efficient service mesh orchestration is necessary, but significantly challenging. Besides, strict service level objective (SLO) requirements and f ine-grained latency analysis with multi-nest routing further impose great difficulties to online orchestration. When considering multi instance modeling and multi-hop data communications for numerous microservices, the difficulty is extremely amplified. Nevertheless, most prevailing work failed to design sophisticate models and methods for addressing the above difficulties, and ignored the inherent transmission energy consumption for highly-concurrent multi-hop data interactions. Therefore, this paper investigates the energy aware service mesh deployment and online request routing in edge. First, we establish a multi-instance queuing network model to accurately analyze the end-to-end response latency with complicated dependencies and multi-hop communications, and optimize energy consumption in a fine-grained manner. Then, to boost the overall performance, we design an efficient multi-dimensional hierarchical deep reinforcement learning algorithm, which enables edges and service instances to cooperate with each other to handle massively concurrent requests. Besides, we propose an energy-aware proactive autoscaling algorithm to carefully adapt to exceedingly dynamic scenarios. Finally, extensive experiments are performed to show our superior performance compared to other baselines.

As an emerging computing paradigm, Computing Power Networks (CPNs) are dedicated to coordinating and managing network resources and computing resources to achieve interconnectivity in computing power perception. Efficient collaborative computing of massive data can be achieved through the scheduling function of CPNs. However, existing scheduling research mainly focuses on selecting network links and computing nodes, lacking consideration for task execution after scheduling, which may degrade the Quality of Service (QoS), leading to widespread failures and significant losses. To address this issue, we design a priority-aware preemptive task scheduling (P2TS) strategy for CPNs to jointly optimize task scheduling and execution in terms of success rate, average processing delay, and load balancing. Specifically, at the execution level, we propose a priority-aware preemptive mechanism (P2M) to optimize post-scheduling task execution. Then, at the scheduling level, we apply deep reinforcement learning (DRL) to optimize the scheduling process supporting the P2M in CPNs. A series of simulations are conducted to demonstrate the superiority of our strategy.

Recently, Named Data Networking (NDN) has garnered widespread attention in academia as an innovative network architecture, offering solutions to challenges such as the vulnerability of end-to-end connections in IP-based networks. In NDN, nodes utilize a “pull-push” architecture, exchangingInterestandDatapackets for communication. This architecture is particularly well-suited for highly dynamic, topology-varying unmanned aerial vehicle (UAV) swarm networks, known as UAV Named Data Networking (UNDN). However, in UNDN, due to constraints such as the lightweight design and limited energy of UAVs, the UAVs may exhibit selfish behaviors, opting not to share data in order to conserve their own energy consumption. This behavior results in degraded network performance, as the lack of cooperation among UAVs can hinder efficient data sharing and communication. Therefore, an effective incentive mechanism needs to be proposed. In this paper, we formulate the content-sharing process in UNDN as a double auction market for data exchange. To tackle the problem of asymmetric information between content consumers and producers, we propose an Iterative Double Auction algorithm (IDAA). This algorithm introduces a virtual central broker to guide both parties in conducting honest auctions. Furthermore, we develop a diffusion model-based reinforcement learning algorithm (DiffRL-DA) to derive optimal auction policies, with the goal of better capturing market behaviors and overcoming the limitations of the IDAA. Finally, simulation results verify the efficacy of our proposed mechanisms.

We consider decentralized federated learning (DFL) in mobile computing networks (MCNs), where dynamically changing neighborhood sets among devices arise from mobility and environmental perturbations. The time-varying topology coupled with inherent system heterogeneity poses significant challenges to achieve stable and efficient convergence in DFL. However, existing studies rarely consider both dynamic connectivity and statistical heterogeneity. To close this gap, this paper proposes a novel DFL framework enhanced with topology learning (DFL-TL) to mitigate the spatio-temporal volatility induced by MCNs, where each mobile device faces coupled constraints on its temporal windows for local updates and spatial scopes for model interaction. We introduce a new bounded neighborhood heterogeneity to jointly measure and constrain both the inter-device heterogeneity and the spectral properties of the topologies. Additionally, we formulate a mixed-integer nonlinear programming (MINLP) problem to jointly optimize learning costs and neighborhood heterogeneity. Through problem decomposition, DFL-TL efficiently identifies optimal resource allocations and adaptive mixing matrices, thereby enabling the selection of optimal training time windows while reducing the adverse effects of dynamic topologies in heterogeneous networks. Furthermore, we establish the iteration complexity of DFL-TL under non-convex settings and show that solving the proposed MINLP formulation leads to a tighter convergence bound. Extensive experiments demonstrate that DFL-TL achieves a faster convergence performance and reduces the wall-clock training time compared to the state-of-the-art baselines.

The rising data demands of generative AI models, such as large language models (LLMs), underscore the value of utilizing edge device data in mobile computing, where federated learning (FL) provides a privacy-preserving approach via decentralized model training. To address data heterogeneity across edge devices, federated multi-view learning (FedMVL) has been proposed to improve global model performance by capturing consistency and complementarity among diverse data views. However, existing methods often assume ideal conditions and neglect data uncertainty in mobile edge devices environments. To overcome these challenges, we propose Federated Reliable Multi-view Classification (FedRMVL), a vertical FedMVL framework incorporates Subjective Logic for lightweight uncertainty quantification at local edge devices and applies the Dempster-Shafer combination rule for adaptive and reliable multi-view opinion fusion at the server. Additionally, a partial parameter-sharing strategy is introduced to address feature dimension heterogeneity during federated optimization. The effectiveness and robustness of FedRMVL are validated through theoretical analysis and extensive experiments on real-world datasets.

The rapid development of AI accelerates the implementation and delivery of AI applications in diverse fields. In cloud-edge collaboration, delivering a complete AI application relies on the robust coordination between AI-supporting microservices and AI services. However, most existing studies only coarsely considered monolithic AI service orchestration while neglecting microservice orchestration. Such coarse-grained orchestration severely impacts application performance. To enable diverse high-performance AI applications, fine-grained hybrid orchestration of AI services and microservices (HOAIM) is highly desirable, yet presents formidable challenges. Due to heterogeneous services, call dependencies, and service multiplexing, fine-grained hybrid orchestration modeling is highly non-trivial. Moreover, the tight coupling between deployment and routing results in a complex joint optimization problem. To address this, we first propose a heterogeneous service orchestration network that supports orchestration optimization and automated management. Then, based on queuing networks and multi-instance models, we conduct an accurate analysis of delay and load. Furthermore, to achieve efficient hybrid orchestration, we propose preference-driven resource allocation and instance computation algorithms, along with reinforcement learning with action masking and reward shaping. Finally, extensive trace-driven simulations demonstrate that our algorithms optimize average response delay by up to 41.83%, and achieve significant advantages in load balancing, response success rate, and resource efficiency.

With rapid advancements in large language models (LLMs), AI-generated content (AIGC) has emerged as a key driver of technological innovation and economic transformation. Personalizing AIGC services to meet individual user demands is essential but challenging for AIGC service providers (ASPs) due to the subjective and complex demands of mobile users (MUs), as well as the computational and communication resource constraints faced by ASPs. To tackle these challenges, we first develop a novel multi-dimensional quality-of-experience (QoE) metric. This metric comprehensively evaluates AIGC services by integrating accuracy, token count, and timeliness. We focus on a mobile edge computing (MEC)-enabled AIGC network, consisting of multiple ASPs deploying differentiated AIGC models on edge servers and multiple MUs with heterogeneous QoE requirements requesting AIGC services from ASPs. To incentivize ASPs to provide personalized AIGC services under MEC resource constraints, we propose a QoE-driven incentive mechanism. We formulate the problem as an equilibrium problem with equilibrium constraints (EPEC), where MUs as leaders determine rewards, while ASPs as followers optimize resource allocation. To solve this, we develop a dual-perturbation reward optimization algorithm, reducing the implementation complexity of adaptive pricing. Experimental results demonstrate that our proposed mechanism achieves a reduction of approximately$64.9\%$in average computational and communication overhead, while the average service cost for MUs and the resource consumption of ASPs decrease by$66.5\%$and$76.8\%$, respectively, compared to state-of-the-art benchmarks.

The Digital Twin (DT) system has become a new paradigm to empower Unmanned Aerial Vehicles (UAV) networks for low-altitude applications, such as parcel delivery. However, due to high computing complexity, traditional DT technology might confront challenges to imitating highly dynamic UAVs in large-scale parcel delivery scenarios. It causes a negative influence on low-latency and high-accuracy delivery. To address the issue, we propose a terminal-edge cooperative multi-scale DT framework. It can perform a cooperative DT implementation with a cross-layer computing resource orchestration based on a multi-scale imitation manner. Explicitly, we propose a graph matching network based DT algorithm to run macro-scale DTs at the edge. It can assist edge UAVs in exploring feasible delivery associations among UAV groups and parcel clusters based on information on UAV topology and parcel destinations for a high successful delivery ratio. We then propose a Competitive and Cooperative Reinforcement Learning (CCRL) based DT algorithm to implement micro-scale DTs at the terminal. It can enable UAVs to implement low-latency delivery by optimizing delivery paths with low energy consumption. We demonstrate the effectiveness of the proposed framework with verifications under multiple metrics. The results show that our solution provides a real-time UAV delivery performance, with up to 94% successful delivery ratio, under a low system latency compared to the state-of-the-art solutions.

In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize gradient ascent and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates and service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%.

Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-latency intelligence services. Facing global coverage and complex environments in SAGINs, edge intelligence can provision large language models (LLMs) agents for users via edge servers at ground base stations (BSs) or cloud data centers relayed by satellites. As LLMs with billions of parameters are pretrained on vast datasets, LLM agents have few-shot learning capabilities, e.g., chain-of-thought (CoT) prompting for complex tasks, which raises a new trade-off between resource consumption and performance in SAGINs. In this paper, we propose a joint caching and inference framework for edge intelligence to provision sustainable and ubiquitous LLM agents in SAGINs. We introduce “cached model-as-a-resource” for offering LLMs with limited context windows and propose a novel optimization framework, i.e., joint model caching and inference, to utilize cached model resources for provisioning LLM agent services along with communication, computing, and storage resources.We design “age of thought” (AoT) considering the CoT prompting of LLMs, and propose a least AoT cached model replacement algorithm for optimizing the provisioning cost. We propose a deep Q-network-based modified second-bid (DQMSB) auction to incentivize satellite/ground network operators in real-time, which can enhance allocation efficiency by 23% while guaranteeing strategy-proofness and being free from adverse selection.

The rapid development of service computing has led to the emergence of scalable and flexible architectures such as microservices and Artificial Intelligence as a Service (AaaS), enabling the orchestration of AI-driven intelligent applications. However, existing work on intelligent applications orchestration overlooked essential microservice components that support AI services, resulting in coarse-grained and incomplete models. To ensure system integrity and enhance QoS, fine-grained collaborative orchestration of microservices and AI services is crucial. However, this poses significant challenges due to complex service dependencies, high request concurrency, and heterogeneous resource demands in edge environments. Moreover, the strong coupling between service deployment and request routing complicates their joint optimization, since effective decisions in one depend on the other. To address these challenges, we propose a collaborative orchestration framework that jointly optimizes the deployment of microservices and AI services along with probabilistic request routing in edge environments. We formulate the problem as a mixed-integer nonlinear program and leverage Jackson queuing networks for accurate delay modeling. To solve this, we develop a dual time scale hybrid greedy proximal policy optimization (DTS-HGPPO) algorithm that performs instance-level deployment and adaptive routing, enhanced with iterative instance planning, action masking and intrinsic motivation mechanisms. Extensive trace-driven experiments demonstrate that our method significantly reduces both response delay and service cost compared to state-of-the-art baselines.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can be assembled in the air-ground integrated sensing and communication (ISAC) to significantly enhance the coverage and sensing performance. However, the near-field effect should be further considered with higher carrier frequency and increasing number of STAR-RIS elements. In this paper, we propose a STAR-RIS enabled air-ground near-field ISAC scheme, where an unmanned aerial vehicle (UAV) is deployed as the mobile base station (BS) and the semi-passive STAR-RIS architecture is adopted to alleviate the severe path loss. Specifically, we maximize the weighted sum rate to guarantee both the communication and sensing functionalities by jointly modifying the beamforming vectors at the BS, the reflection/transmission matrices of the STAR-RIS and, the hovering location of the UAV to well match the near-field effect, which is non-convex with coupled variables. To address this challenge, we first decompose the problem into three subproblems via block coordinate descent. Then, the semidefinite relaxation and successive convex approximation are leveraged to recast these subproblems into convex ones. Finally, we develop an alternating algorithm with low complexity to iteratively solve them. Simulation results are shown to demonstrate the superiority and validity of the proposed scheme.

Ambient backscatter communication (AmBC) enables ultra-low-power, low-cost and massive connectivity. However, practical AmBC systems suffer from symbol timing offset (STO) due to propagation delay and backscatter receiver (BR) activation latency, while conventional correlation-based synchronization methods are inapplicable because ambient radio frequency sources are non-cooperative. Moreover, residual STO (RSTO) inevitably remains due to the finite synchronization sequence, which degrades symbol detection performance. To address these challenges, we first design a specialized synchronization sequence with alternating “0” and “1” bits at the backscatter device to induce observable sampling errors at the BR. Based on this, we propose a pilot-aided, sampling-error-aware maximum likelihood estimation (PSE-MLE) method for STO estimation and compensation, which exploits the statistical variations in the received synchronization signal. After STO compensation, the remaining RSTO is statistically modeled as a discrete bilateral Laplace distribution, with its parameter estimated via ridge regression. Leveraging this prior information, we further develop a Bayesian average energy detector (ave-ED) and derive closed-form expressions for both the detection threshold and bit error rate. Simulation and experimental results on a practical AmBC platform validate the effectiveness of the proposed methods.

This paper proposes a promising framework of multi-functional service incorporating sensing targets (STs), information receivers (IRs), and energy receivers (ERs) in an active reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transfer (ISCPT) system. In the proposed system, we aim to maximize the weighted sum of the received radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the transmit beamforming at the multi-functional base station (MFBS), the coefficients of active RIS, and the radar receive filter coefficients. Meanwhile, the constraints of the SINR of IRs, energy harvesting (EH) requirements of ERs, the power budget for the MFBS and active RIS, and the amplification gain should be satisfied. To guarantee the generality of formulated problems, we further incorporate the self-interference effects of echo signals, multi-target echo interference, simultaneous detection of multiple STs, and a nonlinear EH model into the generalized system model. Due to the presence of echo interference and multi-target echo interference, the MFBS transmits the dedicated sensing signal with the communication to enhance the sensing performance. The formulated problem is tackled by developing an efficient alternating optimization (AO) algorithm combined with fractional programming (FP) and majorization-minimization (MM) techniques. Finally, the numerical results reveal the impact of system parameters on the sensing performance, the trade-off relationship between multiple functionalities, and the deployment strategy of RIS. The main findings are as follows: 1) Active RIS is remarkably superior to passive RIS for ISCPT systems, especially for closer to the receivers with a 40 dB performance gain. 2) Comparatively, the radar sensing SINR is more sensitive to the number of active RIS units, while the SINR of IRs is more sensitive to the number of antennas at the base station. These results demonstrate that the proposed system holds the potential for practical deployment.

Low Earth orbit (LEO) satellite networks are expected to support heterogeneous services, including enhanced mobile broadband (eMBB) communications, massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). However, existing coexistence schemes, such as puncturing and superposition, struggle to achieve an effective trade-off among reliability, latency, and spectral efficiency due to their limited degrees of freedom (DoF). To address this challenge, we propose a novel rate-splitting grant-free (RS-GF) transmission scheme that integrates rate-splitting multiple access (RSMA) with grant-free random access (GF-RA) to efficiently support heterogeneous quality of service (QoS) requirements. The high-rate eMBB user employs single-layer rate splitting (RS) over the entire slot, while short-packet Internet-of-Things (IoT) devices associated with URLLC and mMTC adopt GF-RA via single mini-slot transmissions. Building on this RS-GF framework, we analyze the outage performance of the proposed scheme. Specifically, we derive the average packet error probability (PEP) of IoT devices in the finite blocklength (FBL) regime and analyze the eMBB user’s outage probability under imperfect successive interference cancellation (SIC) and mini-slot collisions. On this basis, we present simplified analytical solutions for sparse and dense IoT deployment scenarios, and Monte Carlo simulations validate our analytical derivations. Simulation results demonstrate that the proposed RS-GF scheme outperforms state-of-the-art solutions for service coexistence in LEO satellite networks.

As edge applications demand real-time processing with limited bandwidth and energy, traditional communication systems face challenges to meet performance requirements due to the centralized architecture and redundant data transmission. To address these challenges, we propose a UAV-assisted semantic edge computing network that leverages UAV mobility and semantic communication. We formulate a joint optimization problem involving UAV trajectory, data allocation, and semantic extraction to maximize the semantic processing rate. To solve this problem, we develop a hybrid deep deterministic policy gradient (H-DDPG) algorithm that integrates deep reinforcement learning (DRL) with convex optimization via block coordinate descent (BCD), thereby enabling efficient joint decision-making across tightly coupled variables. Furthermore, we propose a hybrid diffusion deep deterministic policy gradient (H-D3PG) algorithm, which incorporates denoising diffusion models into the DRL framework. By addressing the limited adaptability of deterministic strategies, this design enhances policy expressiveness and stability. As a result, the algorithm enables adaptive trajectory control under time-varying semantic tasks and wireless channel conditions in UAV-assisted edge networks. Simulations show that H-D3PG improves the semantic processing rate by up to 38.8% while reducing energy consumption compared to Raw Data Transmission.

Uncrewed aerial vehicles (UAVs)-assisted low-altitude communication networks have emerged as a promising solution for extending air-to-ground communication coverage and services. However, UAV-assisted communications are highly susceptible to jamming attacks due to its high probability of line-of-sight links. In this paper, we design an embodied intelligence-enhanced low-altitude communication network under malicious jammers, where multiple UAVs act as embodied intelligent agents to collaborate and jointly optimize power allocation and spectrum allocation to minimize transmission delay, while guaranteeing quality of service requirements against jamming attacks. Considering the non-convex problem and highly dynamic wireless environments, we propose an embodied multiagent deep reinforcement learning (E-MA-DRL)-based intelligent resource allocation approach to jointly optimize the communication resource, where embodied intelligent agents (UAVs) sense communication states, learn to make decisions and perform resource allocation actions. To enhance learning efficiency and performance, we then design prioritized experience replay (PER) and transfer learning (TL) in a double deep Q-network (DDQN) algorithm, to smartly schedule the communication resource and reduce the effect of jamming attacks and inter-channel interference. Simulation results show that the proposed approach significantly reduces communication delay and improves the probability of successful transmission in low-altitude communication networks against jamming attacks.

Cooperative multi-Unmanned Aerial Vehicle (UAV) systems for dynamic tasks, such as target tracking, face challenges in maintaining efficient coordination when agents become inactive upon task completion. This dynamic behavior introduces heterogeneous input streams to centralized state evaluation components (Critics) in multi-agent reinforcement learning frameworks, impairing coordination and increasing network resource demands, such as bandwidth and latency. This work proposes a novel Learned Inactive Feature Mixture-of- Experts (LIF-MoE) Critic to address the above issue, which jointly learns a compact inactive representation and applies expert-based specialization to diverse agent inputs. LIF-MoE replaces uninformative inactive observations with a learnable feature vector to provide meaningful representations for inactive states, while employing per-agent MoE processing with sparse routing to enable specialized handling of heterogeneous inputs. This approach enhances state representation for accurate value estimation, thus facilitating efficient coordination of the UAV swarms. Simulation results validate that LIF-MoE significantly improves task performance and reduces mission times compared to baselines, with pronounced advantages in complex scenarios.

Federated Learning (FL) offers promising solutions for deploying AI in wireless networks, allowing resourceconstrained devices to collaboratively train machine learning models, and reducing deployment costs. However, FL faces challenges due to device heterogeneity and unreliable communication links, which extend training time. Unmanned Aerial Vehicles (UAVs), with their flexibility and deployment advantages, have emerged as valuable assets in addressing these limitations by enhancing line-of-sight communication and providing proximal computational resources. This paper proposes a UAV-assisted FL framework that jointly optimizes resource allocation, task loads, and UAV trajectories to minimize FL completion time. Through a block coordinate descent (BCD) approach, our framework addresses the formulated joint optimization problem. Simulation results demonstrate that our proposed framework effectively balances resource allocation and significantly reduces FL completion time compared to benchmark schemes.

The exponential increase in connected user devices poses scalability challenges for centralized cloud computing. Mobile Edge Computing (MEC) and Fog Computing alleviate latency by deploying computation and storage resources closer to end-users. However, due to the resource limitations, heterogeneity, and dispersed nature of edge servers, there is a need to jointly optimize service caching and application placement strategies to enhance service quality. Given the widespread use of containerized services at the edge, we propose a distributed caching scheme that allows all edge nodes to cache services at the granularity of container image layers. This collaborative caching approach reduces the real-time latency, bandwidth consumption, and caching costs associated with retrieving and initializing applications. Additionally, to address the variability in application popularity across different edge regions, we model application popularity using a Zipf distribution and construct a multi-slot joint optimization model for caching and deployment decisions based on deployment cost, application startup time, and average delay. We then propose a two-stage optimization method to solve this model, demonstrating through comparison with centralized and P2P models the effectiveness of the proposed approach.

Federated Learning (FL) is an innovative learning paradigm that allows multiple devices to collaboratively train a shared model without uploading the raw data to the cloud, thereby enhancing privacy and security. Leveraging Mobile Edge Computing (MEC), Hierarchical Federated Learning (HFL) can further reduce the communication overhead, thereby increasing the efficiency and scalability of FL systems by enabling model aggregation at the network edge. However, this framework often encounters security challenges, such as single points of failure and the risk of malicious model tampering. To address these challenges, researches have employed blockchain technology to enhance the security of FL systems, but most of these solutions incur significant resource burdens due to the intensive computation demands of blockchain consensus mechanisms, such as Proof-of-Work (PoW). In this work, we aim to explore a lightweight blockchain-enabled federated learning (LBFL) framework that utilizes the Delegated Proof-of-Stake (DPoS) consensus mechanism, which employs a simple voting process to elect a small number of candidate block producers (known as delegates) to aggregate the FL model and produce blocks. This framework significantly reduces the number of consensus nodes, thereby minimizing resource consumption during the consensus process. We study the joint optimization of mobile device association, bandwidth allocation, computing frequency management, and block producer selection, aiming to minimize the overall delay and energy consumption. To address the challenges posed by discrete and continuous decision variables, we decompose the problem into three sequential subproblems and solve them iteratively. Simulation results show that compared with the existing benchmarks, the proposed scheme can reduce overall delay and energy consumption by 15% to 22%.

With the rise of AI-Generated Content (AIGC) services in wireless networks, efficient and high-quality distributed training of Large Language Models (LLMs) has become essential for enabling the large-scale application of next generation AI technologies. However, the extensive parameters of LLMs impose significant demands on memory, computing power and communication resources in heterogeneous networks. To efficiently utilize the dispersed network resources, this paper presents a First-Pipeline- Then-Federated Learning (FPTFL) approach with a hybrid parallel scheduling strategy to facilitate the training of Transformer-based LLMs. We propose a block-wise splitting mechanism to partition the Transformer's encoder into distinct segments, which are deployed cross individual devices. The encoder parameters and intermediate smashed data are uploaded to the edge server, where the whole model is updated through federated aggregation. Particularly, we develop a fine-grained computation-efficient method based on pipeline parallelism, enabling the segments to cooperatively train the entire encoder. An optimization problem is formulated to determine the LLM segments and the number of micro-batches under network resource constraints, with the goal of minimizing the total latency of LLM training services. Simulation results demonstrate that our approach enables Transformer-based model training on resource-constrained devices, preserves model performance, and reduces waiting time.

Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads.

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

With the continuous advancement of network technologies and hardware devices, computation-intensive and latency-sensitive tasks have emerged worldwide, requiring networks to provide extensive coverage, low latency, and robust computing capabilities. Leveraging the global coverage of LowEarth Orbit (LEO) satellites and the flexible resource invocation capabilities of the Computing Power Network (CPN), we propose a Satellite-Terrestrial Computing Power Network (ST-CPN) framework that integrates both strengths. In this framework, tasks can be offloaded to satellites closer to users for processing, ensuring high-quality services anytime and anywhere. However, due to the dynamic nature of the network and the limited resources of individual nodes, efficiently executing complex dependent tasks presents significant challenges. Therefore, we investigate the dependent task offloading problem in the dynamic ST-CPN environment. Considering dynamic changes of topology and available resources caused by satellite mobility, we propose a Time-Space-Varying Resource Graph (TSVRG) to capture the status of the communication, storage, and computation resources. On this basis, given that individual nodes struggle to process dependent tasks, we offload multiple subtasks of a task to different nodes for collaborative processing. In this paper, we model the task as a Directed Acyclic Graph (DAG) and transform the offloading problem into a mapping problem from the DAG to TSVRG. We then introduce a Delay Predictionbased Graph Mapping Algorithm (DPGMA) to address this problem. Simulation results indicate that our scheme achieves better performance than the benchmark schemes.

With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we present Bc2FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc2FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc2FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

The rapid proliferation of Internet of Things (IoT) devices has increased the variety of devices and data traffic, making data management and analysis more complex. This complexity has raised the demand for efficient device identification methods to ensure the smooth operation of the network. Conventional identification methods rely on Machine Learning (ML) and Deep Learning (DL), which either suffer from unstable feature engineering or rely on large labeled datasets with confined representation. To overcome these shortcomings, generic hybrid representations of raw traffic are essential for precise device identification. Additionally, existing work mainly investigated device identification in clouds, incurring high network latency and computation costs. A few studies have identified IoT devices in edge, but such methods used simple neural networks, resulting in incomplete representation and redundant operations. Comprehensive representations typically require complex models, but the limited resources at the edge are insufficient to execute these models. Therefore, this paper proposes a lightweight hybrid device identification (LHDI) approach, which achieves efficient device identification in resource-constrained edge nodes. First, we adopt the unsupervised pre-training to enhance the characterization of network packets. Second, we devise LHDI by integrating bidirectional long short-term memory (Bi-LSTM) and Transformerbased blocks in a parallel configuration. Third, a pruning framework is introduced to automatically reduce Transformer parameters using structured sparsity methods without retraining. By reducing redundant neural network parameters, the proposed lightweight model facilitates effective device identification in edge, without losing representation capabilities. Experimental results demonstrate that our methods deliver high accuracy with low cost compared to others.

Federated learning allows clients to share model gradients instead of privacy-sensitive data, which can solve the issue of data silos, but lead to the problem of data privacy leakage due to the model gradient revealing the characteristics of the training data. Privacy-preserving federated learning based on homomorphic encryption schemes (HE-based PPFL) can properly solve the issues of participantsfs data privacy leakage, but they encounter some new challenges. Existing PPFL-based single-key homomorphic encryption schemes face the problem that clients can obtain othersf model gradients due to the shared key and PPFL-based multi-key homomorphic encryption schemes face the issues of incomplete privacy protection for models and high communication overhead due to the requirement of the collaborated decryption. Moreover, existing PPFL schemes either assume the server is always honest or the verification method is unreliable and expensive. To tackle these emerging challenges in HE-based PPFL, we propose an enhancing privacy-preserving and verifiable federated learning scheme. Specifically, we first construct a novel multi-key homomorphic encryption algorithm that achieves single-key decryption instead of the collaborated decryption in traditional PPFL-based multi-key homomorphic encryption. Meanwhile, we design a blockchain-based public verification method for the global model by applying a vector homomorphic hash, which can properly solve the issues of unreliable and expensive global model verification of the existing global model verification methods. Formal security analysis shows that the proposed scheme can well provide complete privacy protection and guarantee the integrity of the global model. Extensive experiments demonstrate that the proposed schemes can keep high accuracy (≈95%) compared with existing differential privacy-based PPFL schemes (≤90%). Meanwhile, the proposed schemes can achieve no decryption share size (0MB) compared to existing HE-based PPFL schemes and efficient verification compared wit

With the development of intelligent transportation systems (ITSs), digital twin (DT) technology is becoming increasingly widespread in the application of connected automated vehicles (CAVs) to enhance driving safety. However, when DT systems are used for driving safety decisions through virtual control of reality and virtual reflection of reality, decision errors may occur, which can be fatal for the driving safety of CAVs. The main reasons are attributed to three aspects: 1) the accuracy; 2) the communication delay; and 3) the safety control of the DT system. In this article, we study to improve the accuracy and safety of the DT system decisions with communication delay. First, we considered powertrain factors to construct a high-precision and high-fidelity DT system. We use the Goodness-of-Fit Functions (GoFs) and Measure-of-Performances (MoPs) to fit the vehicle’s model and carry out error measurements in the DT system. Second, we analyze the stability of the DT system using plant stability and string stability under time delay. The effective range of time delay ensures the accuracy and stability of the DT system, and provides a safety constraint for the design of the CAV’s controller. Finally, we propose a DT-assisted robust safety-critical traffic control (RSTC) strategy based on the control barrier functions (CBFs). This strategy ensures the driving safety of CAVs with preceding and following vehicles while maintaining traffic stability. The theoretical analysis and experimental results present that the proposed scheme can effectively avoid conflicts and crash risks to ensure driving safety.

The deployment of the microservice architecture in edge networks presents new opportunities for supporting latency-sensitive network services. However, most such services are both computation-intensive and energy-consuming, posing significant challenges for edge nodes with constrained computing resources and energy supply. Therefore, designing efficient microservice orchestration strategies to reduce service latency and network energy consumption is essential but highly challenging. Due to frequent communication among microservices, service deployment and request routing are tightly coupled, which lead to a complex joint optimization problem. This complexity further increases when considering large-scale microservices under multi-instance modeling and fine-grained analysis. Nevertheless, previous work has failed to address these challenges and largely overlooked the balance between latency and energy consumption. To overcome these issues, this paper proposes an energy-latency balanced microservice orchestration method to jointly minimize service latency and energy usage. First, we adopt multi-instance modeling to enable precise end-to-end latency analysis, and integrate an energy model to quantify overall network consumption. Then, we design the Node Ranking Matrix-based Microservice Orchestration Algorithm (NRMA), which dynamically selects high-ranking nodes based on centrality and energy metrics, thereby balancing latency and energy in the deployment stage. Moreover, we use the proportional routing strategy that distributes user request traffic according to the number of deployed instances, preventing node overload and reducing cross-node communication. Experimental results show that the proposed method is significantly better than the baseline algorithms in terms of latency and energy consumption, and achieves significant results.

Existing federated learning frameworks are primarily designed for single-modal data. However, real-world scenarios require processing multi-modal data on heterogeneous devices. The gap between existing methods and real-world scenarios presents challenges in processing multimodal data on heterogeneous devices, significantly impacting model training efficiency. To address these issues, we propose a multimodal federated learning framework, which integrates multimodal algorithms with a semi-synchronous training method. The multimodal algorithm trains local autoencoders on different data modalities. By leveraging the similarity of encodings across different modalities with the same data labels, we further train and aggregate these local autoencoders into a global autoencoder, which is then deployed on the blockchain to perform downstream classification tasks. In the semi-synchronous training method, each device updates its parameters independently during a round. At the end of each round, a global aggregation combines the updates from devices. We conduct an empirical evaluation of our framework on various multimodal datasets, including Opportunity (Opp) Challenge, mHealth, and UR Fall Detection datasets. Experimental results demonstrate that our federated learning framework, outperforms the state-of-the-art multimodal frameworks on three multimodal datasets, achieving an average accuracy improvement of 9.07%. Furthermore, in terms of training speed, MMFed is obviously superior to synchronization strategies when it is extended to a large number of clients.

In the Web 3.0 ecosystem, blockchain and Artificial Intelligence of Things (AIoT) construct the infrastructure, where blockchain transaction semantic detection (BTSD) aims to enhance blockchain security by identifying illegal transactions through distributed miners executing AI algorithms. However, the computational cost of performing semantic detection discourages miners from participating without adequate incentives. Existing studies focus on algorithmic aspects of BTSD, which generally ignore the critical issue of incentive mechanism. To fill this gap, we propose the first incentive-based BTSD framework in the transaction pool phase, emphasizing how incentives affect the behavior of miners and users. We use evolutionary game theory to model miner-user interactions and define three key scenarios to simulate the impact of reward decay and penalty factors on system dynamics. Our results demonstrate that adjusting these parameters significantly influences the number of miners engaging in semantic detection and users initiating legitimate transactions. Under certain conditions, a well-designed incentive mechanism can lead to an Evolutionary Stable Strategy (ESS), thereby achieving systemic stability. This study introduces a novel incentive mechanism for BTSD during the transaction pool phase and validates its effectiveness through both theoretical insights and numerical solutions to enhance blockchain security.

Industrial Cyber-Physical Systems (CPS) have made significant strides in recent years, driving the future of manufacturing. However, for further advancement in Cloud-Fog Automation (CFA), several challenges remain: rigid sensor sampling, inflexible communication configurations, insufficient coordination between cloud and fog resources, and a lack of integration between sensing, communication, and computing for effective control. To address these issues, this article presents SeCo4, an intelligent control framework for the co-design of sensing, communication, and computing in industrial CPS. The SeCo4 optimization problem is analyzed and divided into two sub-problems: a multi-controller cloud resource competition problem, formulated with a combinatorial auction to enable multi-controller competition for additional cloud resources and improve control performance; and a joint resource optimization problem for sensing, communication, and computing, modeled using a Mixed Integer Programming (MIP) problem to minimize control costs. Given the interdependence of these sub-problems, a hierarchical solution based on the online matching mechanism and the heuristic approach is developed to iteratively find the optimal solution. Finally, extensive simulations demonstrate the effectiveness and superiority of the proposed approach.

Distributed Artificial Intelligence-Generated Content (AIGC) has attracted significant attention, but two key challenges remain: maximizing subjective Quality of Experience (QoE) and improving energy efficiency, which are particularly pronounced in widely adopted Generative Diffusion Model (GDM)-based image generation services. In this paper, we propose a novel user-centric Interactive AI (IAI) approach for service management, with a distributed GDM-based AIGC framework that emphasizes efficient and cooperative deployment. The proposed method restructures the GDM inference process by allowing users with semantically similar prompts to share parts of the denoising chain. Furthermore, to maximize the users' subjective QoE, we propose an IAI approach, i.e., Reinforcement Learning With Large Language Models Interaction (RLLI), which utilizes Large Language Model (LLM)-empowered generative agents to replicate users interactions, providing real-time and subjective QoE feedback aligned with diverse user personalities. Lastly, we present the GDM-based Deep Deterministic Policy Gradient (G-DDPG) algorithm, adapted to the proposed RLLI framework, to allocate communication and computing resources effectively while accounting for subjective user traits and dynamic wireless conditions. Simulation results demonstrate that G-DDPG improves total QoE by 15% compared with the standard DDPG algorithm.

Mega-satellite constellations have the potential to leverage inter-satellite links to deliver low-latency end-to-end communication services globally, thereby extending connectivity to underserved regions. However, harsh space environments make satellites vulnerable to failures, leading to node removals that disrupt inter-satellite networking. With the high risk of satellite node failures, understanding their impact on end-to-end services is essential. This study investigates the importance of individual nodes on inter-satellite networking and the resilience of mega satellite constellations against node failures. We represent the mega-satellite constellation as discrete temporal graphs and model node failure events accordingly. To quantify node importance for targeted services over time, we propose a service-aware temporal betweenness metric. Leveraging this metric, we develop an analytical framework to identify critical nodes and assess the impact of node failures. The framework takes node failure events as input and efficiently evaluates their impacts across current and subsequent time windows. Simulations on the Starlink constellation setting reveal that satellite networks inherently exhibit resilience to node failures, as their dynamic topology partially restore connectivity and mitigate the long-term impact. Furthermore, we find that the integration of rerouting mechanisms is crucial for unleashing the full resilience potential to ensure rapid recovery of inter-satellite networking.

Integrated sensing and communication (ISAC) can ensure the secure transmission through sensing the eavesdroppers. However, the information obtained by a single base station (BS) is difficult to accurately track the moving eavesdroppers. In this paper, we investigate the sensing-assisted secure communication, where multiple BSs cooperatively sense an unmanned aerial vehicle (UAV) target, also regarded as an aerial eavesdropper. We propose a two-stage scheme to ensure the secure transmission. In the first stage, we estimate the current location and velocity of the UAV through fusing the sensing information from these BSs, to further predict the location in the next time slot. Meanwhile, the prediction variance is derived to bound the errors. In the second stage, we tackle the robust optimization with the prediction errors. Considering the tradeoff between the security and sensing performance, the weighted sum of secrecy rate and radar mutual information rate is maximized via jointly designing the user scheduling and beamforming, which is non-convex. Thus, we decompose it into two subproblems, where the scheduling is obtained via the branch and bound algorithm and the beamforming vectors are optimized by the successive convex approximation. In the end, we design a robust algorithm to address the original problem. Simulation results are shown to prove the efficiency of the proposed scheme.

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are spherically or uniformly distributed. Different from the existing works, this paper investigates two particular motion patterns of radiosondes in a typhoon, which significantly challenges the theoretical analysis. According to their particular motion patterns, this paper first separately models the distributions of horizontal and vertical distances from a radiosonde to its receiver. Secondly, this paper derives the closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of a radiosonde’s three-dimensional (3D) propagation distance to its receiver. Thirdly, this paper derives the analytical expression of the uplink CP for any radiosonde in the network. Finally, extensive numerical simulations are conducted to validate the theoretical analysis, and the influence of various network design parameters is comprehensively discussed. Simulation results show that when the signal-to-interference-noise ratio (SINR) threshold is below -35 dB, and the density of radiosondes remains under 0.01/km3, the uplink CP approaches 26%, 39%, and 50% in three patterns.

Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) in space-air-ground integrated networks (SAGINs) provide a promising solution for enhancing communication, computing, and storage services for the increased number of Internet of Things (IoT) devices. However, jamming attacks and co-channel interference severely degrade the network performance due to wide field of line of sight. In this work, we propose a resource scheduling method to jointly optimize the channel selection, UAV deployment and task offloading to minimize both the communication and computing latency, under the malicious jamming attacks and resource constraints. Considering the highly dynamic and complex nature of the SAGIN environment and the multi-UAV collaboration framework, we then design an advanced anti-jamming-driven multi-agent deep reinforcement learning (MADRL) method based on the multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm. This method adaptively adjusts the resource scheduling strategy to enhance the network’s ability to withstand jamming attacks, reduce total network latency, and maintain real-time services even under unfavorable conditions. Simulation results show that our proposed method significantly outperforms existing benchmark methods in terms of latency reduction, signal-to-interference-plus-noise ratio (SINR) improvement, and overall network robustness under jamming attacks. For example, the proposed MATD3-SAGAJ method reduces latency by about 10% and improves the SINR satisfaction ratio from around 91% to nearly 95% compared to the current optimal benchmark method.

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature.

Unmanned Aerial Vehicles-based Multiple Targets Tracking (UAV-MTT) has been mainstream in serving mission-critical scenarios for public safety, such as hit-and-run tracking and border patrol. Nonetheless, it is challenging to implement high-efficiency UAV topology control due to the variable moving speeds of targets and the limited sensing and communication resources of UAVs. To address the problem, we propose a terminal-edge cooperative Digital Twin (DT) framework for real-time and accurate MTT. Based on the DT technology, we achieve joint optimization of local and global UAV topologies to track targets with diverse speeds. Explicitly, we construct time-spatial DT models based on temporal and spatial information of targets and UAVs. The DT models can instruct UAVs to dynamically adjust position relations among one-hop neighbors for local topology optimization using our proposed Time Spatial Graph Learning based DT (TSGL-DT) algorithm. UAVs can use the optimization results to invite feasible neighbors to track low-speed moving targets. Our DT models can also allocate feasible UAVs to connect suitable local topologies for global topology optimization. It can achieve cooperative MTT to track high-speed moving targets. The experiment results demonstrate that our solution reduces the MTT latency by 41.2% while improving the successful tracking ratio delivery ratio by 15.6% on average compared to state-of-the-art benchmarks.

Vehicular edge computing (VEC) helps improve the task computational performance of vehicles on roads but has difficulty in defending against eavesdropping and selfish attacks simultaneously. In this paper, we design a reputation-based smart contract with blockchain and propose a multi-agent reinforcement learning (RL) based secure offloading scheme for VEC against both eavesdropping and selfish attacks. This scheme has a three-level hierarchical structure for each vehicle and uses the reputations obtained from the blockchain as the basis to optimize the edge node selection, offloading ratio, and power allocation, which aims to reduce the task computational latency, the vehicle energy consumption and eavesdropping rate. By using a punishment function based on the constraints, this scheme avoids exploring dangerous policies that can cause task failure or severe data leakage. A multi-agent deep RL-based secure offloading scheme is proposed for vehicles with sufficient resources, which evaluates the long-term risk rather than the punishment function to further improve the secure offloading performance. The regret bound is analyzedand the cumulative reward upper bound is provided. Simulation results verify the effectiveness of our schemes as compared with the benchmark.

To boost the encoder stealing attack under the perturbation-based defense that hinders the attack performance, we propose a boosting encoder stealing attack with perturbation recovery named BESA. It aims to overcome perturbation-based defenses. The core of BESA consists of two modules: perturbation detection and perturbation recovery, which can be combined with canonical encoder stealing attacks. The perturbation detection module utilizes the feature vectors obtained from the target encoder to infer the defense mechanism employed by the service provider. Once the defense mechanism is detected, the perturbation recovery module leverages the well-designed generative model to restore a clean feature vector from the perturbed one. Through extensive evaluations based on various datasets, we demonstrate that BESA significantly enhances the surrogate encoder accuracy of existing encoder stealing attacks by up to 24.63% when facing state-of-the-art defenses and combinations of multiple defenses.

In next-generation wireless networks, integrated satellite-terrestrial networks are regarded as a pivotal solution for supporting seamless coverage and elevated data rates, but the physical layer security performances are severely degraded under both jamming and eavesdropping attacks due to wide field of line of sight. Thus, this paper designs an aerial hybrid active-passive reconfigurable intelligent surface (aerial hybrid RIS) communication system to enhance secure and reliable communication for integrated satellite-terrestrial networks, where an active eavesdropper aims to jam legitimate channels and eavesdrop on any data stream from RIS simultaneously. Specifically, we propose a resource scheduling approach that jointly optimizes the position of the aerial RIS, the hybrid beamforming matrix, the satellite beamforming design, and the satellite transmission power to maximize the ground users’ (GUs) secrecy rate under quality of service (QoS) requirements. To address the optimization problem in complex and dynamic communication environments, we reformulate the problem as a reinforcement learning (RL) problem and propose a secure resource scheduling method based on the relay hindsight experience replay-softmax deep double deterministic policy gradients (RHER-SD3) algorithm. The proposed RHER-SD3 algorithm effectively schedules the secure hybrid active-passive beamforming matrix, the aerial position of the RIS, the satellite beamforming vectors, and the satellite power allocation to avoid both jamming and eavesdropping attacks, even though the behavior information of the attacker is imperfect. Simulation results demonstrate that the proposed method outperforms existing approaches in improving system secrecy performance and QoS satisfaction against hybrid attacks.

Space-air-ground (SAG) integrated heterogenous networks can provide pervasive intelligence services for various ground users (GUs). The network can help cellular networks release network resources and alleviate congestion pressure. Moreover, one important application of the network is that digital twin (DT) can enable nearly-instant wireless connectivity and highly-reliable data mapping from physical systems to digital world in a real-time fashion. The integration of SAG and DT (SAG-DT) reduces the gap between data analysis and physical status, which can further realize robust edge intelligence services. However, the random computation task arrival, time-varying channel gains, and the lack of mutual trust among ground GUs hinder better quality of service in the promising SAG-DT network. In this paper, we envision a SAG-DT integrated blockchain model to transfer the task data to the aerial network, and then perform the computation offloading, energy harvesting and privacy protection. Moreover, we propose a Lyapunov-aided multi-agent deep federated reinforcement learning (MADFRL) algorithm framework to optimize the CPU cycle frequency, the size of block, the number of DTs, and harvested energy to minimize the execution costs and privacy overhead. Extensive performance analyses indicate that the MADFRL algorithm framework can strengthen the data privacy via blockchain verification mechanism and approaches the optimal performance on the basis of lower computation complexity. Finally, simulation results corroborate that the proposed Lyapunov-aided MADFRL algorithm is superior to advanced benchmarks in terms of execution costs, task processing quantities and privacy overhead.

Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intelligent control in terrestrial industrial cyber-physical systems, whose system-level privacy security can be strengthened via blockchain based consensus protocol. Moreover, digital twin (DT) can shorten the gap between physical unities and digital space to enable instant data mapping in SGTNs environments. However, complex multi-modal network environments, such as stochastic task size, dynamic low earth orbit location, and time-varying channel gains, hinder better performance metrics in terms of energy consumption, throughput and privacy overhead. Hence, we establish a SGTN integrated cloud-fog automation model to transfer task data to low earth orbit satellites, and then execute broad communication access, powerful computation offloading, and efficient twin control. Next, we propose a Lyapunov stability theory based multi-modal federated learning (LST-MMFL) method to optimize the battery energy, the size of block, computation frequency, and the number of twin control for minimizing the total energy consumption and privacy overhead. Furthermore, we design a novel blockchain based transaction verification protocol to strengthen privacy security, derive performance upper bounds of SGTN model, and fulfill the long-term average task as well as energy queue constraints. Finally, massive simulation results show that the proposed LST-MMFL algorithm outperforms existing state-of-the-art benchmarks in line with energy consumption, available battery level, networked control and privacy protection overhead.

The integration of time-sensitive networking (TSN) and fifth-generation (5G) offers a promising solution for real-time and reliable data transmission in the Industrial Internet of Things (IIoT). However, current research focuses on traffic scheduling in TSN-5G networks to support low latency. New challenges arise when TSN-5G networks leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) to achieve low latency and high reliability. Simply combining TAS and FRER (SCTF) requires scheduling all time-triggered (TT) flows and their replica flows, which substantially increases the computational complexity of gate control lists (GCLs) and severely weakens scheduling capabilities. Moreover, the packet elimination function (PEF) in FRER may induce packet misordering. In this paper, we propose an efficient and fault-tolerant time-aware shaper (EF-TAS) mechanism for TSN-5G networks. EF-TAS only allocates timeslots for TT flows, while replica TT (RT) flows are delivered using a best-effort strategy. Due to the potential violation of deadlines in RT flows, we design an adaptive cyclic GCL window (ACGW)-based hybrid scheduling (AHS) algorithm to schedule TT and RT flows differentially. The AHS algorithm utilizes network calculus to ensure the timely arrival of RT flows without affecting the deterministic transmission of TT flows. In particular, we provide upper bounds on the amount of reordering to quantify the disorder caused by PEF and analyze the impact of introducing the packet ordering function (POF) on EF-TAS performance. The evaluation results show that EF-TAS not only meets the reliability and deadline requirements but also significantly reduces the total number of GCL entries and the computation time of GCLs compared to state-of-the-art methods.

The explosive growth of the Internet of Things (IoT) has dramatically increased the demand for secure mechanisms to protect against unauthorized access and attacks. Traditionally, expensive Software-Defined Radios (SDRs) have been utilized to gather IoT physical features, which are critical for reliable authentication. However, the high cost of SDRs makes them impractical for widespread deployment across the vast and diverse IoT ecosystem. In contrast, this paper presents AUTHFi, a novel cross-technology device authentication framework that transforms the SDR approach for collecting and authenticating IoT device signals (e.g., ZigBee and Bluetooth) by utilizing commercial WiFi devices. Specifically, AUTHFi leverages the recent advances in Cross-Technology Communication (CTC) to reconstruct the partial waveform of IoT transmission, thus eliminating the requirement for expensive SDRs. AUTHFi requires us to address several unique challenges. First, AUTHFi compensates for signal losses of the partial waveform to get more signal information. Then, it introduces an enhanced Carrier Frequency Offset (CFO) estimation and a fusion neural network that combines CFO and the reconstructed waveform for accurate device authentication. We implement AUTHFi based on RTL8812au (commodity WiFi) and CC2652P (commodity ZigBee/Bluetooth). Our thorough evaluation confirms that AUTHFi offers reliable authentication under various settings, achieving a maximum accuracy of 94.2%.

Wireless sensing systems have been employed in the field of healthcare, environment monitoring, and smart agriculture, etc. Since the freshness and accuracy indicators of the sensing data are critical to wireless sensing systems, it is of great significance to ensure their performances simultaneously, i.e., the Age of Information (AoI) and information entropy of the sensing data should be jointly optimized. In this regard, we first establish the wireless sensing system models, including AoI and information entropy expressions. Next, from the information entropy viewpoint, we theoretically analyze an impact of the AoI on data accuracy. Then, we formulate the joint optimization problem of AoI, information entropy, and sensing energy consumption. Furthermore, we propose two numerical algorithms to solve the formulated problem in the known or unknown transmission environment, respectively. Finally, we evaluate the correctness and effectiveness of our proposals under various parameter settings, where the proposed scheme can obtain a better sum-weighted performance on AoI, information entropy, and sensing energy consumption than baselines in the literature.

Generative diffusion models like Stable Diffusion are at the forefront of the thriving field of generative models today, celebrated for their robust training methodologies and high-quality photorealistic generation capabilities. These models excel in producing rich content, establishing them as essential tools in the industry. Building on this foundation, the field has seen the rise ofpersonalized content synthesisas a particularly exciting application. However, the large model sizes and iterative nature of inference make it difficult to deploy personalized diffusion models broadly on local devices with heterogeneous computational power. To address this, we propose a novel framework for efficient multi-user offloading of personalized diffusion models. This framework accommodates a variable number of users, each with different computational capabilities, and adapts to the fluctuating computational resources available on edge servers. To enhance computational efficiency and alleviate the storage burden on edge servers, we propose a tailored multi-user hybrid inference approach. This method splits the inference process for each user into two phases, with an optimizable split point. Initially, a cluster-wide model processes low-level semantic information for each user's prompt using batching techniques. Subsequently, users employ their personalized models to refine these details during the later phase of inference. Given the constraints on edge server computational resources and users' preferences for low latency and high accuracy, we model the joint optimization of each user's offloading request handling and split point as an extension of the Generalized Quadratic Assignment Problem (GQAP). Our objective is to maximize a comprehensive metric that balances both latency and accuracy across all users. To solve this NP-hard problem, we transform the GQAP into an adaptive decision sequence, model it as a Markov decision process, and develop a hybrid solution combining deep reinforcement learning with convex optimization techniques. Simulation results validate the effectiveness of our framework, demonstrating superior optimality and low complexity compared to traditional methods. All related code, datasets, and fine-tuned models are available athttps://github.com/wty2011jl/E-MOPDM.

Mobile edge computing (MEC) is a promising technology that enhances the efficiency of mobile blockchain networks, by enabling miners, often acted by mobile users (MUs) with limited computing resources, to offload resource-intensive mining tasks to nearby edge computing servers. Collaborative block mining can further boost mining efficiency by allowing multiple miners to form coalitions, pooling their computing resources and transaction data together to mine new blocks collaboratively. Therefore, an MEC-assisted collaborative blockchain network can leverage the strengths of both technologies, offering improved efficiency, security, and scalability for blockchain systems. While existing research in this area has mainly focused on the singlecoalition collaboration mode, where each miner can only join one coalition, this work explores a more comprehensive multicoalition collaboration mode, which allows each miner to join multiple coalitions. To analyze the behavior of miners and the edge computing service provider (ECP) in this scenario, we propose a novel two-stage Stackelberg game. In Stage I, the ECP, as the leader, determines the prices of computing resources for all MUs. In Stage II, each MU decides the coalitions to join, resulting in an overlapping coalition formation (OCF) game; Subsequently, each coalition decides how many edge computing resources to purchase from the ECP, leading to an edge resource competition (ERC) game. We derive the closed-form Nash equilibrium for the ERC game, based on which we further propose an OCFbased alternating algorithm to achieve a stable coalition structure for the OCF game and develop a near-optimal pricing strategy for the ECP's resource pricing problem. Simulation results show that the proposed multi-coalition collaboration mode can improve the system efficiency by 12.64% ∼ 17.63%, compared to the traditional single-coalition collaboration mode.

Traditional federated learning (FL) frameworks rely on a central server for model coordination among distributed mobile terminals (MTs). The centralization faces two critical challenges, i.e., single point of failure and potential privacy leakage. Differentially private decentralized FL (DP-DFL) has been proposed to address these challenges, wherein the MTs exchange models in a decentralized manner and maintain the differential privacy (DP) guarantee by adding noise to local models before model interaction. However, existing DP-DFL frameworks confront difficulty in achieving the expected privacy and convergence performance, simultaneously. To address this issue, we propose a novel DP-DFL framework (called randomized DP-DFL) that employs a randomized model interaction scheme to lower the model exposure frequency and hence reduce privacy budget consumption. Specifically, the scheme includes two sequential steps, i.e., randomized terminal assignment and randomized model transmission. In Step 1), the model interaction phase of DFL is further divided into several sequential substages. MTs are randomly assigned to each sub-stage. In Step 2), each MT sequentially transmits either a model previously received from its neighbors or its own local model according to the assigned sub-stage order. The proposed scheme enhances the MTs' privacy of DFL since the exposure probabilities of the MTs' local models are significantly reduced via these two randomized steps. Besides, we theoretically analyze the convergence and privacy performance of randomized DP-DFL. In particular, properly tuning the number of sub-stages in randomized DP-DFL can achieve an optimal balance between privacy and convergence. Experimental results show that randomized DP-DFL consistently outperforms traditional frameworks. Compared with baselines, randomized DP-DFL reduces 40.9% privacy loss under the same target accuracy while improving 9.5% learning accuracy under the same privacy loss on EMNIST and CIFAR-10, respectively

The Digital Twin (DT) represents a rapidly advancing technological innovation within the Industrial Internet of Things (IIoT) domain. DT leverages the power of simulation, machine learning, and data mining to facilitate optimal decision-making for physical objects. However, the creation of a dynamic and living digital counterpart comes at a considerable cost. It requires continuous massive data updating and processing every time the physical object changes. As most data collected by IIoT devices are in their original form, such as images and videos, transmitting such data to remote cloud computing will result in large delays. Furthermore, data processing is often a computationally intensive operation, such as image recognition and video coding, making it impractical to perform processing tasks directly in IIoT devices. To overcome this problem, we introduced the Multi-access/mobile Edge Computing (MEC) architecture to enhance capabilities of DT-enabled IIoT devices. IIoT devices can leverage the extra computing resources in MEC to process raw data, transmitting only the calculation results to update the digital counterpart. To efficiently allocate resources between IIoT devices and MEC, we propose a double auction-based resource allocation scheme. The IIoT devices can purchase computing power from MEC, and an iterative double auction scheme is applied to achieve system efficiency within this market. Furthermore, we propose the Win or Learn Fast Algorithm Policy Hill Climbing (Wolf-PHC) algorithm, which enables agents to improve their strategies continuously through participation in auctions. Simulation results demonstrate that this algorithm accelerates the process of market equilibrium convergence.

The escalating demands for Artificial Intelligence-generated content (AIGC) services greatly require computing resources. The edge-cloud market offers an effective solution for AIGC services by integrating, managing, and trading distributed computing resources. Within this novel service market, participants contribute idle resources to support AIGC services to earn income, creating a more flexible market environment. Meanwhile, the generation quality and computing resource requirements of AIGC services are related to input prompts. Therefore, this relationship introduces new challenges, such asthe information uncertainty in input prompts, the inability to model resource continuity, and high-dimensional complexity for optimization.In this paper, we propose a resource fluid equilibrium management strategy for supporting AIGC services within edge-cloud market, termedFluE. To address the challenge of information uncertainty in user prompts, we measure the content value of AIGC prompts by information entropy and introduce a redundancy reduction approach to focus on meaningful information in prompts. To tackle the challenge of the inability to model the continuity provision of computing resources, we utilize the fluid model to ensure seamless resource provision and facilitate a more balanced management of computing resources. To address the challenge of high-dimensional complexity of strategy optimization, we develop a diffusion-based algorithm namedReDiffto reconstruct the target strategy distribution and generate precise and effective optimization decisions. We evaluate our proposed scheme under a dynamic resource provisioning environment. Based on the DiffusionDB dataset, the publicly available real trace of AIGC service prompt, ourReDiffalgorithm achieves up to 69.8% and 77.4% improvements in average social welfare compared to LySAC and CD-PPO, respectively.

Timely updating of Internet of Things (IoT) data is crucial for achieving immersion in vehicular metaverse services. However, challenges such as latency caused by massive data transmissions, privacy risks associated with user data, and computational burdens on metaverse service providers (MSPs) hinder the continuous collection of high-quality data. To address these challenges, we propose an immersion-aware model trading framework that enables efficient and privacy-preserving data provisioning through federated learning (FL). Specifically, we first develop a novel multi-dimensional evaluation metric for the immersion of models (IoM). The metric considers i) the freshness and accuracy of the local model, and ii) the amount and potential value of raw training data. Building on the IoM, we design an incentive mechanism to encourage metaverse users (MUs) to participate in FL by providing local updates to MSPs under resource constraints. The trading interactions between MSPs and MUs are modeled as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains, where MSPs as leaders determine rewards, while MUs as followers optimize resource allocation. To ensure privacy and adapt to dynamic network conditions, we develop a distributed dynamic reward algorithm based on deep reinforcement learning, without acquiring any private information from MUs and other MSPs. Experimental results show that the proposed framework outperforms state-of-the-art benchmarks, achieving improvements in IoM of 38.3% and 37.2%, and reductions in training time to reach the target accuracy of 43.5% and 49.8%, on average, for the MNIST and GTSRB datasets, respectively. These findings validate the effectiveness of our approach in incentivizing MUs to contribute high-value local models to MSPs, providing a flexible and adaptive scheme for data provisioning in vehicular metaverse services.

In the era of 6G, with compelling visions of intelligent transportation systems and digital twins, remote surveillance is poised to become a ubiquitous practice. Substantial data volume and frequent updates present challenges in wireless networks. To address these challenges, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, consisting of two tailored modules. Moreover, the effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework in both energy saving and reconstruction accuracy.

Generative Artificial Intelligenge (GAI) is revolutionizing the world with its unprecedented content creation ability. Large Language Model (LLM) is one of its most embraced branches. However, due to LLM’s substantial size and resource-intensive nature, it is cloud-hosted, raising concerns about privacy, usage limitations, and latency. In this paper, we propose to utilize ubiquitous distributed wireless edge computing resources for real-time LLM inference. Specifically, we introduce a novel LLM edge inference framework, incorporating batching and model quantization to ensure high throughput inference on resource-limited edge devices. Then, based on the architecture of transformer decoder-based LLMs, we formulate an edge inference optimization problem which is NP-hard, considering batch scheduling and joint allocation of communication and computation resources. The solution is the optimal throughput under edge resource constraints and heterogeneous user requirements on latency and accuracy. To solve this NP-hard problem, we develop an OT-GAH (Optimal Tree-search with Generalized Assignment Heuristics) algorithm with reasonable complexity and$\frac {1}{2}$-approximation ratio. We first design the OT algorithm with online tree-pruning for single-edge-node multi-user case, which navigates the inference request selection within the tree structure to miximize throughput. We then consider the multi-edge-node case and propose the GAH algorithm, which recrusively invokes the OT in each node’s inference scheduling iteration. Simulation results demonstrate the superiority of OT-GAH batching over other benchmarks, revealing an over 45% time complexity reduction compared to brute-force searching.

Unmanned aerial vehicles (UAVs) have been widely employed as airborne base stations to enhance terrestrial communications. However, the growing demand for communications, spectrum and energy resources are increasingly in short supply, while malicious jamming from jammers threatens the communications reliability. To address these challenges, we propose a joint reliable channel selection and power control approach for multi-UAV-enabled communications networks under malicious jamming attacks, with the goal of maximizing user communication capacity under limited energy constraint and avoiding malicious jamming from jammers. Due to the dynamic and time-varying nature of communication environments, we propose an intelligent resource optimization algorithm based on game theory guided reinforcement learning. To be specific, we employ a hierarchical learning algorithm based on the Stackelberg game to help users in the follower layer cooperatively select channels to against co-channel interference and jamming, and develop a deep reinforcement learning-based algorithm for dynamic power control to maintain communication efficiency. Simulation results demonstrate that our proposed approach can significantly improve the user communication rate and achieves faster convergence compared with existing algorithms.

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. Computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts’ information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare.

Recently, the advancement of information technologies have accelerated the generation of big data, necessitating substantial computing power. This has spurred the development of Computing Power Networks (CPNs), which can overcome the limitations of computing power isolation. However, CPNs consume significant energy and produce large carbon emissions during big data processing. Therefore, an energy-efficient task scheduling scheme, coupled with the utilization of renewable energy, appears to be particularly necessary. Nevertheless, the interplay between computing power and networks, and the spatiotemporal variations in green CPNs pose a challenge to designing the task scheduling scheme. In this paper, we propose a transferable spatiotemporal task scheduling scheme with a triple selection of CPN nodes, routing paths, and forwarding time of tasks. The scheme can overcome the dynamics of green CPNs, and jointly optimize the energy consumption and carbon emissions with ensuring delay constraints and long-term load balancing. Then, we present a task scheduling algorithm based on improved nondominated sorting genetic algorithm-II (NSGA-II) to solve the problem, and numerical results demonstrate that our scheme is effective in reducing the overall energy consumption and carbon emissions of CPNs.

The mining process in blockchain networks generates substantial computing consumption, which can be very challenging for miners operated by mobile users (MUs) with limited computing resources. Cloud mining service (CMS) offers a viable solution to this challenge by allowing miners to offload computation-intensive mining tasks to cloud mining providers (CMP) with abundant resources. One key problem in such a scenario is to design an effective resource pricing mechanism for the CMP. Existing researches in this field mainly focused on the differentiated pricing mechanisms, where different MUs are required to pay different prices, which are often very complicated. In this work, we explore an efficient resource pricing mechanism, where the CMP sets a uniform price for all MUs but regulates the quality of resources for different MUs. Such a mechanism can capture the key essence of differentiated pricing and greatly reduce complexity. Based on this novel mechanism, we establish a two-stage Stackelberg game between the CMP and MUs, which consists of a resource pricing and quality control problem (for the CMP) as the first stage and a mining competition game (among all MUs) as the second stage. We derive the closed-form Nash equilibrium for the mining competition game in the second stage and propose a successive convex approximation (SCA)-based algorithm that converges to the near-optimal solution in the first stage. Simulation results show that the proposed iterative algorithm can improve system utility by 32.5% compared to other schemes.

The application of inverse synthetic aperture radar (ISAR) is investigated in orthogonal frequency-division multiplexing (OFDM) integrated sensing and communication (ISAC) systems. In contrast to velocity sensing of a point target of most ISAC works, ISAR enables rotational velocity sensing to obtain the cross-range values of different scatterers on an extended target. To utilize this characteristic, we initially derive the ISAR OFDM received signal reconstruction in the frequency domain, which demonstrates that the ISAR OFDM echo signal can be equivalent to the signal received by an array, including the decoupled radial range and cross-range parameters. According to the derived signal model, a supporting parameter estimation algorithm based on the equivalent array form is proposed to estimate the range and cross-range parameters for resolvable scatterers on the extended target. Finally, numerical results confirm the effectiveness of utilizing ISAR sensing in wideband ISAC systems.

With the enhancement of health awareness and the development of artificial intelligent technology, healthcare recommender systems (HRS) play an increasingly important role in individual health management. Meanwhile, the widespread usage of large models has significantly improved the efficiency and accuracy in the analysis and utilization of medical data. In this paper, we comprehensively summarize the basic types of recommender systems as well as the new trends in utilizing large models. Then we introduce the recommendation applications in healthcare areas from six aspects, i.e., disease risk prediction, medication recommendation, medical resource recommendation, mental health support, health life management, and health education. At last, we explore some current issues and challenges within HRS, as well as the development of potential solutions and directions in the future.

Semantic communication (SemCom) holds promise for reducing network resource consumption while achieving the communications goal. However, the computational overheads in jointly training semantic encoders and decoders—and the subsequent deployment in network devices—are overlooked. Recent advances in Generative artificial intelligence (GAI) offer a potential solution. The robust learning abilities of GAI models indicate that semantic decoders can reconstruct source messages using a limited amount of semantic information, e.g., prompts, without joint training with the semantic encoder. A notable challenge, however, is the instability introduced by GAI’s diverse generation ability. This instability, evident in outputs like text-generated images, limits the direct application of GAI in scenarios demanding accurate message recovery, such as face image transmission. To solve the above problems, this paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. Moreover, in response to security concerns, we introduce the application of covert communications aided by a friendly jammer. The system jointly optimizes the diffusion step, jamming, and transmitting power with the aid of the generative diffusion models, enabling successful and secure transmission of the source messages.

Over the past decade, the Unmanned Aerial Vehicle (UAV) swarm has emerged as a disruptive force reshaping our lives and work. Benefiting from its fast and flexible deployment capabilities, UAV swarms have been widely applied to emergency communications. In the event of damaged ground communication base stations, UAV swarms can quickly reconstruct an emer-gency communication network. However, considering the limited coverage power of a single UAV node, it underscores the need for effective coordination among swarm units as well as diligent planning of a coverage trajectory. In this paper, we propose a cooperative intelligence-based UAV swarm approach for establishing emergency communications. We model a multi-UAV base station-assisted emergency communication scenario as a team Markov game model. To achieve cooperative collaboration among multiple UAVs, we propose a Q-function mixing network based coverage trajectory planning algorithm. Our experimental results demonstrate the superior convergence speed and throughput of the proposed algorithm.

This paper proposes a hybrid Quantum Federated Learning (QFL) method, called QQFL, a revolutionary approach for Dynamic Security Assessment (DSA) optimized for modern smart grids. Built on the synergy of measurement-device-independent QKD (MDI-QKD) and Variational Quantum Circuit (VQC), QQFL uniquely addresses the challenges of centralized structures and vulnerabilities in existing ML-based DSA techniques. It enables accurate label predictions for quantum states encoded from classical DSA data while ensuring data security via QKD networks. A novel mechanism, the DNN-based MDI-QKD optimizer, ensures optimal secret key exchange. Unlike traditional methods reliant solely on classical CPUs, QQFL integrates QPUs for executing computational tasks. Given the imperative of frequent data transmission in modern rapidly changing smart grid environment, QQFL emphasizes swift online learning and dynamic deployment. Testing on the synthetic Illinois 49-machine 200-bus system affirms QQFL's superior the DSA accuracy while upholding the data privacy of smart grids. Ultimately, QQFL enhances the security, reliability, confidentiality, and robustness of sophisticated smart grids.

In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose a Dynamic Activity-Aware Health Monitoring strategy (DActAHM), as a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model, for striking a balance between optimal monitoring performance and cost efficiency while ensuring precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.

Graph neural networks (GNNs) have been widely applied in various graph analysis tasks. To provide more convenient and faster predictive services, many enterprises are choosing to deploy GNNs in cloud environments. However, given the increasing privacy concerns about GNNs models and graph data, as well as the need to quickly generate embeddings for new nodes in real-world applications, a critical issue in this emerging paradigm is to ensure the security and scalability of GNN predictions. In this paper, we propose a privacy-preserving and scalable GNN prediction scheme, named PS-GNN, to address the privacy issues in cloud environments. Specifically, PS-GNN utilizes a customized array structure to store graph data and employs secret sharing to preserve the confidentiality of both the GNN model and graph data. Besides, the scalability of PS-GNN is achieved by aggregating feature information from local node neighborhoods in parallel. Through a detailed analysis, we demonstrate the security of PS-GNN. Extensive experiments on real-world datasets demonstrate that PS-GNN outperforms existing schemes in terms of computational and communication overhead, and reaches state-of-the-art performance on large graphs.

In cross-silo federated learning (FL), due to the heterogeneous participants, free-riders can utilize information asymmetry to make profits without performing any local model training. Free-riding attack poses possibilities and opportunities for unfairness and can seriously impair the operation of the FL ecosystem. It motivates our work to explore and characterize the unique features of free-riding attack, which differ from other attacks such as poisoning attacks. In this paper, we propose an evolutionary public goods game-based incentive model (Fed-EPG), which makes the first attempt to construct the interaction model among the participants through the evolutionary public goods game. Specifically, we consider both the public good characteristics of cross-silo FL models as well as the bounded rationality and incomplete information of competitors. We first introduce asymmetric environmental feedback to represent reward and punishment strategies in evolutionary game, and then adopt a multi-segment nonlinear control method to dynamically adjust the rewards and punishments among the participants, which achieves the incentive for the participants to cooperate stably during the training process. Experimental results validate that our incentive model is effective in the mitigation of free-riding. attacks.

Committee-based blockchain consensus is a fusion of permissionless consensus and the permissioned Byzantine Fault- Tolerant (BFT) classical protocol. However, three enduring challenges remain: the formal framework for the Proof-of-Stake (PoS)-based hybrid consensus, the dynamic adjustment of committee size and the definition of consensus time-bound. To tackle these challenges, in this paper, we present a Stackelberg game-driven fair committee-based blockchain consensus protocol, dubbed SG-FCB, which combines PoS and reputation-based blockchain hybrid BFT consensus. The SG-FCB protocol lever-ages an unbiased BLS-threshold signature and a random shuffle algorithm to achieve fair leader election and committee reconfiguration seamlessly. Specifically, the variant-BFT is designed to maintain the low communication cost of$\mathcal{O}(n)$, and a Stackelberg game-based incentive mechanism is proposed to jointly maximize the individual profit of the validators and the expected consensus committee responsiveness efficiency of blockchain user. Rigorous security analysis shows that for an adversary with a stakeholding fraction less than 1/3 and sufficient reputation value, we define the time bound for consensus, and the SG-FCB protocol achieves consistency and liveness properties by reasonably setting a corruption parameter and liveness parameter within a formal framework.

Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinforcement Learning (DRL), the need for customized optimization tasks for individual users complicates developing and applying numerous DRL models, leading to substantial computation resource and energy consumption and can lead to inconsistent outcomes. To address this issue, we propose a novel approach utilizing a Mixture of Experts (MoE) framework, augmented with Large Language Models (LLMs), to analyze user objectives and constraints effectively, select specialized DRL experts, and weigh each decision from the participating experts. Specifically, we develop a gate network to oversee the expert models, allowing a collective of experts to tackle a wide array of new tasks. Furthermore, we innovatively substitute the traditional gate network with an LLM, leveraging its advanced reasoning capabilities to manage expert model selection for joint decisions. Our proposed method reduces the need to train new DRL models for each unique optimization problem, decreasing energy consumption and AI model implementation costs. The LLMenabled MoE approach is validated through a general maze navigation task and a specific network service provider utility maximization task, demonstrating its effectiveness and practical applicability in optimizing complex networking systems.

Intelligent reflecting surface (IRS) can reconfigure the channel conditions, while the passive beamforming gain is limited by the severe double path-loss effect. Fortunately, active IRS (AIRS) is emerging to tackle obstacles by simultaneously adjusting the phases and amplitudes. In this paper, we propose an AIRS-assisted unmanned aerial vehicle (UAV)-relaying scheme, where the AIRS is equipped on the UAV to reflect the signal from the ground base station (GBS) to users via non-orthogonal multiple access. We jointly adjust transmit beamforming, reflection matrix and UAV trajectory to maximize the average sum rate, which is non-convex. Thus, the problem is decomposed into three subproblems via block coordinate descent. The beamforming optimization is solved through semidefinite relaxation. Then, the reflection matrix of AIRS and UAV trajectory are jointly optimized via successive convex approximation. Finally, we design an iterative algorithm to effectively tackle the original problem. Simulation results are shown to verify the performance of the designed scheme.

The space-air-ground integrated network (SAGIN) is a crucial technology for sixth-generation (6G) wireless communication networks to achieve seamless coverage and high throughput. In this paper, we propose an unmanned aerial vehicle (UAV)-assisted SAGIN structure, where the UAV is responsible for collecting data from ground users (GUs) and transmitting it to low-earth orbit (LEO) satellites. This paper also formulates a joint energy-efficient and fair resource scheduling optimization problem under jamming attacks and limited energy constraints, where the line-of-sight (LoS) links between the UAV and GUs are susceptible to being jammed. Due to the non-convex problem and dynamic environments, a deep reinforcement learning (DRL)-based twin delayed deep deterministic policy gradient (TD3) is developed to search optimal UAV trajectory to maximize energy efficiency (EE) and fairness against jamming. Simulation results verify that the proposed intelligent resource scheduling algorithm outperforms the baseline algorithms in terms of EE and fairness index in different settings.

Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular network where multiple UAVs are deployed as aerial base stations to provide communication services for mobile ground users in the presence of a malicious jammer. We propose a two-step learning-based resource scheduling approach. First, an algorithm based on K-means clustering is proposed to partition ground users (GUs) to reduce mutual interference. Moreover, a cooperative multi-agent twin delayed deep deterministic algorithm is proposed to jointly optimize UAVs' trajectories, power allocation and GU association to maximize the system energy efficiency (EE) while guaranteeing minimum quality-of-service (QoS) requirements. Extensive results demonstrate that the proposed solution can efficiently improve EE and QoS performances under jamming attacks compared with existing popular approaches.

The rapidly evolving field of generative artificial intelligence technology has introduced innovative approaches for developing semantic communication (SemCom) frameworks, leading to the emergence of a new paradigm—generative AI-assisted SemCom (GSC). Benefiting from its strong ability to understand and generate high-quality content across various domains, this approach can effectively address the reconstruction limitations that challenge traditional SemCom systems. However, this architecture often suffers from high latency due to the complex processes involved in semantic extraction and generative semantic inference. To mitigate this issue, we propose a low-latency GSC framework, achieved by enabling the parallel execution of the transmitter’s semantic extracting and the receiver’s generating processes from a macro perspective. Furthermore, to attain more accurate and semantically aligned reconstruction, we design a temporal prompt engineering approach that utilizes reinforcement learning to sequence the temporal feature extraction steps at the transmitter. The results show that compared to the conventional GSC architecture, our designed framework can achieve a 52% reduction in residual task latency that extends beyond the fixed inference duration while only incurring an approximate 9% decrease in task score.

With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-end communication system with a multiuser Gaussian interference channel. In this context, the standard constellation is not optimal under high interference conditions. To address this issue, we propose an adaptive learning algorithm for learning and predicting dynamic interference. Note that existing DL-based autoencoders are unable to train end-to-end learning systems by deep learning without a known channel. Thus, we propose a generative adversarial network (GAN)-based training scheme to imitate the real channel. Simulation results show that compared with traditional PSK and QAM modulation schemes, our proposed adaptive learning-based auto encoder can achieve significantly lower block error rate (BLER) in presence of interference. Besides, the BLER performance of our proposed GAN-based training scheme is close to that of the optimal training scheme with known channel on different channel models.

The physical-virtual world synchronization to develop the Metaverse will require a massive transmission and exchange of data. In this paper, we introduce semantic communication for the development of virtual transportation networks in the Metaverse. Leveraging the perception capabilities of edge devices, virtual service providers (VSPs) can subscribe to their preferred edge devices to receive the semantic data of interest. However, the demands of the VSPs are highly dependent on the users that they are serving. To address the resource allocation problem amid stochastic user demand, we propose a stochastic semantic transmission scheme (SSTS) based on two-stage stochastic integer programming. Using real data captured by edge devices we deploy in Singapore, the simulation results show that SSTS can minimize the transmission cost of the VSPs while accounting for the users' demand uncertainties.

Wireless communications are susceptible to eaves-dropping, and intelligent reflecting surface (IRS) as a relay capable of reconfiguring the propagation environment to extend the range of covert communication. In this paper, we investigate the covert communication in which the ground transmitter secretly delivers information to the full-duplex receiver through a two-way IRS, avoiding detection by the warden. Furthermore, the error detection probability is determined with an optimal threshold at a warden, which is the worst case for covert transmission. We aim to maximize expected error detection probability of warden subject to the covertness constraint. To this end, we alternately optimize the prior probability and the transmit power of artificial noise while satisfying the outage probability and covertness requirement. Numerical results demonstrate the effectiveness of the proposed scheme for covert communications via the two-way IRS.

Metaverse is an immersive, seamless, interactive, comprehensive virtual world, as well as a replication, extension, and transcendence of the real world. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is becoming a key technology for ubiquitous Metaverse services. To enhance network resource utilization, we introduce dual connectivity (DC) technologies in UAV-enabled MEC, which increases the time complexity associated with resource management. Considering the specific features of DC communication channels, we propose a UAV-assisted Metaverse rendering problem to enhance the Metaverse service experience and reduce the energy cost of edge devices. To solve the rendering problem with low complexity, we propose a diffusion model-based Metaverse rendering algorithm, where a novel diffusion model is used to generate integer rendering decisions with the aid of the gradient provided by the model-based Metaverse rendering problem. Moreover, with the given rendering decisions, the communication and computation resource allocation results are derived by the model-based optimization method. Finally, we conduct extensive simulation experiments based on real-world datasets. Comprehensive simulation results demonstrate that the diffusion model-based Metaverse rendering algorithm can reduce the Metaverse frame rendering time and improve user experience.

The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature inherent in existing end-to-end training manner for the existing SemCom framework, as well as deterioration of the user experience caused by the inevitable error floor in deep learning-based SemCom. In this paper, we focus on the widespread remote monitoring scenario, and propose a semantic change driven generative SemCom framework. Therein, the semantic encoder and semantic decoder can be optimized independently. Specifically, we develop a modular semantic encoder with value of information based semantic sampling function. In addition, we propose a conditional denoising diffusion probabilistic mode-assisted semantic decoder that relies on received semantic information from the source, namely, the semantic map, and the local static scene information to remotely regenerate scenes. Moreover, we demonstrate the effectiveness of the proposed semantic encoder and decoder as well as the considerable potential in reducing energy consumption through simulation based on the realistic F composite channel fading model. The code is available at https://github.com/wty2011jl/SCDGSC.git.

Generative Artificial Intelligence (GAI) is taking the world by storm with its unparalleled content creation ability. Large Language Models (LLMs) are at the forefront of this movement. However, the significant resource demands of LLMs often require cloud hosting, which raises issues regarding privacy, latency, and usage limitations. Although edge intelligence has long been utilized to solve these challenges by enabling real-time AI computation on ubiquitous edge resources close to data sources, most research has focused on traditional AI models and has left a gap in addressing the unique characteristics of LLM inference, such as considerable model size, auto-regressive processes, and self-attention mechanisms. In this paper, we present an edge intelligence optimization problem tailored for LLM inference. Specifically, with the deployment of the batching technique and model quantization on resource-limited edge devices, we formulate an inference model for transformer decoder-based LLMs. Furthermore, our approach aims to maximize the inference throughput via batch scheduling and joint allocation of communication and computation resources, while also considering edge resource constraints and varying user requirements of latency and accuracy. To address this NP-hard problem, we develop an optimal Depth-First Tree-Searching algorithm with online tree-Pruning (DFTSP) that operates within a feasible time complexity. Simulation results indicate that DFTSP surpasses other batching benchmarks in throughput across diverse user settings and quantization techniques, and it reduces time complexity by over 45% compared to the brute-force searching method.

Avatars, as promising digital assistants in Vehicular Metaverses, can enable drivers and passengers to immerse in 3-D virtual spaces, serving as a practical emerging example of Artificial Intelligence of Things (AIoT) in intelligent vehicular environments. The immersive experience is achieved through seamless human–avatar interaction, e.g., augmented reality navigation, which requires intensive resources that are inefficient and impractical to process on intelligent vehicles locally. Fortunately, offloading avatar tasks to roadside units (RSUs) or cloud servers for remote execution can effectively reduce resource consumption. However, the high mobility of vehicles, the dynamic workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making avatar migration decisions. To address these challenges, in this article, we propose a dynamic migration framework for avatar tasks based on real-time trajectory prediction and multiagent deep reinforcement learning (MADRL). Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs. Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed-integer programming problem. To tackle this problem efficiently, the problem is transformed into a partially observable Markov decision process (POMDP) and solved by multiple DRL agents with hybrid continuous and discrete actions in decentralized. Numerical results demonstrate that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction and 30% with prediction and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse (AeVeM).

The blockchain can offer a dependable and secure platform for Internet of Things (IoT) transactions with its distributed and secure network architecture. Unfortunately, it faces challenges, such as limited throughput, excessive computational costs, and high-transaction fees. Off-chain scaling protocols are used to address the scalability of blockchain for their outstanding performance and efficiency. To mitigate the high-cost interactions with blockchain, previous studies only considered moving transactions of payment hubs (PHs) off-chain, utilizing off-chain operators to aggregate multiple transactions. However, existing PHs overly rely on central operators for system maintenance, greatly increasing the risk of central operator failure (COF). Previous solutions allowed operators to submit unsettled state commitments (USCs) to the blockchain and overlooked the pessimistic scenario that could lead to state rollbacks. To address these issues, this article proposes an efficient multiparty payment protocol (HyperPay), aimed at utilizing the off-chain scaling technique to enhance transaction throughput and reduce on-chain cost. Specifically, we first propose a novel off-chain committee and collateral-based verifiable random leader election (C-VRE) to elect leaders fairly, thus mitigating the COF problem. Additionally, we design a new state validation mechanism and one-step fraud challenge (OSFC), enabling verifiers to directly construct fraud proofs and challenges on-chain, thereby preventing leaders from submitting USC. Our evaluation indicates that HyperPay reduces on-chain costs of challenge by 80% and boosts peak throughput by a factor of 10X-283X. A comprehensive theoretical analysis and experimental results substantiate the security and effectiveness of our proposed approach.

With the booming of mobile edge computing (MEC) and blockchain-based blockchain-based federated learning (BCFL), more studies suggest deploying BCFL on edge servers. In this case, edge servers with restricted resources face the dilemma of serving both mobile devices for their offloading tasks and the BCFL system for model training and blockchain consensus without sacrificing the service quality to any side. To address this challenge, this article proposes a resource allocation scheme for edge servers to provide optimal services at the minimum cost. Specifically, we first analyze the energy consumption of the MEC and BCFL tasks, considering the completion time of each task as the service quality constraint. Then, we model the resource allocation challenge into a multivariate, multiconstraint, and convex optimization problem. While solving the problem in a progressive manner, we design two algorithms based on the alternating direction method of multipliers (ADMMs) in both homogeneous and heterogeneous situations, where equal and on-demand resource distribution strategies are, respectively, adopted. The validity of our proposed algorithms is proved via rigorous theoretical analysis. Moreover, the convergence and efficiency of our proposed resource allocation schemes are evaluated through extensive experiments.

Stress tracing is an important research domain that supports many applications, such as health care and stress management; and its closest related works are derived from stress detection. However, these existing works cannot well address two important challenges facing stress detection. First, most of these studies involve asking the users to wear physiological sensors to detect their stress states, which has a negative impact on the user experience. Second, these studies have failed to effectively utilize the multimodal physiological signals, which results in less satisfactory detection results. This article formally defines the stress tracing problem, which emphasizes the continuous detection of human stress states. A novel deep stress tracing (DST) method, named DST, is presented. Note that, DST proposes tracing human stress based on the physiological signals collected by a noncontact ultrawideband radar, which is more friendly to users when collecting their physiological signals. In DST, a signal extraction module is carefully designed at first to robustly extract the multimodal physiological signals from the raw RF data of the radar, even in the presence of body movement. Afterward, a multimodal fusion module is proposed in DST to ensure that the extracted multimodal physiological signals can be effectively fused and utilized. Extensive experiments are conducted on the three real-world data sets, including one self-collected data set and two publicity data sets. Experimental results show that the proposed DST method significantly outperforms all the baselines in terms of tracing human stress states. On average, DST averagely provides a 6.31% increase in detection accuracy on all the data sets, compared with the best baselines.

In this article, based on martingale theory, we investigate the problem of maximum throughput in hybrid energy harvesting wireless communication systems (EH-WCSs) under energy storage and delay (or backlog) constraints. Specifically, the energy supply and data transmission of the hybrid EH-WCS are modeled as two queuing systems. For the first energy supply queueing system, we construct corresponding martingales for each type of energy harvesting (EH) process and the system’s energy consumption process. Leveraging the multiplicativity of martingales, the stochastic characteristics of the hybrid EH process are described in the martingale domain. On this foundation, a closed-form expression for the energy depletion probability bound (EDPB) under various energy storage constraints is derived. In the second data transmission queueing system, to capture the impact of channel fading on the system’s service, we map the arrival and service processes to the signal-to-noise ratio (SNR) domain and construct the corresponding martingales. A martingale parameter is proposed that connects the martingales of the arrival and service processes with the system’s EDPB. Based on this, the closed-form expressions for the delay violation probability bound and backlog violation probability bound are derived. Utilizing these derived performance bounds, we address the maximum throughput optimization problems under the energy storage and delay (or backlog) constraints. Furthermore, we instantiate a scenario and provide guidance on the impact of resource allocation on maximum throughput through simulation and validation, offering insights for achieving green communication networks.

Owing to its practical configuration to edge computing and privacy preservation capabilities, federated learning (FL) has been increasingly appealing in Internet of Things (IoT) networks. However, due to the inherent openness of IoT network architectures, FL clients are susceptible to various attacks, resulting in unreliable local model updates. To address this challenge, federated unlearning (FU) emerges as a viable solution, which can erase such unreliable updates from the FL model using the unlearning operation while preserving model accuracy. Existing FU studies have significant potential, but they are not directly applicable to IoT networks because of their high computational costs and limited capacity to defend against prevalent dynamic attacks in mobile network environments. In this work, we propose FedRemover, a novel FU method specifically tailored for deployment in IoT networks. The key insight behind FedRemover is that model updates will exhibit inconsistency when exposed to attacks. Therefore, we devise a real-time malicious client detection scheme by examining the performance consistency of model updates. Upon detecting malicious clients, FedRemover promptly executes the unlearning operation, achieving an unlearned global model within a minimal number of rounds. This makes FedRemover highly efficient and robust against dynamic attacks, enabling it well-suited for practical deployment in IoT networks. Experiments on three standard datasets demonstrate the efficiency and robustness of FedRemover, with an obvious speed-up of 10× and comparable robustness guarantees compared with benchmark algorithms.

As a fundamental component of Internet of Things (IoT) devices, firmware plays an essential role. Nowadays, the development of IoT firmware relies extensively on third-party components and substantially enhances development efficiency. However, these components are not inherently secure, and their vulnerabilities can adversely affect the security of IoT firmware. Existing research adopts binary code similarity analysis to detect known vulnerabilities in firmware. However, it encounters significant challenges, primarily in extracting function features from the limited semantic information within binary code. Another challenge is the need for real-world datasets to assess the model’s performance in practical scenarios, such as firmware supply chain analysis. We present a detection model named PDG2VEC based on Program Dependence Graphs (PDGs) to tackle these challenges. PDG2VEC extracts function features at the variable level on PDG and assesses function similarity by evaluating whether two functions can represent each other. We conducted evaluations using three datasets, including one we created to simulate a firmware supply chain scenario. The experimental results demonstrate that PDG2VEC exhibits resilience to cross-architecture challenges and captures more precise semantics than other approaches. Furthermore, PDG2VEC outperforms state-of-the-art tools in the supply chain analysis scenario, with a 16% higher AUC value average against baseline approaches.

Vehicle trajectory prediction (VTP) is important for ensuring safe decision-making and planning in Internet of Vehicles (IoV). In complex traffic scenarios, accurate and reliable trajectory prediction requires comprehensive understanding of the interaction behaviors among vehicles. However, existing methods fail to effectively capture vehicle interaction features and fully explore their potential dependencies, limiting improvements in prediction accuracy. To this end, we propose a social attention network fused multipatch temporal–variable dependency (SAN-FTVD) model to tackle the above problems. In specific, we first design a variable token embedding module (VTEM) to extract the motion state information of vehicles, which independently embeds each variable of vehicle historical data into a variable token. After that, we propose a physical informed vehicle interaction encoder (PI-VIE) to capture vehicle interaction features over continuous time. The encoder is combined with physical priors to encode vehicle interaction features based on the correlations between the variable tokens. Following that, a temporal–variable dependency fusion module (TVDFM) is proposed to extract and fuse the multipatch temporal and variable dependencies, fully exploring potential dependencies in vehicle interaction features. Numerical results demonstrate that compared with the state-of-the-art model, the proposed model reduces the average prediction root mean square error over 5-s time range by 8% and 7% on two public data sets with 75% less inference cost. Furthermore, extensive ablation experiments validate the effectiveness of the above modules in the model.

The Internet of Things (IoT) ecosystem is undergoing a significant evolution through its integration with satellite networks, empowering remote and computation-intensive IoT tasks to leverage computing services via satellite links. Current research in this field predominantly focuses on minimizing latency and energy consumption in computation offloading, yet overlooks the substantial costs incurred by satellite resource utilization. To address this oversight, we introduce a cost-effective hybrid computation offloading (CE-HCO) paradigm in satellite-terrestrial integrated networks (STINs) in this article. First, we propose the 5G-based system framework facilitates gNB and user plane function functionalities on satellites and fosters collaboration between public cloud providers and satellite operators. The framework is in line with the latest 3GPP activities and business models in satellite computing. Then, we formulate the CE-HCO problem, aiming to minimize total computation offloading costs while satisfying diverse user latency requirements and adhering to satellite energy constraints. To tackle this NP-hard problem, we develop an algorithm employing the penalty method and successive convex approximation to simplify the complex mixed-integer nonlinear programming into tractable convex iterations. Simulation results show that our approach outperforms existing baselines in balancing performance and cost, and offer guidance on pricing policies for satellite computing services to promote future commercial growth.

Task scheduling in large-scale industrial Internet of Things (IIoT) is characterized by the presence of diverse resources and the requirement for efficient and synchronized processing across distributed edge clouds, raising a significant challenge. This paper proposes a task scheduling framework across edge clouds, namely LsiA3CS, which employs deep reinforcement learning (DRL) and heuristic guidance to achieve distributed, asynchronous task scheduling for large-scale IIoT. Specifically, the Markov game-based model and the asynchronous advantage actor-critic (A3C) algorithm are leveraged to orchestrate diverse computational resources, effectively balancing workloads and reducing communication latency. Moreover, the incorporation of heuristic policy annealing and action masking techniques further refines the adaptability of the proposed framework to the unpredictable requirements of large-scale IIoT systems. Real-world task datasets are utilized to conduct extensive experimental evaluations on a simulated large-scale multi-edge cloud IIoT. The results shows that LsiA3CS significantly reduces task completion times and energy consumption while managing unpredictable task arrivals and variable resource capacities.

The emerging space-terrestrial integrated network (STIN) assumes a pivotal role within the 6G vision, promising to deliver seamless global coverage and connectivity. Achieving advanced, high-reliability, and time-sensitive (TS) services in a resource-constrained and failure-prone space environment is critical, but also presents challenges. Existing space-terrestrial communication approaches either suffer from temporary link failures with unstable reliability, or intolerable service latency due to the extensive coverage and uneven traffic distribution. This paper presents FastTS, a heuristic resilient and performant scheduling strategy to achieve fault-tolerant and time-sensitive scheduling in futuristic STINs. First, we model the high-dynamic and failure-prone topology in space, and formulate the scheduling problem as a mixed non-linear problem with the objective of minimizing the average task completion time. To approach the optimal solution, joint time-variant routing and frame replication and elimination for reliability (FRER) redundancy under resource constraints are formally considered in our design. During the path-stable duration, FastTS prioritizes the multipath selection with higher redundancy scores, all while ensuring a bounded low latency for TS services based on time-sensitive networking (TSN) techniques. Specifically, our FastTS is divided into three phases: time-sensitive multipath generation (TMG), series-parallel redundancy scoring (SPRS), and SPRS-based time-variant routing (STR). Finally, simulation results show that FastTS exhibits outstanding performance improvements in terms of packet delay, scheduling success ratio, task completion time and packet loss rate, when compared to other state-of-the-art methods.

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

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

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

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregate them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated.

Web 3.0 pursues the establishment of decentralized ecosystems through blockchain technologies, driving digital transformation in commerce and governance. With consensus algorithms and smart contracts grounded in cryptographic technologies, Web 3.0 enables secure and transparent digital services, such as digital identity, asset management, decentralized autonomous organizations (DAOs), and decentralized finance (DeFi), fostering integration between digital and physical economies. As quantum devices rapidly advance, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with enhanced quantum computing and communication capabilities. This article provides a comprehensive overview of blockchain-based Web 3.0, examining its quantum and postquantum advancements from two key perspectives. On the one hand, postquantum migration methods and quantum-resistant signatures offer robust solutions to safeguard blockchain against quantum threats. On the other hand, quantum and postquantum encryption and verification algorithms boost blockchain performance, creating a decentralized, secure, and value-driven system. Additionally, we outline potential applications of quantum blockchain and offer guidance for implementation within the Web 3.0 ecosystem. Finally, we discuss future directions for developing a provably secure and decentralized digital ecosystem.

Federated Learning(FL) has attracted wide research interest due to its potential in building machine learning models while preserving users' data privacy. However, due to the distributive nature of FL, it is vulnerable to misbehavior from participating worker nodes. Thus, it is important to select clients to participate in FL. Recent studies on FL client selection focus on the perspective of improving model training efficiency and performance, without holistically considering potential misbehavior and the cost of hiring. To bridge this gap, we propose a first-of-its-kind reputation-awareStochastic integer programming-based FLClientSelection method (SCS). It can optimally select and compensate clients with different reputation profiles. Extensive experiments show that SCS achieves the most advantageous performance-cost trade-off compared to other existing state-of-the-art approaches.

Due to the scarcity of spectrum resources in Non-orthogonal Multiple Access (NOMA) systems and insufficient satellite-ground integration in satellite Internet of Things (IoT), this paper investigates its issue in spectrum resource management. We propose a resource allocation method based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for NOMA enabled satellite IoT. We formulate the spectrum allocation problem of the satellite-ground integrated network as a distributed optimization problem. Then we decouple the problem into two sub-problems. Firstly, a user grouping method based on matching coefficients is defined, and a Linear Programming (LP) method is utilized for obtaining solution. Secondly, the power allocation problem is transformed into a multi-agent problem, where MADDPG is employed to allocate the power. Through this approach, the system is capable of real-time user association and spectrum resource allocation optimization, achieving optimal user grouping while maximizing system transmission rate. Based on the simulation results, the MADDPG-based method demonstrates fast convergence within 100 training iterations. The proposed MADDPG-based resource management method also achieves increased system transmission rate with more effective matching outcomes over Deep Deterministic Policy Gradient (DDPG), Orthogonal Multiple Access (OMA), and random allocation baselines.

In recent years, jointly optimizing unmanned aerial vehicle (UAV) hover point selection and resource management for UAV-assisted mobile edge computing (MEC) is a hot research topic. Unlike previous studies, this paper investigates the optimization problem of hover point selection and resource management under dynamic jamming attacks, where the objective is to maximize overall communication and computing efficiency while taking into account constraints on total UAV power and the availability of channels. Due to the non-convex problem and highly dynamic environments, we then propose an advanced deep reinforcement learning (DRL) algorithm to jointly optimize UAV hover point selection, task collection time ratio, transmission power, channel selection, and task offloading ratio to improve the efficiency of UAV-assisted MEC. Specifically, the algorithm optimizes UAV hover point selection to minimize the negative effect of jamming attacks, and then manages resources to improve UAV task processing capacity and reduce energy consumption while mitigating jamming. Simulation results demonstrate that our proposed learning-based algorithm significantly enhances the computing and offloading efficiency in complex and dynamic UAV-assisted MEC environments against jamming compared to other existing algorithms.

Due to the open accessibility of wireless networks with severe privacy risks, covert communication has gained increasing attention, where the effective range is limited by the low transmit power. Fortunately, employing intelligent reflecting surface (IRS) as a relay has become an appealing solution to extend the range of covert communication. To this end, we investigate the covert communication in which the ground transmitter secretly delivers information to the full-duplex receiver through a two-way IRS, avoiding detection by the warden. Then, the error detection probability is determined with an optimal threshold at a warden, which is the worst case for covert transmission. Moreover, we analyze the closed-form expression of outage probability. To improve the covertness, artificial noise is generated by the receiver to interfere with the adversarial monitoring. Thus, considering the optimal prior probability, we maximize the expected error detection probability of warden subject to the covertness constraint. Specifically, we alternately optimize the prior probability and the transmit power of artificial noise while satisfying the outage probability and covertness requirement. Numerical results demonstrate the effectiveness of the proposed scheme for covert communications via the two-way IRS.

In this paper, we investigate the mmWave beam tracking for vehicle-to-infrastructure (V2I) communications to find the optimal beam via sub-6 GHz channel state information (CSI). We consider two scenarios: 1) sub-6 GHz and mmWave transceivers are co-located on the same base station (BS), and 2) sub-6 GHz and mmWave BSs are separated in different places constituting heterogeneous networks (HetNets) where one sub-6 GHz BS controls multiple mmWave BSs. Considering the mobility of the vehicle and time-varying channels, we propose a predictive beam tracking method based on long short-term memory (LSTM) to construct the maps from historical sequential sub-6 GHz CSI to the future optimal mmWave beam. A single LSTM model can handle the beam tracking in the co-located scenario, since there is a one-to-one correspondence between the sub-6 GHz and mmWave transceivers, and the propagation of sub-6 GHz and mmWave signals is similar. However, in the HetNet scenario, it is difficult to select the best one among the beams of multiple mmWave BSs only via the CSI of one sub-6 GHz BS. To address this challenge, we design an LSTM fusion model, which exploits not only the historical sequential sub-6 GHz CSI but also a number of mmWave wide beam measurements, to obtain the optimal mmWave BS and beam in the HetNet. In this case, the collected sub-6 GHz CSI and mmWave wide beam measurements are analyzed by the LSTM and fully connected network (FCN) modules, respectively, providing two beam prediction results. Then the results are fused by an attention-based FCN module to accomplish the final prediction. Simulation results verify the effectiveness and superiority of our LSTM-based beam tracking models compared with other state-of-the-art deep learning beam tracking models that also leverage sub-6 GHz channels. Besides, the robustness and generalization of our proposed LSTM models are illustrated through simulations.

The location information of cars contains great value, but the uncontrollable characteristics of public data and the difficulty in distributing benefits derived from the potential value of the data greatly reduces the enthusiasm for data owners to share their data. In addition, the current selective disclosure schemes based on merkle tree still require large costs when there are many data items. To solve these problems, a blockchain-based framework for sharing cars’ location information applicable to the online car hailing industry is proposed in this paper, enabling the sharing of cars’ location information while protecting passengers’ privacy through selective disclosure. The combination of homomorphic encryption and probabilistic verification enables a faster batch data verification compared to other blockchain-based data sharing schemes, as well as ensures the authenticity of the data uploaded to the blockchain. The experimental results show that the proposed selective disclosure mechanism based on hash exclusive or tree has lower costs than the baseline for cases with many data items. Moreover, the proposed framework meets both security and feasibility requirements. Specifically speaking, under the constraint of 128-bits security level, the costs of time and space on the location information during one drive are at microsecond level and kilobyte level, respectively. Finally, the scheme is suitable for scenarios with higher throughput.

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

Mobile AI-Generated Content (AIGC) has achieved great attention in unleashing the power of generative AI and scaling the AIGC services. By employing numerous Mobile AIGC Service Providers (MASPs), ubiquitous and low-latency AIGC services for clients can be realized. Nonetheless, the interactions between clients and MASPs in public mobile networks, pertaining to three key mechanisms, namely MASP selection, payment scheme, and fee-ownership transfer, are unprotected. In this paper, we design the above mechanisms in a systematic approach and present the first blockchain to protect mobile AIGC, called ProSecutor. Specifically, by roll-up and layer-2 channels, ProSecutor forms a two-layer architecture, realizing tamper-proof data recording and atomic fee-ownership transfer with high resource efficiency. Then, we present the Objective-Subjective Service Assessment(OS2)framework, which effectively evaluates the AIGC services by fusing the objective service quality with the reputation-based subjective experience of the service outcome (i.e., AIGC outputs). DeployingOS2on ProSecutor, firstly, the MASP selection can be realized by sorting the reputation. Afterward, the contract theory is adopted to optimize the payment scheme and help clients avoid moral hazards in mobile networks. We implement the prototype of ProSecutor on BlockEmulator. Extensive experiments demonstrate that ProSecutor achieves 12.5× throughput and saves 67.5% storage resources compared with BlockEmulator. Moreover, the effectiveness and efficiency of the proposed mechanisms are validated.

Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services become accessible for resource-constrained end users. However, this advancement, referred to as mobile AIGC, also introduces a significant challenge: users should download large AIGC outputs from the MASPs, leading to substantial bandwidth consumption and potential transmission failures. In this paper, we apply cross-modalGenerativeSemanticCommunications (G-SemCom) in mobile AIGC to overcome wireless bandwidth constraints. Specifically, we utilize cross-modal attention maps to indicate the correlation between user prompts and each part of AIGC outputs. In this way, the MASP can analyze the prompt context and filter the most semantically important content efficiently. Only semantic information is transmitted, with which users can recover the entire AIGC output with high quality while saving mobile bandwidth. Since the transmitted information not only preserves the semantics but also prompts the recovery, we formulate a joint semantic encoding and prompt engineering problem to optimize the bandwidth allocation among users. Particularly, we present a human-perceptual metric named Joint Perceptual Similarity and Quality (JPSQ), which is fused by two learning-based measurements regarding semantic similarity and aesthetic quality, respectively. Furthermore, we develop the Attention-aware Deep Diffusion (ADD) algorithm, which learns attention maps and leverages the diffusion process to enhance the environment exploration ability of traditional deep reinforcement learning (DRL). Extensive experiments demonstrate that our proposal can reduce the bandwidth consumption of mobile users by 49.4% on average, with almost no perceptual difference in AIGC output quality. Moreover, the ADD algorithm shows superior performance over baseline DRL methods, with 1.74× higher overall reward.

To enable ubiquitous Artificial Intelligence (AI) in the next-generation wireless communications networks, computation-intensive tasks such as data processing and model training have to be performed by energy-constrained end users. In this paper, we present a hybrid coded edge computing network whereby users can choose to complete their computation task through: i) local computation with the wireless power transfer derived from base stations, ii) coded edge offloading, or iii) hybrid computation involving edge offloading and local computation. To minimize the overall network cost, we propose a stochastic resource optimization approach. Given the stochastic nature of wireless charging efficiency and edge servers computation capacities, which can only be observedex-post, a computation strategy for each user is determined using the two-stage stochastic integer programming (SIP). To address the complexity of the SIP problem which scales with the size of the network, we introduce the efficient computation methods of Benders’ decomposition and sample average approximation. Besides, we present a special case of$z$-stage stochastic offloading optimization that is applicable when the corrective edge offloading action can be executed in multiple stages, e.g., for non-time-sensitive tasks that do not need to be completed by stage two. Finally, we provide extensive sensitivity analyses to evaluate the performance of the proposed cost minimization approach amid varying network parameters. We demonstrate that our approach outperforms deterministic optimization approaches for in-network cost minimization.

With the recent development of the Metaverse, people are more connected with each other. Avatars are used to represent the people, to communicate with one another, and they can build the community virtually. In these processes, a massive amount of data is exchanged between the physical and the virtual world. However, the existing communication technologies are insufficient to support the Metaverse, and the energy consumption of the Metaverse is huge. Therefore, semantic communication is one of the emerging communication paradigms to reduce the size of the data transmitted and reduce energy consumption while maintaining its meaning. Virtual service providers (VSPs) who provide services in the Metaverse can purchase semantic data from the nearby edge sensing units by using two subscription plans: reservation and on-demand. However, in practice, the demand of the VSPs is uncertain due to the variability of the Metaverse. To minimize the cost of the network and prevent over- and under-subscription of the resources, we propose a two-phase stochastic semantic resource allocation (SSRA) scheme. In phase one, a double dutch auction performs a one-to-one matching between VSPs and edge sensing units. The matching is dynamic and depends on the quality of experience (QoE) from the Metaverse users and the semantic data transmission cost from the edge sensing units. The matching changes whenever QoE and the semantic data transmission cost vary. In phase two, we consider the demand uncertainty and matching result from the phase one to formulate a distributed robust optimization (DRO) problem to minimize the operation cost of the VSPs. Using a real-world dataset, simulation results demonstrate that our proposed scheme is fully dynamic and minimizes the operation cost/energy consumption of VSPs in the presence of stochastic uncertainties.

In mobile edge computing (MEC) systems, multiple unmanned aerial vehicles (UAVs) can be utilized as aerial servers to provide computing, communication, and storage services for edge users, called UAV-assisted MEC, which has emerged as a promising technology to improve both the computing and communication performances. Unlike existing works without considering jamming attacks, we investigate a multi-UAV-assisted-MEC scenario under multiple malicious jammers and then propose a resource management approach with the objective of minimizing both the system energy consumption and latency. Due to the time-varying nature of communication environments, we design a multi-agent deep reinforcement learning (MADRL)-based resource management approach to dynamically adjust the CPU frequency, communication bandwidth, and channel access selection of UAVs to enhance the system performance against jamming attacks. On this basis, in order to enhance the algorithm learning efficiency, we propose a multi-agent twin-delayed deep deterministic policy algorithm in combination with the prioritized experience replay mechanism (PER-MATD3) to effectively search for the joint resource management strategy under high-dimensional state and action spaces, where the time-varying channel state information and imperfect attack behavior information are also effectively trained to improve the learning capacity and convergence speed. Simulation and experimental results verify that the proposed approach can significantly decrease the overall system latency (i.e., computing and communication latency) and energy consumption compared to other benchmark algorithms under different real-world settings.

With the significant advancements in artificial intelligence (AI) technologies and computational capabilities, generative AI (GAI) has become a pivotal digital content generation technique for offering superior digital services. However, due to the inherent instability of AI models, directing GAI towards the desired output remains a challenging task. Therefore, in this paper, we design a novel framework that utilizeswirelessperception to guideGAI(WiPe-GAI) in delivering AI-generated content (AIGC) service, within resource-constrained mobile edge networks. Specifically, we first propose a new sequential multi-scale perception (SMSP) algorithm to predict user skeleton based on the channel state information (CSI) extracted from wireless signals. This prediction then guides GAI to provide users with AIGC, i.e., virtual character generation. To ensure the efficient operation of the proposed framework in resource constrained networks, we further design a pricing-based incentive mechanism and propose a diffusion model based approach to generate an optimal pricing strategy for the service provisioning. The strategy maximizes the user's utility while incentivizing the participation of the virtual service provider (VSP) in AIGC provision. The experimental results demonstrate the effectiveness of the designed framework in terms of skeleton prediction and optimal pricing strategy generation, outperforming other existing solutions.

Unmanned aerial vehicle (UAV) swarms have found extensive applications owing to their flexibility, mobility, cost-effectiveness, and capacity for collaborative and autonomous service delivery. Empowered by intelligent algorithms, UAV swarm can exhibit cohesive behaviors and autonomously coordinate to achieve collective objectives. Nonetheless, in real-world scenarios with uncertainty and stochasticity, its performance suffers from the unstable information exchange among UAVs and inefficient data sampling. In this paper, we introduce a metaverse-based UAV swarm system, where monitoring, observation, analysis, and simulation can be realized collaboratively and virtually. Within the metaverse, virtual service providers (VSPs) utilize digital twin (DT) to generate and render virtual sub-worlds, while providing diverse virtual services. In particular, the VSP trains the learning model using high-fidelity data from the physical world, formulates optimal decisions for diverse tasks, and returns these decisions to the UAV swarm for the execution of the corresponding tasks. Since synchronization between two worlds needs frequent data exchange, we employ the semantic communication technique in our system which could reduce communication latency by transmitting only the semantic information. In such design, UAVs as workers are employed to collect data and provide extracted semantic information to the VSPs. Moreover, we propose a hierarchical framework to investigate the reliability and sustainability of the metaverse-based UAV swarm system. In the lower layer, we design a worker selection scheme to determine reliable UAVs for data synchronization. In the upper layer, we consider deep learning (DL)-based auction as the incentive mechanism for resource allocation in semantic information trading between UAV swarm and VSPs.

Vehicular ad hoc networks (VANETs) establish wireless connections among all vehicles, enabling seamless mobile communication. However, existing conditional privacy protection VANETs authentication schemes fail to address the issue of potential key-exposure and do not provide accelerated vehicle authentication. In this paper, we propose a reputation incentive committee-based secure conditional dual authentication scheme for VANETs called RIC-SDA. Our proposed scheme incorporates dual authentication of the consensus committee and vehicle-to-vehicle (V2V) communication. It enables the rapid provision of dynamic vehicle epoch-key from consensus committee authentication for V2V authentication through our designed reputation incentive mechanism. To mitigate the potential key-exposure problem, we introduce a novel concept of secure vehicle epoch communication, which means V2V authentication is valid for only one epoch blockchain unit time. The proposed scheme achieves lightweight computation and incurs minimal communication overheads, with the signature size being just 137 bytes. The RIC-SDA scheme supports fast batch verification. We prove that our proposed scheme is unforgeable security under random oracle and demonstrate its feasibility by implementing it in a test network based on Ethereum Sepolia. The results demonstrate that our RIC-SDA solution outperforms the existing state-of-the-art authentication VANET schemes regarding efficiency and communication costs.

By utilizing intelligent mobile terminals, mobile crowdsensing (MCS) can realize the sensing data collection effectively and economically. However, the privacy security and freshness quality of the obtained sensing data are two major concerns to be addressed in MCS, as they directly impact the system security and timeliness performance. In this regard, we focus on improving the data freshness performance and protecting sensing data content, sensing terminals' identification, and location information simultaneously. Accordingly, based on the artificial noise (AN)-based differential privacy and covert communication technologies, we aim to jointly minimize the Age of Information (AoI) metric and weighted privacy preservation budget in the single terminal scenario. Besides, we achieve the goal of average AoI optimization with data computing requirements in multiple terminal systems, where the privacy preservation budget is treated as the critical constraint. Furthermore, by using the backward induction (BI) method and block successive upper-bound minimization (BSUM) approach, we solve the above two optimization problems, respectively. Finally, compared with the listed baselines, the results evaluate the proposed schemes' effectiveness under various simulation settings.

Recently, MEC has been integrated with satellite networks to process remote terrestrial computation tasks with superior coverage and delay. Since single satellite computation is hard to tackle spatially uneven computation workloads, computation peer offloading among multiple satellites is urgently needed to further improve service quality and resource utilization. However, considering limited resources, deficient energy, and costly overheads of communication and computation, how to enable efficient offloading cooperation in the time-varying satellite networks is a significant challenge. In this paper, we first design a satellite peer offloading scheme, where offloading is performed along multi-hop paths to explore collaborative computing capabilities. Second, we formulate the Multi-Hop Satellite Peer offloading (MHSPO) problem, aiming to jointly minimize the delay and energy consumption under system resources and backlog constraints. Then, to adapt to the network dynamics, the decision-making process with uncertain future workloads is optimized by leveraging the delayed online learning method under the Lyapunov framework. Finally, we develop a practical online distributed algorithm to solve the MHSPO problem, which is proven to achieve close-to-optimal performance. Extensive simulations show that multi-hop peer offloading among satellites improves edge computing performance efficiently.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has largely extended the border and capacity of artificial intelligence of things (AIoT) by providing a key element for enabling flexible distributed data inputs, computing capacity, and high mobility. To enhance data privacy for AIoT applications, federated learning (FL) is becoming a potential solution to perform training tasks locally on distributed IoT devices. However, with the limited onboard resources and battery capacity of each UAV node, optimization is required to achieve a large-scale and high-precision FL scheme. In this work, an optimized multi-UAV-assisted FL framework is designed, where regular IoT devices are in charge of performing training tasks, and multiple UAVs are leveraged to execute local and global aggregation tasks. An online resource allocation (ORA) algorithm is proposed to minimize the training latency by jointly deciding the selection decisions of clients and a global aggregation server. By leveraging the Lyapunov optimization technique, virtual energy queues are studied to depict the energy deficit. With the help of the actor-critic learning framework, a deep reinforcement learning (DRL) scheme is designed to improve per-round training performance. A deep neural network (DNN)-based actor module is designed to derive client selection decisions, and a critic module is proposed through a conventional optimization method to evaluate the obtained selection decisions. Moreover, a greedy scheme is developed to find the optimal global aggregation server. Finally, extensive simulation results demonstrate that the proposed ORA algorithm can achieve optimal training latency and energy consumption under various system settings.

The Metaverse, envisioned as the next-generation Internet, will be constructed via twining a practical world in a virtual form, wherein Meterverse service providers (MSPs) are required to collect massive data from Meterverse users (MUs). In this regard, a critical demand exists for MSPs to motivate MUs to contribute computing resources and data while preserving user privacy. Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, can support distributed intensive computation in the Metaverse. In this work, we first investigate minting the machine learning models into NFT with FL assistance (referred to as FL-NFT), such that MUs as stakeholders can control the ownership and share the economic value of user-generated content (UGC). Specifically, MUs are encouraged to establish a decentralized autonomous organization (i.e., MU-DAO) to aggregate local models and mint FL-NFT. MUs and MSPs optimize the strategies by formulating an imperfect information Stackelberg game to trade off the cost and benefit. We apply the backward induction to derive the equilibrium solution. Then, we construct a privacy-preserving multi-winner sealed-bid auction mechanism (PMS-AM), in which the Hidden Markov Model assists MSPs in choosing rational bidding strategies according to historical bids, and the double auction mechanism determines the winners and price of FL-NFT. Finally, the numerical results based on theoretical analysis and simulations demonstrate that the proposed PMS-AM can increase the quality of FL-NFT and achieve the economic properties of incentive mechanisms such as individual rationality and incentive compatibility.

AI-Generated Content (AIGC) services are essential in developing the Metaverse, providing various digital content to build shared virtual environments. The services can also offer personalized content with user assistance, making the Metaverse more human-centric. However, user-assisted content creation requires significant communication resources to exchange data and construct trust among unknown Metaverse participants, which challenges the traditional centralized communication paradigm. To address the above challenge, we integrate blockchain with semantic communication to establish decentralized trust among participants, reducing communication overhead and improving trustworthiness for AIGC services in Metaverse. To solve the out-of-distribution issue in data provided by users, we utilize the invariant risk minimization method to extract invariant semantic information across multiple virtual environments. To guarantee trustworthiness of digital contents, we also design a smart contract-based verification mechanism to prevent random outcomes of AIGC services. We utilize semantic information and quality of digital contents provided by the above mechanisms as metrics to develop a Stackelberg game-based content caching mechanism, which can maximize the profits of Metaverse participants. Simulation results show that the proposed semantic extraction and caching mechanism can improve accuracy by almost 15% and utility by 30% compared to other mechanisms.

Consortium blockchains have attracted considerable interest from academia and industry due to their low-cost installation and maintenance. However, typical consortium blockchains can be easily attacked by colluding block validators because of the limited number of miners in the systems. To address this problem, in this paper, we propose a novel block validation framework to enhance blockchain security. In the framework, the block validations are assisted and implemented by various lightweight nodes, e.g., edge devices, in addition to the typical blockchain miners. This improves the blockchain security but can cause an increased block validation delay and, thereby, reduced blockchain throughput. To tackle this challenge, we propose an effective method to select lightweight nodes based on their computing powers to maximize the blockchain throughput, and prove the uniqueness of the optimal nodes selection strategy. Security analysis and simulation results from the deployed consortium blockchain platform show that the proposed framework achieves higher throughput and security than the existing consortium blockchain models.

The privacy-preserving query is critical for modern blockchain systems, especially when supporting many crucial applications such as finance and healthcare. Recent advances in blockchain query schemes mainly focus on enhancing the traceability efficiency of integrity authentication. Despite these efforts, we argue that the exposure of retrieval information may result in privacy leakage, which inevitably poses an important yet unresolved challenge. In this paper, we introduce Cloak, a novel privacy-preserving blockchain query scheme with two notable features. First, it utilizes a two-phase distributed query requests technique, i.e., division and aggregation, to hide retrieval information based on the natural independent characteristic of blockchain. Second, we add noise to the sub-request set to avoid malicious attacks during transmission and adopt smart contract-based asymmetric encryption to guarantee the correctness of query results. Experimental results demonstrate that Cloak improves the query performance by up to 4× and reduces the storage overhead by 50% compared with the state-of-the-art Spiral.

In the era driven by big data and algorithms, the efficient collaboration of pervasive computing power is crucial for rapidly meeting computing demands and enhancing resource utilization. However, current mainstream end-edge-cloud collaboration faces challenges of computing isolation, adversely affecting resource efficiency and user experience. The Computing Power Network (CPN) is a novel architecture designed to sense and collaborate ubiquitous computing resources through networks. Nevertheless, the expansion of its scope and the integration of networks complicate task scheduling. To address this, we design a collaborative scheduling system that considers the joint selection of computing nodes and network links, aiming to reduce delay, enhance reliability, and ensure long-term load balance. First, we propose a delay-prioritized reliable scheduling policy based on a dual-priority mechanism for forwarding and computing. Second, we define the scheduling problem as a Constrained Markov Decision Process (CMDP) and introduce Lyapunov optimization to transform constraints into instantaneous optimizations, achieving a long-term balanced load of computing and network resources. Lastly, we employ an enhanced Deep Reinforcement Learning (DRL) approach to solve the problem. Performance evaluation demonstrates that compared to standard DRL, the proposed algorithm effectively reduces delay and improves reliability while maintaining long-term load balance, resulting in an overall performance improvement of 54.7%.

Matchmaking encryption is a method employed to address the security and privacy concerns of cloud-enabled IoT healthcare. Nevertheless, matchmaking encryption technology encounters challenges in effectively implementing critical functionalities, such as resolving a single-key-exposure problem, achieving secure short-epoch communication, and simultaneously enabling lightweight computation and communication overheads for IoT healthcare. These challenges pose obstacles to the widespread adoption of this technology. To tackle these constraints, we first present aReputation incentive committeeConsensus-based forMatchmakingEncryption in IoT healthcare (RCME), which utilizes consensus nodes to eliminate the single-key-exposure problem and enables fast provision of permission proof based on our design reputation incentive mechanism. The proposed RCME scheme adopts low-consumption pairing-free technology to realize lightweight matchmaking encryption in a multi-party, non-interactive certificateless cryptosystem. Rigorous security analysis shows it achieves chosen ciphertext attack security under the random oracle model. To further reduce consensus communication overhead from$\mathcal {O}(n^{2})$to$\mathcal {O}(n)$, we propose an optimized Practical Byzantine Fault Tolerance (PBFT) consensus, and we adopt reputation incentive mechanism and threshold cryptography technology to achieve unbiased leader election. The comprehensive evaluation corroborates that our solutions outperform the existing state-of-the-art schemes regarding security and performance. Therefore, our RCME scheme is a practical solution for resource-constrained IoT healthcare devices.

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

Intelligent reflecting surface (IRS) can reconfigure the channel conditions, while the passive beamforming gain is limited by the severe double path-loss effect. Fortunately, active IRS (AIRS) is emerging to tackle obstacles by simultaneously adjusting the phase and amplitude of each reflection element. In this paper, we propose an AIRS-assisted unmanned aerial vehicle (UAV)-relaying scheme, where the AIRS is equipped on the UAV to reflect the signal from the ground base station (GBS) to users via non-orthogonal multiple access. We jointly adjust beamforming vectors at the GBS, reflection matrix of the AIRS and UAV trajectory to maximize the average sum rate. However, the problem is non-convex. Thus, it is decomposed into three subproblems via block coordinate descent. The beamforming optimization at the GBS is transformed into a standard semidefinite program through semidefinite relaxation. Then, the reflection matrix of AIRS and UAV trajectory subproblems are solved through successive convex approximation. Ultimately, we design an iterative algorithm to effectively tackle the original problem. Simulation results are shown to verify the performance of designed scheme.

The integration of semantic communication with mobile edge computing (MEC) has emerged as a prominent research area. In this paper, we explore a novel scenario where semantic communication is integrated with unmanned aerial vehicles (UAVs) to enhance MEC, particularly in the face of jamming attacks. Our research focuses on addressing the resource management challenge to minimize task completion time and maximize semantic spectral efficiency (SSE) while adhering to quality of service requirements and resource constraints. Given the non-convexity of this problem and the dynamic behavior of jamming attacks, this paper proposes a deep reinforcement learning (DRL) algorithm by jointly optimizing UAV trajectories, user associations, and channel selections against jamming. In detail, the proposed anti-jamming DRL-based resource management approach can effectively capture the jammer’s behavior, and learn to adjust semantic task and resource scheduling strategies with the objective to minimize the negative effect of jamming attacks on task offloading and semantic communication. Simulation results demonstrate that the proposed approach outperforms baseline algorithms in terms of task completion time and total SSE under different real-world settings.

In this paper, we investigate a cognitive multicast communication system aided by active reconfigurable intelligent surface (active RIS). Specifically, for an underlay spectrum sharing cognitive multicast network, a cognitive radio base station (CRBS) communicates with secondary users (SUs) assisted by an active RIS. Meanwhile, the interference to primary users (PUs) is suppressed within the constraints of the transmit power of both the CRBS and active RIS, together with the restriction of the active RIS amplitude gain. We aim at the fairness problem for maximizing the minimum signal-to-interference-plus-noise-ratio (SINR) via joint beamforming design at the CRBS and the active RIS. To cope with this problem, the optimization and deep reinforcement learning (DRL) based algorithms are proposed. Specifically, the decision variables are decoupled by the alternating optimization (AO) method and then, the non-convex problem is transformed into a solvable convex form by using the successive convex approximation (SCA), Schur complement, and penalty convex-concave procedure (PCCP) methods. Furthermore, we design an AO-based algorithm for the formulated problem. Due to the characteristics of both exploration and exploitation, the DRL-based algorithms outperform the AO-based algorithm with proper parameter settings. Meanwhile, the DRL algorithm inherits the advantages of low execution complexity. The original optimization problem is first converted into a Markov decision process (MDP) form in DRL. Due to the complex objective function and various restrictions of power/amplification gain budget and quality of service (QoS), the constraints are categorized as the switching constraints for action adjustment and performance constraints for reward function setting, respectively. Subsequently, a segmented incentive-based reward function is developed to attain higher performance on SINR. We also propose two effective deep deterministic policy gradient (DDPG)-based and twin delayed deep deterministic policy gradient (TD3)-based algorithms. Finally, the simulation results demonstrate a notable enhancement in system performance upon the introduction of active RIS compared to the case with a passive RIS and the case without using an RIS. Moreover, with appropriately configured parameters, DRL algorithms outperform the AO-based algorithm, and notably, the TD3 algorithm is superior to the DDPG algorithm in optimization effectiveness.

Mobile edge computing (MEC) enables communication users with limited computation power to offload computation-intensive tasks to the edge server, thus dramatically enhancing the limited computing capabilities of the users. As the reality of scarce spectrum resources and the energy-constrained nature of communication users, this paper introduces non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) techniques to achieve more efficient task offloading in MEC. To minimize the number of computationally failed tasks while simultaneously satisfying different quality of service (QoS) requirements of users, a joint resource management problem of the spectrum, computation, and energy resources is formulated. Due to the non-convexity of the offloading optimization problem and the stochastic nature of the constructed MEC environment, a multiple agents deep deterministic policy gradient (MADDPG)-based resource management algorithm is proposed to manage each user’s multidimensional resources without collaborating. The simulation results show that compared to other benchmark schemes, the proposed algorithm can effectively improve both the communication and computational performances in MEC.

The growth of intelligent vehicular services, like augmented reality (AR) road simulation, underscores the need for rapid, multi-modal content generation. Generative artificial intelligence (GAI) models, known for their swift production of diverse artificial intelligence-generated content (AIGC), stand out as a prime solution. However, integrating cloud-centric GAI models into vehicular networks is fraught with challenges. Notably, to offer specialized generative edge intelligence (EI) and boost vehicular AIGC, GAI models need to tap into user data and utilize significant computation resources. Moreover, their deployment across vehicular networks is essential for proximity-based distributed inferences. Yet, edge devices are resource-limited, and data sharing can raise safety and privacy concerns. Addressing these challenges, this paper introduces GAI-IoV, an EI-enabled GAI framework facilitated through the cooperation between road-side units (RSUs) and vehicles. Subsequently, we propose the workflow for collaborative fine-tuning and distributed inference. On this basis, two pivotal vehicle-centric problems are then formulated: computation and communication resource allocation for federated fine-tuning (FFT) to optimize time and energy cost, and splitting strategy of shared and local inferences to optimize inference latency and content-generation capability. To solve these optimizations, we introduce a self-adaptive global best harmony search (SGHS) algorithm for resource allocation and a backward induction method for determining inference splitting strategy. Our experiments based on the Stable Diffusion v1-4 model vouch for a superior fine-tuning and inference capabilities of GAI-IoV. Furthermore, simulations underscore its resource utilization and distributed inference efficiency in dynamic vehicular scenarios.

With the rapid development of maritime activities, efficient and reliable maritime communications have attracted ever-increasing attention, and mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, can provide flexible and adaptable services for maritime communications. In this paper, we investigate a UAV-RIS-assisted maritime communication system under a malicious jammer, where a UAV-RIS is deployed to jointly adjust its placement and RIS surface elements to maximize the system energy efficiency (EE) and guarantee quality of service requirements against jamming attacks. In addition, an adaptive energy harvesting scheme is developed for information transmission (IT) and energy harvesting (EH) simultaneously to enhance the endurance of the UAV by deploying different IT times for each RIS element. Considering the non-convex optimization problem and highly complex maritime environments, an intelligent resource management approach based on deep reinforcement learning is proposed to jointly optimize the base station’s transmit power, placement of UAV-RIS, and RISs reflecting beamforming. Furthermore, hindsight experience replay is adopted to improve the learning efficiency and performance. The simulation results demonstrate that the proposed approach achieves the better EE and EH performances under different real-world settings compared with existing popular approaches.

Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS’s passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.

Metaverse provides embodied artificial-reality experience to the users in the virtual spaces. Many innovative and creative services such as virtual conference and tourism have been realized in the digital twins maintained by the virtual service providers (VSP) in Metaverse. Digital twins are digital copies of the physical world constructed virtually by the VSPs using real-world data. For a realistic experience, VSPs need to collect data that is up-to-date and relevant to their services. In this paper, we propose an incentive design framework to support the data trading between VSPs and edge devices. In the auction model, we model the valuation of data by considering data relatedness and data freshness. In our model, the semantic communication model is used to filter the relevant data, and the age of information (AoI) metric is used to assess the data freshness. Results show that by considering the data freshness, our mechanism helps to increase the average update frequency so that the VSPs obtain fresh data for construction of digital twins. Our model ensures the desired properties of individual rationality, incentive compatibility, and budget balance.

In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing scheme among users to replace the heavy and repetitive computation. In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications. Specifically, the view images of MR users in the same real-world scenario may be analogous. Therefore, when one user (i.e., a device) completes some computation tasks, the user can send his own calculation results and the semantic features extracted from the user's own view image to nearby users (i.e., other devices). On this basis, other users can use the received semantic features to obtain the spatial matching of the computational results under their own view images without repeating the computation. Using generalized small-scale fading models, we analyze the key performance indicators of full-duplex D2D communications, including channel capacity and bit error probability, which directly affect the transmission of semantic information. Finally, the numerical analysis experiment proves the effectiveness of our proposed methods.

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

Unmanned aerial vehicles (UAVs) have been increasingly employed as aerial servers in mobile edge computing (MEC) systems, providing essential computing, communication, and storage services for edge users. This UAV-assisted MEC paradigm shows great promise in enhancing both computing and communication performances. However, the presence of malicious jammers poses significant challenges to the system's reliability and efficiency. In this study, we explore the resource management problem in a multi-UAV-assisted MEC scenario under the influence of multiple malicious jammers. To mitigate the impact of jamming attacks, we propose a resource management approach with the primary objective of minimizing system energy consumption and latency while adhering to UAV energy constraints. Due to the dynamic and time-varying nature of the communication environment, we present a deep reinforcement learning (DRL)-based algorithm that dynamically adjusts the CPU frequency and communication bandwidth of the UAV to optimize the system performance even under jamming attacks. Through simulations, we demonstrate the effectiveness of the proposed algorithm in significantly reducing the overall system latency (both computational and communication latency) as well as minimizing energy consumption.

Cognitive unmanned aerial vehicle (UAV) communication systems combine benefits of both the cognitive radio and UAV, which improves the spectral efficiency and communication coverage area. However, high-quality air-ground channel links maybe more vulnerable to potential eavesdropping or jamming attacks. Thus, this paper proposes a secure transmission approach assisted by deploying a cooperative UAV to transmit artificial noise to jam an active eavesdropper, in order to maximize the system secrecy rate under the quality of service (QoS) requirement of a primary device. Specifically, we jointly optimize the flight trajectory and transmission power of the cooperative jammer to maximize the system's secrecy rate under strict constraints. To achieve this, we convert the non-convex problem into an approximately convex problem using the block coordinate descent algorithm and successive convex approximation method. Simulation results show that compared to existing algorithms, the proposed algorithm in this study can significantly improve the system's secrecy rate.

Recently, diverse emerging scenarios have precipitated a substantial surge in the variety of devices and applications, which has consequently imposed more stringent demands on Quality of Service (QoS) prerequisites. As a burgeoning emerging network measurement method, In-band network telemetry (INT), can provide detailed metrics for QoS by obtaining fine-grained network status information. However, INT only outlines device-level operations, which fails to provide an entire network view for monitoring. To address this, INT orchestration based on network topology and application requirements to achieve network-level monitoring is necessary. In this paper, we propose an INT orchestration model that efficiently measures the entire network while minimizing measuring overhead. The model outputs the probe path and collects requirements for the devices it passes through. Our method effectively reduces network bandwidth consumption caused by INT process and ensures telemetry items remain fresh. Experiment results support the effectiveness of our approach.

With the rapid development of artificial general intelligence (AGI), various multimedia services based on pretrained foundation models (PFMs) need to be effectively deployed. With edge servers that have cloud-level computing power, edge intelligence can extend the capabilities of AGI to mobile edge networks. However, compared with cloud data centers, resource-limited edge servers can only cache and execute a small number of PFMs, which typically consist of billions of parameters and require intensive computing power and GPU memory during inference. To address this challenge, in this paper, we propose a joint foundation model caching and inference framework that aims to balance the tradeoff among inference latency, accuracy, and resource consumption by managing cached PFMs and user requests efficiently during the provisioning of generative AI services. Specifically, considering the in-context learning ability of PFMs, a new metric named the Age of Context (AoC), is proposed to model the freshness and relevance between examples in past demonstrations and current service requests. Based on the AoC, we propose a least context caching algorithm to manage cached PFMs at edge servers with historical prompts and inference results. The numerical results demonstrate that the proposed algorithm can reduce system costs compared with existing baselines by effectively utilizing contextual information.

Vehicles on the road generate a large amount of data, which often can be used to train models for destination prediction and traffic flow prediction in intelligent transportation systems (ITS). To break down the information silos and further strengthen privacy protection, we leverage federated learning and differential privacy in this paper. In order to motivate the participation of data owners, we further devise a single-leader multi-follower Stackelberg game incentive mechanism which accounts for the heterogeneous privacy budgets and participation costs of vehicle owners. Due to the lack of prior knowledge, deep reinforcement learning is used to obtain the approximate solution for each player to achieve the Stackelberg equilibrium. Our proposed framework is capable of achieving a solution close to the Nash Equilibrium.

In heterogeneous networks (HetNets), although deploying numerous small base stations (SBSs) can effectively enhance spectral efficiency (SE), it is difficult to achieve seamless coverage due to their fixed locations. To handle this issue, we propose a HetNet structure assisted by unmanned aerial vehicles (UAVs), where the high mobility and flexible deployment of UAVs are leveraged. However, interference is inevitable in the proposed UAV-assisted HetNet, thus we design a comprehensive interference management (IM) scheme, selectively adopting successive interference cancellation (SIC) algorithm, space-time interference alignment (STIA) and interference steering (IS) according to the location of users and interference types. The numerical results verify that with SIC-STIA-IS scheme, the proposed UAV-assisted HetNet is advantageous in degrees of freedom (DoF) and sum rate.

In this paper, a novel paradigm of mobile edgequantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.

In this paper, the performance of a multiple-in-single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system assisted by simultaneous trans-mitting and reflecting reconfigurable intelligent surface (STAR-RIS) under fading channel is studied. Firstly, the joint BS active beamforming and STAR-RIS passive beamforming are discussed. Based on that and using the Gamma approximation method, the statistical characteristics of the equivalent cascaded channels for BS-IR and BS-ER assisted by STAR-RIS are analyzed and derived, including the first-order and second-order moments, as well as the distribution function (CDF) and probability density function (PDF). Furthermore, we define and derive the rate outage probability of IR and power outage probability of ER and their approximate expressions at high SNR. Finally, the theoretical analysis results are verified by numerical simulations, and it is confirmed that the number of STAR-RIS units has positive effects on improving the system outage performance.

The integration of unmanned aerial vehicles (UAVs) with 5G networks presents a promising opportunity to revolutionize wireless communication and provide high-speed internet access to remote areas. Nevertheless, the vast quantity of data generated by UAVs requires the implementation of efficient distributed learning techniques. In this study, we present a novel hybrid approach that merges Federated Learning (FL) and Split Learning (SL) to optimize the performance of UAV swarms in 5G networks. While FL is capable of reducing communication overhead and preserving privacy, SL can enhance the accuracy of the model through the utilization of the local computational resources of each device. To realize the hybrid approach, we first locally train the model on each UAV using split learning. Subsequently, the encrypted model parameters are transmitted to a central server for federated averaging. Finally, the updated model is dispatched back to each UAV for local fine-tuning, and this cycle is repeated until convergence is achieved. The hybrid approach capitalizes on the strengths of both FL and SL to minimize communication overhead and increase accuracy. To tackle the challenge of selecting the most suitable UAVs for participation in the learning process, we propose a multiagent algorithm that considers factors such as communication latency and training time. Our experimental results indicate that the proposed approach leads to substantial improvements in communication overhead and accuracy compared to conventional methods.

Quantum cloud computing is a promising paradigm for efficiently provisioning quantum resources (i.e., qubits) to users. In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. Literally, the cost of quantum resources in the reservation plan is expected to be cheaper than the cost of quantum resources in the on-demand plan. However, quantum resources in the reservation plan have to be reserved in advance without information about the requirement of quantum circuits beforehand, and consequently, the resources are insufficient, i.e., under-reservation. Hence, quantum resources in the on-demand plan can be used to compensate for the unsatisfied quantum resources required. To end this, we propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized. Particularly, the objective is to minimize the total costs of quantum circuits under uncertainties regarding qubit requirement and minimum waiting time of quantum circuits. In experiments, practical circuits of quantum Fourier transform are applied to evaluate the proposed qubit resource allocation. The results illustrate that the proposed qubit resource allocation can achieve the optimal total costs.

Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inverse semantic communications as a new paradigm to achieve lightweight wireless sensing using the reconfigurable intelligent surface (RIS). Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message. Specifically, we first develop a novel RIS hardware for encoding several signal spectrums into one MetaSpectrum. We then propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from the real world, we show that our framework can reduce the data volume by 90% compared to that before encoding, without affecting the execution of various sensing tasks. Experiment results also demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.

With the development of mobile services such as autonomous driving and the industrial internet, ultralow latency and pervasive mobility have become key characteristics of the intelligent interconnections among people, machines, and things. As a prevailing mobile network architecture, the network function virtualization (NFV)-based edge-cloud architecture brings the computing and memory resources closer to the end user, significantly reducing service delays and supporting more efficient mobility management. However, the geographically distributed nature of the edge-cloud architecture and the quality of service (QoS) requirements of latency-sensitive services in extreme mobile scenarios make service function chain (SFC) deployment more challenging. In this paper, we investigate a mobility-aware SFC deployment scheme with service migration in an NFV-based edge-cloud system. To properly cope with the mobility pattern of mobile services, a multistage decision-making problem is formulated, aiming to jointly minimize the long-term deployment and migration costs and the average end-to-end service latency while simultaneously satisfying various QoS constraints for services and the physical resource constraints of the edge-cloud system. Then, to address the formulated problem, a deep reinforcement learning (DRL)-based online SFC deployment algorithm is proposed that can automatically detect variations in the widely distributed edge-cloud environment and generate online deployment solutions without human intervention to implement adaptive and fast service provision and also support mobile service migration. Extensive experimental results demonstrate our proposed scheme surpasses its competitors in terms of end-to-end latency and migration cost, with average reductions of 6.26% and 18.77%, respectively, while improving the average service acceptance rate by 19.19%.

Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.

Unmanned aerial vehicles (UAVs) have gained wide research interests due to their technological advancement and high mobility. The UAVs are equipped with increasingly advanced capabilities to run computationally intensive applications enabled by machine learning techniques. However, because of both energy and computation constraints, the UAVs face issues hovering in the sky while performing computation due to weather uncertainty. To overcome the computation constraints, the UAVs can partially or fully offload their computation tasks to the edge servers. In ordinary computation offloading operations, the UAVs can retrieve the result from the returned output. Nevertheless, if the UAVs are unable to retrieve the entire result from the edge servers, i.e., straggling edge servers, this operation will fail. In this article, we propose a coded distributed computing (CDC) approach for computation offloading to mitigate straggling edge servers. The UAVs can retrieve the returned result when the number of returned copies is greater than or equal to the recovery threshold. There is a shortfall if the returned copies are less than the recovery threshold. To minimize the cost of the network, energy consumption by the UAVs, and prevent over and under subscription of the resources, we devise a two-phase stochastic coded offloading scheme (SCOS). In the first phase, the appropriate UAVs are allocated to the charging stations amid weather uncertainty. In the second phase, we use the$z$-stage stochastic integer programming (SIP) to optimize the number of computation subtasks offloaded and computed locally, while taking into account the computation shortfall and demand uncertainty. By using a real data set, the simulation results show that our proposed scheme is fully dynamic and minimizes the cost of the network and UAV energy consumption amid stochastic uncertainties.

As edge computing faces increasingly severe data security and privacy issues of edge devices, a framework called federated edge learning (FEL) has recently been proposed to enable machine learning (ML) model training at the edge, ensuring communication efficiency and data privacy protection for edge devices. In this paradigm, the training efficiency has long been challenged by the heterogeneity of communication conditions, computing capabilities, and available data sets at devices. Currently, researchers focus on solving this challenge via device selection from the perspective of optimizing energy consumption or convergence speed. However, the consideration of any one of them is insufficient to guarantee the long-term system efficiency and stability. To fill the gap, we propose an optimization problem to simultaneously minimize the total energy consumption of selected devices and maximize the convergence speed of the global model for device selection in FEL, under the constraints of training data amount and time consumption. For the accurate calculation of energy consumption, we deploy online bandit learning to estimate the CPU-cycle frequency availability of each device, based on an efficient algorithm, named fast-convergent energy-efficient device selection (FCE2DS), is proposed to solve the optimization problem with a low level of time complexity. Through a series of comparative experiments, we evaluate the performance of the proposed FCE2DS scheme, verifying its high training accuracy and energy efficiency.

Massive amounts of high-quality data are the prerequisite and support for AI technologies. Due to the nature of privacy-preserving and low communication overheads, federated learning (FL) has garnered considerable attention in comparison with traditional data collection methods. However, the performance of FL is hampered by the lack of interested clients and limited local data due to selfishness and individual behavioral preferences. To this end, we propose PractFL, a proactive and distributed framework that incorporates the concept of mobile crowdsensing into the FL paradigm. Specifically, we design an incentive mechanism in the form of virtual red packets, which are a widely used way of monetary reward and gift-giving in social lives. In this article we extend this further by giving meaning to the locations, i.e., the red packets are only accessible at specific places. The virtual red packets’ locations and monetary amounts can be dynamically updated by the cloud center to encourage clients to collect additional data that may benefit the FL process. Further, we propose a distributed behavioral decision engine based on multiarmed bandits (i.e., choose which red packet to go for) in response to the incentive mechanism enforced by the cloud. Considering the movement cost and conflicts with other clients,$K$-anonymity and probabilistic selection are introduced in the distributed behavioral decision to recommend the optimal red packet choice for clients without revealing their privacy. The experimental results demonstrate that PractFL outperforms the baselines in terms of classification accuracy. We also find that PractFL can effectively alleviate the overfitting problem caused by class imbalance during the training.

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground-truth-labeled data between unmanned aerial vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a semisupervised federated learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose a model parameter mixing strategy to improve the naive combination of federated learning and semisupervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as federated mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client’s participation in training, namely, the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-independent and identically distributed(IID) levels of client data.

For future Internet of Things (IoT) systems, data-driven and dynamic spectrum-sharing schemes can significantly improve the spectrum utilization and efficiency. However, conventional centralized architecture of such dynamic IoT spectrum-sharing systems is often considered to be nontransparent, costly, and vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-based dynamic spectrum-sharing scheme has been proposed and investigated in this work, which aims at enhancing the system by providing desirable features, such as decentralization, transparency, immutability, and auditability. By considering the privacy and transaction dynamics issues when blockchain is integrated into spectrum-sharing systems, a privacy-preserving double auction mechanism based on differential privacy is developed for incentivizing spectrum sharing, where the time-varying valuations of the spectrum resources are also taken into consideration. In the proposed auction, a winner determination problem (WDP) is formulated to decide the winning bidders and spectrum allocation. A deep reinforcement learning (DRL)-based method is then proposed for efficiently solving the WDP. The proposed auction mechanism can be integrated with smart contracts on blockchain platforms. Furthermore, the computation of the DRL-based method for solving the WDP is designed as part of the consensus mechanism in the blockchain. Theoretical analysis show that the proposed privacy-aware double auction mechanism satisfies the properties of differential privacy, individual rationality, and truthfulness. Finally, simulation results are provided to validate the performance of the spectrum-sharing approach.

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

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

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

Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e.g., smart healthcare. In this paper, we focus on UAV image-sensing-driven task-oriented semantic communications scenarios. The majority of existing work has focused on designing advanced algorithms for high-performance semantic communication. However, the challenges, such as energy-hungry and efficiency-limited image retrieval manner, and semantic encoding without considering user personality, have not been explored yet. These challenges have hindered the widespread adoption of semantic communication. To address the above challenges, at the semantic level, we first design an energy-efficient task-oriented semantic communication framework with a triple-based scene graph for image information. We then design a new personalized semantic encoder based on user interests to meet the requirements of personalized saliency. Moreover, at the communication level, we study the effects of dynamic wireless fading channel on semantic transmission mathematically and thus design an optimal multi-user resource allocation scheme by using game theory. Numerical results based on real-world datasets clearly indicate that the proposed framework and schemes significantly enhance the personalization and anti-interference performance of semantic communication, and are also efficient to improve the communication quality of semantic communication services.

The collaborative edge and cloud computing system has emerged as a promising solution to fulfill the unprecedented high requirements of 5G application scenarios. Due to vendor variations, it is often difficult to manage hardware facilities in such a collaborative system. Moreover, the amount of data generated and tasks requested by end devices are increasing exponentially, which introduces storage and computation bottlenecks. To address these issues, a novel systematic framework called software-defined edge and cloud computing (SD-ECC) is designed to manage the underlying physical resources of edge and cloud layers via software. SD-ECC is combined with an erasure-coded storage system, for which a task scheduling problem is formulated by considering data access and task processing steps. Then, a joint data access and task processing (JDATP) algorithm is proposed to minimize the task response time including data access latency and task processing latency. A practical SD-ECC platform is developed on OpenStack, OpenDaylight, and Kubernetes to conduct experiments with real-world datasets. The experimental results demonstrate that our proposed JDATP algorithm can reduce 20.87% of the task response time and increase 14.16% of the remaining storage space on average by comparing it with alternative schemes.

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

With the benefits of unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS), they can be combined to further enhance the communication performance. However, the high-quality air-ground channel is more vulnerable to the adversarial eavesdropping. Therefore, in this paper, we propose a covert communication scheme assisted by the UAV-IRS to maximize the covert transmission rate. Specifically, the ground transmitter, Alice, secretly delivers the private message to a legitimate receiver, Bob, via the UAV-IRS, wishing that the transmission will not be observable by the warden, Willie. In addition, Willie is adversarial to Alice and the UAV-IRS, which makes his accurate location difficult to obtain. Given this fact, we first determine an optimal detection threshold and derive the error detection probability at Willie, which is the worst-case situation for the legitimate transmission. Then, we maximize the covert transmission rate by alternatively optimizing the transmit power of Alice, the IRS phase shift and the horizontal location of UAV-IRS subject to the covert requirements. Numerical results are presented to demonstrate the effectiveness of the proposed covert communication scheme assisted by UAV-IRS.

The popularization of smart Internet of Things (IoT) devices has facilitated the development of fog/edge computing. However, these infrastructure-based service paradigms may fail to complete tasks successfully due to computation and communication overload, or damage in challenging scenarios such as disasters or traffic jams. Noticing that a crowd of devices with considerable idle resources could be available, we investigate the problems of addressing the computation and communication unavailability with peer assistance in this work. To this end, we propose a dispersed service framework for resource-exhausted scenarios that adaptively offloads users’ data to available network computation points. However, the users may not be able to achieve the offloading due to geographical hindrances. Consequently, the relay is introduced as a bridge for data offloading between the users and the network computation points. Furthermore, a game-based incentive-driven offloading mechanism is designed by analyzing and balancing the cost and gain factors of three main entities (users, relays, and network computation points). Considering the interactions among the entities, a two-level Stackelberg game is established for efficiently allocating potential computation resource, as well as balancing the utility conflicts due to the data offloading. Given the hierarchical interaction structure, the upper level game involves network computation points as followers and the relay as a leader, while the lower level game includes the relay as a follower and users as leaders. Moreover, to facilitate applicability in large-scale scenarios with multiple relays, we decompose multiple relays into multiple single relay problems using a tripartite matching strategy that assigns appropriate relays to users and network computation points. The simulation results demonstrate the effectiveness of the proposed game-based incentive-driven mechanism and show that it outperforms the baselines in terms of the overall utilities of the involved entities and the average energy consumption of users.

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to provide seamless coverage of the vehicular network. The presence of uncovered areas tends to hinder the prevalence of the in-vehicle services with large data volume. To this end, we propose a predictive cooperative multi-relay transmission strategy (PreCMTS) for the intermittently connected vehicular networks, fulfilling the 6G vision of semantic and green communications. Specifically, we introduce a task-driven knowledge graph (KG)-assisted semantic communication system, and model the KG into a weighted directed graph from the viewpoint of transmission. Meanwhile, we identify three predictable parameters about the individual vehicles to perform the following anticipatory analysis. Firstly, to facilitate semantic extraction, we derive the closed-form expression of the achievable throughput within the delay requirement. Then, for the extracted semantic representation, we formulate the mutually coupled problems of semantic unit assignment and predictive relay selection as a combinatorial optimization problem, to jointly optimize the energy efficiency and semantic transmission reliability. To find a favorable solution within limited time, we proposed a low-complexity algorithm based on Markov approximation. The promising performance gains of the PreCMTS are demonstrated by the simulations with realistic vehicle traces generated by the SUMO traffic simulator.

Visible light communication (VLC) is an emerging wireless technology to support high transmission rate for indoor devices by using existing lighting infrastructure, and VLC-based indoor localization is capable of providing high-accuracy localization. However, current VLC-based localization systems suffer from several key challenges such as sensitivity to random tilting of the receiver, which limits its full potential in real-world applications. In this paper, we design an integrated visible light communication and localization (VLCL) system to simultaneously support accurate real-time localization and communication services for indoor devices. To achieve this, an advanced differential phase difference of arrival (A-DPDOA) localization design is developed to simplify hardware and improve tracking robustness. In addition, a joint adaptive modulation, subcarrier and power allocation scheme is also proposed, which aims to improve the communication data rate and localization accuracy. Extensive experiments are performed to demonstrate that the proposed integrated VLCL system achieves higher localization accuracy and transmission data rate, compared to existing systems and schemes. Experiments also illustrate that the localization algorithm is more robust against the random tilting of the receiver under device movement in two-dimensional and three-dimensional scenarios.

Remote first-aid treatment on ambulances is a promising application of 5G. However, there still exist gaps between the capabilities of current 5G networks and the stringent requirements of remote emergency on ambulances. Dual connectivity (DC) is an efficient technology to fill these gaps by integrating 5G millimeter wave (mmWave) with Sub-6GHz networks. In this paper, we investigate a dual-connectivity handover scheme to enhance the transmission rate of the wireless links for a 5G-enabled ambulance. Due to the long delay caused by signal transmission and processing, the conventional handover schemes based on reference signal received power (RSRP) measured by users are not sufficiently sensitive to the rapidly changing propagation environments surrounding the 5G-enabled ambulance. Instead, considering the randomness of environments and the delay caused by the handover process, we employ a deep Q network (DQN)-based algorithm to find a far-sighted policy for solving the handover problem. However, due to the drawbacks of single-step bootstrapping, value overestimation, and low-efficiency exploration, the vanilla DQN is performance-limited. To this end, we adopt effective techniques including multi-step learning, double DQN, and NoisyNet to improve learning performances, and propose a noisy double DQN (NDDQN)-based dual-connectivity handover scheme. Simulation results verify the effectiveness and superiority of our NDDQN-based handover scheme compared with the vanilla DQN and upper confidence bound (UCB)-based handover schemes, and then show that our handover scheme can adapt to various handover models.

Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely employed assumption is that of a simple common component shared among multiple graphs. However, in practice, a more intricate topological pattern, comprising simultaneously ofhomogeneousandheterogeneouscomponents, would exhibit in multiple graphs. In this paper, we propose a general graph estimator based on a novel structural fusion regularization that enables us to jointly learn multiple graphs with such complex topological patterns, and enjoys rigorous theoretical guarantees. Specifically, in the proposed regularization term, the structural similarity among graphs is characterized by a Gram matrix, which enables us to flexibly model different types of network structural similarities through different Gram matrix choices. Algorithmically, the regularization term, coupling the parameters together, makes the formulated optimization problem intractable, and thus, we develop an implementable algorithm based on the alternating direction method of multipliers (ADMM) to solve it. Theoretically, non-asymptotic statistical analysis is provided, which precisely characterizes the minimum sample size required for the consistency of the graph estimator. This analysis also provides high-probability bounds on the estimation error as a function of graph structural similarities and other key problem parameters. Finally, the superior performance of the proposed method is demonstrated through simulated and real data examples.

We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as “masters” offloading their computations to edge nodes acting as “workers”. A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers; ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them; ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.

The explosive amount of data generated at the network edge makes mobile edge computing an essential technology to support real-time applications, calling for powerful data processing and analysis provided by machine learning (ML) techniques. In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models. Existing studies on FEL either utilize in-process optimization or remove unqualified participants in advance. In this paper, we enhance the collaboration from all edge devices in FEL to guarantee that the ML model is trained using all available local data to accelerate the learning process. To that aim, we propose acollective extortion (CE)strategy under the imperfect-information multi-player FEL game, which is proved to be effective in helping the server efficiently elicit the full contribution of all devices without worrying about suffering from any economic loss. Technically, our proposed CE strategy extends the classical extortion strategy in controlling the proportionate share of expected utilities for a single opponent to the swiftly homogeneous control over a group of players, which further presents an attractive trait of being impartial for all participants. Moreover, the CE strategy enriches the game theory hierarchy, facilitating a wider application scope of the extortion strategy. Both theoretical analysis and experimental evaluations validate the effectiveness and fairness of our proposed scheme.

Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages, such as decentralization and privacy protection of raw data. However, there has been few studies focusing on the allocation of resources for the participated devices (i.e., clients) in the BCFL system. Especially, in the BCFL framework where the FL clients are also the blockchain miners, clients have to train the local models, broadcast the trained model updates to the blockchain network, and then perform mining to generate new blocks. Since each client has a limited amount of computing resources, the problem of allocating computing resources to training and mining needs to be carefully addressed. In this paper, we design an incentive mechanism to help the model owner (MO) (i.e., the BCFL task publisher) assign each client appropriate rewards for training and mining, and then the client will determine the amount of computing power to allocate for each subtask based on these rewards using the two-stage Stackelberg game. After analyzing the utilities of the MO and clients, we transform the game model into two optimization problems, which are sequentially solved to derive the optimal strategies for both the MO and clients. Further, considering the fact that local training related information of each client may not be known by others, we extend the game model with analytical solutions to the incomplete information scenario. Extensive experimental results demonstrate the validity of our proposed schemes.

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

To accommodate the explosive growth of the Internet of Things (IoT), incorporating interference alignment (IA) into existing multiple access (MA) schemes is under investigation. However, when it is applied in MIMO networks to improve the system capacity, the new problem regarding information delay arises which does not meet the requirement of low-latency. Therefore, in this paper, we first propose a new metric named degree of delay (DoD) to quantify the issue of information delay. By analyzing DoD with classical transmission schemes, it can be seen that the information latency does affect the performance of the system. To cope with this issue, hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR) is first proposed. It achieves optimal performance in 2-user MIMO scenarios, but suffers a performance loss in$K$-user MIMO scenarios. Then, the improved HAA-PIE-RIR scheme (HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR) are proposed. The former achieves optimal performance in$K$-user MIMO scenarios, but requires heavy computational cost. The latter is a trade-off scheme considering performance and computational cost comprehensively. Overall, our proposed schemes can obtain lower DoD and higher DoF than that of traditional IA schemes.

Amid data privacy concerns, Federated Learning (FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists a need for a platform that matches data owners (supply) with model requesters (demand). In this paper, we present CrowdFL, a platform to facilitate the crowdsourcing of FL model training. It coordinates client selection, model training, and reputation management, which are essential steps for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. To the best of our knowledge, it is the first platform to support crowdsourcing-based FL on edge devices.

As countries enter the endemic phase of COVID-19, people's risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowd-sourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.

Intelligent reflecting surface (IRS) is considered as an effective technology to enhance the performance of wireless communication systems. In this paper, the robust optimization design of the IRS-assisted wireless multi-group multicast MISO system with statistical CSI errors is investigated. Two optimization problems, namely max-min fairness problem and QoS problem, are discussed separately. In order to deal with the non-convex imperfect CSI constraint, the Bernstein-type inequality is utilized to transform the outage probability constraint into a second-order cone (SOC) constraint and linear inequalities. Furthermore, two efficient algorithms based on alternating optimization (AD) are proposed to solve the reformulated problems, respectively. In particular, the semi-definite relaxation (SDR) technique is applied to optimize the transmit beamforming and IRS reflection coefficients. The numerical simulation results indicate that by deploying IRS and utilizing the proposed algorithms, the system performance can be improved significantly. However, the gain of introducing IRS in the system heavily depends on the bound of the CSI error.

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively, to minimize the objective of system subject to the computation/communication budget and a target latency requirement. In particular, mobile devices are connect via wireless TCP/IP architectures. Exploiting the optimization problem structure, the problem can be decomposed to two convex sub-problems. Drawing on the Lagrangian dual and harmony search techniques, we characterize the global optimal solution by the closed-form solutions to all sub-problems, which give qualitative insights to multi-resource tradeoff. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm.

The space-air-ground (SAG) integrated networks will play a major role in the sixth generation (6G) mobile networks, which will provide global coverage, full connection and pervasive intelligence services for multiple ground Internet of Things (IoT) devices. Moreover, massive computing tasks can be either performed by local devices, or offloaded to edge servers, such as low orbit satellites, high altitude platforms (HAPs) and remote base stations. Nevertheless, the joint computation and communication resource allocation solutions are becoming challenging due to the large-scale state space, time-varying network scenarios, and limited battery capacity. In this paper, we propose a SAG-integrated three-layer heterogenous network model to maximize the sum-rate of ground IoT devices, which further enhances the deep integration of communication and computation resources. Additionally, we develop a Lyapunov-assisted multi-agent proximal policy optimization algorithm to process the task scheduling, HAP selection, battery harvesting, and CPU cycle frequency optimization. Extensive simulation results corroborate that the proposed method has superior performance gains in terms of the remaining battery capacity, energy consumption, and maximum average sum-rate compared with the state-of-the-art baselines.

In fifth generation networks (5G), beamforming technique is widely used to obtain higher system capacity, but it cannot eliminate inter-user interference (IUI) of networks due to excessive number of users. To handle this problem, interference alignment (IA) schemes attract great attention as they can effectively restrain IUI. However, the existing IA schemes cannot achieve antenna adaptation and the obtained degree of freedom (DoF) may be not optimal. In this paper, a novel antenna adaptation based interference elimination and regeneration (AA-IER) scheme is proposed for cooperative networks, where a relay with hybrid antenna array structure is adopted to assist the communication. The proposed transmission process is completed in two phases, including interference elimination phase (IEP) and interference regeneration phase (IRP). For the former, the IUI is eliminated and the redundant symbols are erased so that the received signal of multiple users can be decoded simultaneously. For the latter, the redundant symbols of all users are regenerated where the space resources are fully utilized. The simulation results show that AA-IER scheme obtains higher DoF than that of three benchmark schemes. Meanwhile, it requires fewer antennas of relay than HAA-CIE-RIA scheme.

As a novel method to increase channel capacity of networks, interference alignment (IA) technique has attracted wide attention in fifth-generation wireless networks (5G). However, when it is applied in interference networks, the problem regarding information delay arises which has not been well addressed yet. In this paper, we formally propose the novel concepts of degree of delay (DoD) to quantify the issue of information delay and analyze its determining factors, i.e., delay sensitive factor, queueing delay slot and size of dataset. To reduce DoD, three novel joint IA schemes are proposed for broadcast channel (BC) networks with different amounts of users, i.e., hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme (HAA-IPIE-RIR) and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR). Among the three, the second scheme extends the application scenarios of the first scheme from 2-user to K-user while bringing huge computational complexity burden. The third scheme relieves the burden but it leads to slight degree of freedom (DoF) loss. Overall, the proposed schemes achieve higher DoF and lower DoD than the existing IA schemes.

Leveraging on the wealth of data and advancements in Artificial Intelligence, smart cities have demonstrated their great potential in providing solutions to challenges that the urban population faces today. However, as the urban population becomes more privacy sensitive and with the introduction of stringent privacy regulations, the differential-private FL (DPFL) is a promising technology that can enable privacy-preserving collaborative model training. In this paper, we consider an FL network of model owners and data owners with heterogeneous privacy budgets and preferences respectively. In exchange for their participation in the training, the model owner offers a reward pool that is shared among the data owners that take part in the FL training. In turn, the FL worker with heterogeneous privacy preferences may select the model owner to contribute its parameters to. To model the dynamic and strategic behaviour of the workers in the process of model owner selection, we propose an evolutionary game approach. Then, we conduct simulations to validate the evolutionary equilibrium, as well as provide the sensitivity analyses of the model.

Dubbed as the next-generation Internet, the meta-verse is a virtual world that allows users to interact with each other or objects in real-time using their avatars. The metaverse is envisioned to support novel ecosystems of service provision in an immersive environment brought about by an intersection of the virtual and physical worlds. The native AI systems in metaverse will personalized user experience over time and shape the experience in a scalable, seamless, and synchronous way. However, the metaverse is characterized by diverse resource types amid a highly dynamic demand environment. In this paper, we propose the case study of virtual education in the metaverse and address the unified resource allocation problem amid stochastic user demand. We propose a stochastic optimal resource allocation scheme (SORAS) based on stochastic integer programming with the objective of minimizing the cost of the virtual service provider. The simulation results show that SORAS can minimize the cost of the virtual service provider while accounting for the users’ demands uncertainty.

With the ubiquitous sensing enabled by the Internet-of-Things (IoT), massive amount of data is generated every second, transforming the way we interact with the world. To manage big data and enable analytics at the edge of the network, large amount of computation power is required to perform the computation intensive tasks. However, the energy-constrained IoT devices are not able to perform the computation tasks without compromising the quality-of-service of the applications. In this paper, we propose a hybrid network in which users can offload their computation tasks to edge servers through coded edge offloading or perform local computation with the wireless power transfer derived from coalitions of unmanned aerial vehicles (UAVs) serving as mobile charging stations. We consider a two-level optimization approach where an optimal UAV coalitional structure that minimizes the network cost is formed. In the performance evaluation, we provide extensive sensitivity analyses to study the performance of the cost minimization approach amid varying network parameters.

The Unmanned Aerial Vehicle (UAV) swarm has attracted widespread attention from both academia and industry. It has been widely adopted in disaster recovery, military communication, agricultural production, and industrial automation. In critical situations or places where communication infrastructure is lacking, deploying a UAV swarm network is a cost-effective solution. However, considering the high speed of UAV devices, designing an effective routing mechanism has been a challenging problem. In this paper, enlightened by the recent success of multi-agent reinforcement learning, we propose a multi-agent policy gradients-based UAV routing algorithm. We adopt a centralized training and decentralized executing framework, where a centralized training platform is implemented to guide the policy updating of each UAV node. Moreover, we introduce a counterfactual baseline scheme in our algorithm to improve the convergence speed. Extensive simulation results validate the effectiveness of the proposed algorithms compared to the state-of-the-art schemes.

The Metaverse is regarded as the next-generation Internet paradigm that allows humans to play, work, and socialize in an alternative virtual world with an immersive experience, for instance, via head-mounted displays for Virtual Reality (VR) rendering. With the help of ubiquitous wireless connections and powerful edge computing technologies, VR users in the wireless edge-empowered Metaverse can immerse themselves in the virtual through the access of VR services offered by different providers. However, VR applications are computation- and communication-intensive. The VR service providers (SPs) have to optimize the VR service delivery efficiently and economically given their limited communication and computation resources. An incentive mechanism can be thus applied as an effective tool for managing VR services between providers and users. Therefore, in this paper, we propose a learning-based Incentive Mechanism framework for VR services in the Metaverse. First, we propose the quality of perceptual experience as the metric for VR users immersing in the virtual world. Second, for quick trading of VR services between VR users (i.e., buyers) and VR SPs (i.e., sellers), we design a double Dutch auction mechanism to determine optimal pricing and allocation rules in this market. Third, for auction information exchange cost reduction, we design a deep reinforcement learning-based auctioneer to accelerate this auction process. Experimental results demonstrate that the proposed framework can achieve near-optimal social welfare while reducing at least half of the auction information exchange cost than baseline methods.

In Internet-of-Vehicles (IoV), smart vehicles can efficiently process various sensing data through federated learning (FL) - a privacy-preserving distributed machine learning (ML) approach that allows collaborative development of the shared ML model without any data exchange. However, traditional FL approaches suffer from poor security against the system noise, e.g., due to low-quality trained data, wireless channel errors, and malicious vehicles generating erroneous results, which affects the accuracy of the developed ML model. To address this problem, we propose a novel FL model based on the concept of Lagrange coded computing (LCC) - a coded distributed computing (CDC) scheme that enables enhancing the system security. In particular, we design the first L-CoFL (Lagrange coded FL) model to improve the accuracy of FL computations in the presence of lowquality trained data and wireless channel errors, and guarantee the system security against malicious vehicles. We apply the proposed L-CoFL model to predict the traffic slowness in IoV and verify the superior performance of our model through extensive simulations.

Payment channel network (PCN) is a layer-two scaling solution that enables fast off-chain transactions but does not involve on-chain transaction settlement. PCNs raise new privacy issues including balance secrecy, relationship anonymity and payment privacy. Moreover, protecting privacy causes low transaction success rates. To address this dilemma, we propose zk-PCN, a privacy-preserving payment channel network using zk-SNARKs. We prevent from exposing true balances by setting up public balances instead. Using public balances, zk-PCN can guarantee high transaction success rates and protect PCN privacy with zero-knowledge proofs. Additionally, zk-PCN is compatible with the existing routing algorithms of PCNs. To support such compatibility, we propose zk-IPCN to improve zk-PCN with a novel proof generation (RPG) algorithm. zk-IPCN reduces the overheads of storing channel information and lowers the frequency of generating zero-knowledge proofs. Finally, extensive simulations demonstrate the effectiveness and efficiency of zk-PCN in various settings.

In this paper, we investigate the stability and effectiveness of intelligent reflecting surface (IRS) aided systems in the context of mobile multi-users and time-varying channel status. Different from the previous researches in the IRS-aided communication mostly based on one or more independent channel realization, we consider dynamic channel status varying with the mobility of users. Specifically, a dynamic problem as maximizing the time-average rate of all users is formulated. A fractional programming method based on Lagrangian dual theory is proposed as a solution. Simulation results demonstrate that the IRS can be more efficient than amplified forward (AF) relay in adapting the dynamically changing channels stably.

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

Due to the prominent advantages of decentralization, transparency, security, and traceability, blockchain technologies have attracted ever-increasing attention from academia and industry, which can be applied to establish secure and reliable resource sharing platforms for future networks and applications. Especially, with the promising 6G technology which has large bandwidth and space-air-ground integrated coverage, blockchains have been evolved into 6G-enabled blockchain and envisioned to build various decentralized data and resource management systems. However, for 6G-enabled wireless blockchain networks, there still exist many challenges for their development and prosperity, e.g., large block propagation delay and propagation incentive. Therefore, this paper focuses on addressing the block propagation challenges. Firstly, inspired by epidemic models, we classify consensus nodes into five different states and establish a block propagation model for public blockchains that depicts block propagation laws. Then, considering consensus nodes are limited rational, we propose an Incentive Mechanism based on evolutionary game for Block Propagation (marked as BPIM) to minimize the block propagation delay. Numerical results demonstrate that compared with traditional routing algorithms, BPIM has better block propagation efficiency and greater incentive strength.

International audience

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.

This article investigates the robust secure transmission problem in two-tier B5G heterogeneous networks with multiple noncollusive eavesdroppers and users, where two types of imperfect channel state information (CSI) scenarios, i.e., instantaneous and statistic CSI scenarios, are considered. Given the two-sidedness of co-channel interference in physical-layer security and the selfishness of femtocell base stations (FBSs), an imperfect-CSI-based noncooperative game framework is proposed to maximize the profits of the macro base station (MBS) and FBSs, while guaranteeing user’s Quality-of-Service (QoS) requirement in terms of outage probability. Specifically, based on the involved two CSI scenarios, the original game where the MBS and FBSs act as players is elaborated as two robust game problems. To address channel uncertainties in the objective function, the worst-case and the mean value of the channel gains are used separately. Besides, the remaining channel uncertainties embodied in the intractable outage probability constraints are treated in a unified way, i.e., the extended Bernstein approximation. The existence and uniqueness of the Nash equilibrium (NE) are analyzed, and the sufficient condition on the uniqueness of the NE is derived. Then, two robust iterative algorithms are given to approach the robust game equilibrium. Finally, numerical results are presented to verify the theoretical analysis and show the robustness and effectiveness of the proposed algorithms.

Federated learning (FL) is an emerging technology for empowering various applications that generate large amounts of data in intelligent cyber–physical systems (ICPS). Though FL can address users’ concerns about data privacy, its maintenance still depends on efficient incentive mechanisms. For long-term incentivization to participants in data federation under dynamic environments, deep reinforcement learning as a promising technology has been extensively studied. However, the nonstationary problem caused by the heterogeneity of ICPS devices results in a serious effect on the convergence rate of existing single-agent reinforcement learning. In this article, we propose a multiagent learning-based incentive mechanism to capture the stationarity approximation in FL with heterogeneous ICPS. First, we formulate the secure communication and data resource allocation problem as a Stackelberg game in FL with multiple participants. Then, to tackle the heterogeneous problem, we model this multiagent game as a partially observable Markov decision process. In particular, a multiagent federated reinforcement learning algorithm is proposed to learn the allocation policies efficiently by dwindling variances in policy evaluation caused by interaction among multiple devices without the requirement of sharing privacy information. Moreover, the proposed algorithm is proved to attain convergence at an expected rate. Finally, extensive experimental results demonstrate that our proposed algorithm significantly outperforms baseline approaches.

With the ever-increasing deployment of 5G and IoT, the number of end-hosts/terminals is increasing rapidly, so that routers have to cache more and more forwarding entries to guarantee communication reachability of these terminals, which makes Ternary Content Addressable Memory (TCAM)-based routers keep expanding resource requirements. However, the design and implementation of large-capacity TCAM-based routers are faced with such challenges: difficult circuit design, high production cost and energy consumption, thereby posing an urgent requirement on a lightweight TCAM that can still maintain those massive communication connections. In this paper, we aim to design a lightweight router with small storage requirement while still retaining the original communication connection performance, which is not straightforward due to the following two challenges: First, under the condition of massive sequential flow data, it’s difficult to accurately and timely select the entries to cache for a small capacity TCAM. Second, given the strict prefix matching principle, how to efficiently insert the selected entries into TCAM is also challenging. To address these problems, we propose A&B: an AI-based Routing entry prediction strategy (AIR) and a Block-based entry Insertion Tactic (BIT). AIR can precisely select entries by conducting accurate entry predictions, which converts dynamic flow-based prediction into stable and parallelizable entry-based prediction by decoupling spatio-temporal characteristics. BIT optimizes entry insertion by isolating TCAM into several blocks, thus eliminating the time-consuming entry movements. The experiment results based on real backbone traffic show that our lightweight A&B achieves comparable performance compared to the traditional schemes by using only 1/8 TCAM storage.

Blockchain technology is becoming the cornerstone for the development and deployment of other technologies like Federated Learning (FL) and the Internet of Things (IoT), as it plays a critical role in data sharing and incentives. Blockchains supports decentralization, data-privacy protection, security, and reliability. Assuring secure data sharing in mobile computing and FL is challenging because of untrustworthy participants and unknown data quality. Blockchain provides trust in decentralized environments without requiring trusted third parties. By using smart contracts, blockchain has been able to supporting rich decentralized applications. However, the scalability of blockchain is a challenge that prevents its wide adoption by high-performance applications. To address the blockchain scalability issue, various blockchain sharding technologies and off-chain solutions have been proposed. To improve the network throughput, blockchain sharding divides the entire network into several smaller parallel groups and exploits fast consensus algorithms in blockchain shards. Off-chain solutions, such as payment channel networks (PCNs), transfer the slow on-chain transactions to the off-chain environment, in which transactions can be accelerated. Without consensus and on-chain expensive operations, off-chain scalable solutions significantly reduce transaction costs and increase transaction throughput. This special issue aims to provide a forum for the presentation of state-of-the-art research approaches that advance the construction of intelligent blockchain systems. A total of 27 articles were accepted after a two-round rigorous review process. Based on their topics, we have grouped the accepted articles into four categories: blockchain-based federated learning systems, blockchain and the IoT, blockchain scalability, and high-performance blockchains. In what follows, we introduce these articles and their contributions.

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. While FL ensures the privacy of the FL workers, its performance is limited by several bottlenecks, which become significant given the increasing amounts of data generated and the size of the FL network. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In CFL, the FL server helps to compute a subset of the partial gradients based on the composite parity data and aggregates the computed partial gradients with those received from the FL workers. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.

For several industrial applications, a sole data owner may lack sufficient training samples to train effective machine learning based models. As such, we propose a federated learning (FL) based approach to promote privacy-preserving collaborative machine learning for applications in smart industries. In our system model, a model owner initiates an FL task involving a group of workers, i.e., data owners, to perform model training on their locally stored data before transmitting the model updates for aggregation. There exists a tradeoff between service latency, i.e., the time taken for the training request to be completed, and age of information (AoI), i.e., the time elapsed between data aggregation from the deployed industrial Internet of Things devices to completion of the FL-based training. On one hand, if the data are collected only upon the model owner's request, the AoI is low. On the other hand, the service latency incurred is more significant. Furthermore, given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner toward AoI and service latency. The performance evaluation validates the incentive compatibility of our contract amid information asymmetry, and shows the flexibility of our proposed scheme toward satisfying varying preferences of AoI and service latency.

The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT). In maritime IoT, the spatio-temporal vessel trajectories, collected from the hybrid satellite-terrestrial automatic identification system (AIS) base stations, are of considerable importance for promoting traffic situation awareness and vessel traffic services, etc. To guarantee traffic safety and efficiency, it is essential to robustly and accurately predict the AIS-based vessel trajectories (i.e., the future positions of vessels) in maritime IoT. In this work, we propose a spatio-temporal multigraph convolutional network (STMGCN)-based trajectory prediction framework using the mobile edge computing (MEC) paradigm. Our STMGCN is mainly composed of three different graphs, which are, respectively, reconstructed according to the social force, the time to closest point of approach, and the size of surrounding vessels. These three graphs are then jointly embedded into the prediction framework by introducing the spatio-temporal multigraph convolutional layer. To further enhance the prediction performance, the self-attention temporal convolutional layer is proposed to further optimize STMGCN with fewer parameters. Owing to the high interpretability and powerful learning ability, STMGCN is able to achieve superior prediction performance in terms of both accuracy and robustness. The reliable prediction results are potentially beneficial for traffic safety management and intelligent vehicle navigation in MEC-enabled maritime IoT.

Large interactive displays are widely used in industrial scenarios to enhance ubiquitous and seamless human–machine interactions. However, few studies have paid attention to design implicit interaction that users can directly manipulate physical circumstance without touch or specific gesture. This article proposes ReflectU, a novel reflection-based approach that leverages mirror reflection for a natural and implicit interactive method for remote control, i.e., user will be able to directly interact with physical circumstance just via the reflection of their bare hands. We compare its performance with that of other two generally known devices: Wii Remoter and Microsoft Kinect. Moreover, performance metrics of ReflectU are evaluated in real-life scenarios and provide evidence in convincing performance in both the tasks requiring instant targeting and trajectory control. Furthermore, ReflectU is reported by users to be the most intuitive and satisfactory approach among all three candidates in user studies. Future industrial applications of the reflection-base mirror approach are discussed.

To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this article, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners' participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete amongst each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.

Amid growing concerns on data privacy, Federated Learning (FL) has emerged as a promising privacy preserving distributed machine learning paradigm. Given that the FL network is expected to be implemented at scale, several studies have proposed system architectures towards improving the network scalability and efficiency. Specifically, the Hierarchical FL (HFL) network utilizes cluster heads, e.g., base stations, for the intermediate aggregation and relay of model parameters. Serverless FL is also proposed recently, in which the data owners, i.e., workers, exchange the local model parameters among a neighborhood of workers. This decentralized approach reduces the risk of a single point of failure but inevitably incurs significant communication overheads. To achieve the best of both worlds, we propose the Serverless Hierarchical Federated Learning (SHFL) framework in this paper. The SHFL framework adopts a two-layer system architecture. In the lower layer, the FL workers are grouped into clusters under cluster heads. In the upper layer, the cluster heads exchange the intermediate parameters with their one-hop neighbors without the aid of a central server. To improve the sustainable efficiency of the FL system while taking into account the incentive design for workers marginal contributions in the system, we propose the reputation-aware hedonic coalition formation game in this paper. Specifically, the workers are rewarded for their marginal contribution to the cluster, whereas the reputation opinions of each cluster head is updated in a decentralized manner, thereby deterring malicious behaviors by the cluster head. This improves the performance of the network since cluster heads with higher reputation scores are more reliable in relaying the intermediate model parameters. The simulation results show that our proposed hedonic coalition formation algorithm converges to a Nash-stable partition and improves the network efficiency.

After several decades of development on cyber security techniques, one clear conclusion can be drawn: no cyber security solution can completely remove the risks faced by the users. In this regard, cyber-insurance has been introduced as a means to enable the users to alleviate the damage from the cyber threats by transferring the cyber risks to an insurer. In this article, we study a cloud security service market, which is composed of cloud users and cloud security service vendors (CSSVs). The CSSVs work as the insurers for selling the cloud security plan, which is consisted of cloud security service and cloud-insurance. The users in the cloud platform can purchase the cloud security plan from the CSSVs to secure their cloud service. If the cloud service is attacked and loss happens, the users will receive the claim from the CSSVs. To lower the successful attack probability, the CSSV has an incentive to invest in improving its cloud security service. Specifically, we model and study the cloud security service market in the framework of a two-stage Stackelberg game. On the upper stage, the CSSVs lead to decide on their own strategies, i.e., the price of the cloud security plan and the security investment to improve their offered cloud security service. On the lower stage, the users follow to decide on the purchase of the cloud security plan according to the price of the cloud security plan and the perceived cyber breach probability of the cloud security service. We analytically verify that the Stackelberg equilibrium exists and is unique. Extensive simulations have been conducted to evaluate the performance of the Stackelberg game. The performance evaluation shows some insightful results. For example, when the users have strong interdependency, the profits of the CSSVs become lower.

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.

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.

The COVID-19 pandemic is one of the most severe challenges the world faces today. In order to contain the transmission of COVID-19, people around the world have been advised to practise social distancing. However, maintaining social distance is a challenging problem, as we often do not know beforehand how crowded the places we intend to visit are. In this paper, we demonstrate crowded.sg, an AI-empowered platform that leverages on Unmanned Aerial Vehicles (UAVs), crowdsourced images, and computer vision techniques to provide social distancing decision support.

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.

Today, modern unmanned aerial vehicles (UAVs) are equipped with increasingly advanced capabilities that can run applications enabled by machine learning techniques, which require computationally intensive operations such as matrix multiplications. Due to computation constraints, the UAVscan offload their computation tasks to edge servers. To mitigate stragglers, coded distributed computing (CDC) based offloading can be adopted. In this paper, we propose an Optimal Task Allocation Scheme (OTAS) based on Stochastic Integer Programming with the objective to minimize energy consumption during computation offloading. The simulation results show that amid uncertainty of task completion, the energy consumption in the UAV network is minimized.

With the advent of the Internet of Everything era, communication data has exploded, which requires more communication resources, such as frequency, time, and energy. In this context, this paper presents a machine learning-based data packet scheduling scheme to achieve efficient data packet transmission in the 5G/6G communication systems. To minimize the average number of packet overflows (APNO), we propose distributed deep deterministic policy gradient (DDPG)-based algorithm for multidimensional resource scheduling. To improve the algorithm stability and training efficiency, the strategy of centralized training and distributed execution is adopted, and an Action Adjuster is designed. The proposed algorithm enables the multidimensional resource management of the 5G/6G commu-nication systems without any information interaction between each agent. Simulation results show that the proposed Action Adjuster DDPG algorithm achieves faster convergence and less data overflow compared to other benchmark algorithms.

This paper investigates the long-term effectiveness and stability of an integrated unmanned aerial vehicles (UAV)-intelligent reflecting surface (IRS) relaying dynamic system in the context of time-varying system states. Consequently, a dynamic optimization problem is constructed to minimize the frame-average transmit power by joint active beamforming at the base station (BS) and passive beamforming at the IRS under frame-average rate constraints. The original problem as an infinite-horizon time-average one can be solved by introducing the drift-plus-penalty (DPP) algorithm and then the optimal active beamforming and passive beamforming can be obtained in an iterative manner. Simulation results demonstrate the theoretical analysis and assess the performance of the dynamic system.

As the amount of data collected for crowdsensing applications increases rapidly due to improved sensing capabilities and the increasing number of Internet of Things (IoT) devices, the cloud server is no longer able to handle the large-scale datasets individually. Given the improved computational capabilities of the edge devices, coded distributed computing has become a promising approach given that it allows computation tasks to be carried out in a distributed manner while mitigating straggler effects, which often account for the long overall completion times. Specifically, by using polynomial codes, computed results from only a subset of devices are needed to reconstruct the final result. However, there is no incentive for the edge devices to complete the computation tasks. In this paper, we present an all-pay auction to incentivize the edge devices to participate in the coded computation tasks. In this auction, the bids of the edge devices are represented by the allocation of their Central Processing Unit (CPU) power to the computation tasks. All edge devices submit their bids regardless of whether they win or lose in the auction. The all-pay auction is designed to maximize the utility of the cloud server by determining the reward allocation to the winners. Simulation results show that the edge devices are incentivized to allocate more CPU power when multiple rewards are offered instead of a single reward.

The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission.

Federated Edge Learning (FEL) is a distributed Machine Learning (ML) framework for collaborative training on edge devices. FEL improves data privacy over traditional centralized ML model training by keeping data on the devices and only sending local model updates to a central coordinator for aggregation. However, challenges still remain in existing FEL architectures where there is high communication overhead between edge devices and the coordinator. In this paper, we present a working prototype of blockchain-empowered and communication-efficient FEL framework, which enhances the security and scalability towards large-scale implementation of FEL.

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose the edge association strategies of the workers to be modelled using an evolutionary game. Then, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.

As the paradigm of crowdsensing involves the data collection from users, the issue of designing reward to incentivize the users is fundamentally important to be addressed, thereby effectively enhancing the participation. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in crowdsensing-based healthcare services, the accuracy of diet recommendation for a certain user can be promoted by exploiting the nutritional information contributed and shared by the socially-connected friends of him/her taking similar types of food. To be more general and practical, we study the incentive schemes in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive schemes. Considering this, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically validate the existence and uniqueness of the Stackelberg equilibrium. Simulations are conducted to evaluate game equilibrium properties, and the results are presented to assess and demonstrate the performance effectiveness of the proposed game model.

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.

Currently, the data collected by the Internet of Healthcare Things, i.e., healthcare oriented Internet of Things (IoT), still rely on cloud-based centralized data aggregation and processing. To reduce the need for transmission of data to the cloud, the edge computing architecture may be adopted to facilitate machine learning at the edge of the network through leveraging on the amassed computation resources of pervasive IoT devices. In this article, federated learning (FL) is proposed to enable privacy-preserving collaborative model training at the edge of the network across distributed IoT users. However, the users in the FL network may have different willingness to participate (WTP), a hidden information unknown to the model owner. Furthermore, the development of healthcare applications typically requires sustainable user participation, e.g., for the continuous collection of data during which a user’s WTP may change over time. As such, we leverage on the dynamic contract design to consider a two-period incentive mechanism that satisfies the intertemporal incentive compatibility (IIC), such that the self-revealing mechanism of the contract holds across both periods. The performance evaluation shows that our contract design satisfies the IIC constraints and derives greater profits than that of the uniform pricing scheme, thus validating its effectiveness in mitigating the adverse impacts of the information asymmetry.

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming increasingly important. Furthermore, data collected by the edge device contain massive user's private data, which is challenging current detection approaches as user privacy has attracted more and more public concerns. With this focus, this article proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce an FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an attention mechanism-based convolutional neural network-long short-term memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based convolutional neural network units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of the long short-term memory unit in predicting time-series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top- k selection to improve communication efficiency. Extensive experimental studies on four real-world data sets demonstrate that our framework accurately and timely detects anomalies and also reduces the communication overhead by 50% compared to the FL framework that does not use the gradient compression scheme.

Future generation communication systems, such as 5G and 6G wireless systems, exploit the combined satellite-terrestrial communication infrastructures to extend network coverage and data throughput for data-driven applications. These ground-breaking techniques have promoted the rapid development of Internet of Things (IoT) in maritime industries. In maritime IoT applications, intelligent vessel traffic services can be guaranteed by collecting and analyzing high volume of spatial data flows from automatic identification system (AIS). This AIS system includes a highly integrated automatic equipment, including functionalities of core communication, tracking, and sensing. The increased utilization of shipboard AIS devices allows the collection of massive trajectory data. However, the received raw AIS data often suffers from undesirable outliers (i.e., poorly tracked timestamped points for vessel trajectories) during signal acquisition and analog-to-digital conversion. The degraded AIS data will bring negative effects on vessel traffic services (e.g., maritime traffic monitoring, intelligent maritime navigation, vessel collision avoidance, etc.) in maritime IoT scenarios. To improve the quality of vessel trajectory records from AIS networks, we propose to develop a two-phase data-driven machine learning framework for vessel trajectory reconstruction. In particular, a density-based clustering method is introduced in the first phase to automatically recognize the undesirable outliers. The second phase proposes a bidirectional long short-term memory (BLSTM)-based supervised learning technique to restore the timestamped points degraded by random outliers in vessel trajectories. Comprehensive experiments on simulated and realistic data sets have verified the dominance of our two-phase vessel reconstruction framework compared to other competing methods. It thus has the capacity of promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems.

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association and resource allocation in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose a hierarchical game framework to study the dynamics of edge association and resource allocation in self-organizing HFL networks. In the lower-level game, the edge association strategies of the workers are modelled using an evolutionary game. In the upper-level game, a Stackelberg differential game is adopted in which the model owner decides an optimal reward scheme given the expected bandwidth allocation control strategy of the edge server. Finally, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.

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.

Fog computing has gained tremendous popularity due to its capability of addressing the surging demand on high-quality ubiquitous mobile services. Nevertheless, the highly virtualized environment in fog computing leads to vulnerability to cyber attacks such as advanced persistent threats. In this paper, we propose a novel game approach of cyber risk management for the fog computing platform. We adopt the cyber-insurance concept to transfer cyber risks from fog computing platform to a third party. The system model under consideration consists of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform which is composed of multiple fog nodes. Meanwhile, the attacker dynamically adjusts the allocation of its attack computing resources to increase the probability of successful attack. Additionally, to prevent from the potential loss due to the attacks, the provider also makes a dynamic decision on the subscription of cyber-insurance for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. To model this dynamic interactive decision making problem, we formulate a dynamic Stackelberg game. In the lower-level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper-level, the cyber-insurer optimizes its premium strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable, and we investigate the Stackelberg equilibrium by capitalizing on tools from the optimal control theory. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.

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.

Effective screening of COVID-19 cases has been becoming extremely important to mitigate and stop the quick spread of the disease during the current period of COVID-19 pandemic worldwide. In this article, we consider radiology examination of using chest X-ray images, which is among the effective screening approaches for COVID-19 case detection. Given deep learning is an effective tool and framework for image analysis, there have been lots of studies for COVID-19 case detection by training deep learning models with X-ray images. Although some of them report good prediction results, their proposed deep learning models might suffer from overfitting, high variance, and generalization errors caused by noise and a limited number of datasets. Considering ensemble learning can overcome the shortcomings of deep learning by making predictions with multiple models instead of a single model, we proposeEDL-COVID, an ensemble deep learning model employing deep learning and ensemble learning. The EDL-COVID model is generated by combining multiple snapshot models of COVID-Net, which has pioneered in an open-sourced COVID-19 case detection method with deep neural network processed chest X-ray images, by employing a proposed weighted averaging ensembling method that is aware of different sensitivities of deep learning models on different classes types. Experimental results show that EDL-COVID offers promising results for COVID-19 case detection with an accuracy of 95%, better than COVID-Net of 93.3%.

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owners, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing, computation, and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design, and shows the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction-coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied to solve the allocation of UAV coalitions. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer to support the IoV components independently and not to form any coalition.

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.

Open-access blockchains based on proof-of-work protocols have gained tremendous popularity for their capabilities of providing decentralized tamper-proof ledgers and platforms for data-driven autonomous organization. Nevertheless, the proof-of-work based consensus protocols are vulnerable to cyber-attacks such as double-spending. In this paper, we propose a novel approach of cyber risk management for blockchain-based service. In particular, we adopt the cyber-insurance as an economic tool for neutralizing cyber risks due to attacks in blockchain networks. We consider a blockchain service market, which is composed of the infrastructure provider, the blockchain provider, the cyber-insurer, and the users. The blockchain provider purchases from the infrastructure provider, e.g., a cloud, the computing resources to maintain the blockchain consensus, and then offers blockchain services to the users. The blockchain provider strategizes its investment in the infrastructure and the service price charged to the users, in order to improve the security of the blockchain and thus optimize its profit. Meanwhile, the blockchain provider also purchases a cyber-insurance from the cyber-insurer to protect itself from the potential damage due to the attacks. In return, the cyber-insurer adjusts the insurance premium according to the perceived risk level of the blockchain service. Based on the assumption of rationality for the market entities, we model the interaction among the blockchain provider, the users, and the cyber-insurer as a two-level Stackelberg game. Namely, the blockchain provider and the cyber-insurer lead to set their pricing/investment strategies, and then the users follow to determine their demand of the blockchain service. Specifically, we consider the scenario of double-spending attacks and provide a series of analytical results about the Stackelberg equilibrium in the market game.

The mobile crowdsensing paradigm facilitates a broad range of emerging sensing applications by leveraging ubiquitous mobile users to cooperatively perform certain sensing tasks with their smart devices. As this paradigm involves data collection from users, the issue of designing rewards to incentivize users is fundamentally important to ensure participation in crowdsensing. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in healthcare-based crowdsensing services, the fun of tracking daily nutrition information for a certain user can be promoted by comparing her nutritional information with that contributed and shared by her socially-connected friends. To be more general and practical, we study the incentive mechanisms in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive mechanisms. With this focus, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically prove the existence and uniqueness of the Stackelberg equilibrium. We conduct extensive simulations to investigate game equilibrium properties, and the real-world dataset is applied to evaluate and demonstrate the performance effectiveness of the proposed game model.

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.

Federated edge learning has emerged as a promising technique to enable distributed machine learning using local datasets from large-scale edge devices, e.g., mobile phones or parked vehicles, that share only model updates without uploading raw training data. This technique not only preserves data privacy of edge devices but also simultaneously ensures high learning performance. However, the emerging federated edge learning still confronts serious challenges, such as the lack of efficient training task assignment schemes with reliable edge devices acting as workers. To address this challenge, we utilize a many-to-one matching model to solve the training task assignment problem between the workers and multiple task publishers. In the matching model, we minimize not only the overall training time of the task publishers but also the energy consumption of the workers. To define against malicious model updates from unreliable workers, we present reputation as a metric to evaluate the reliability and trustworthiness of the edge devices, and also take the reputation into consideration when assigning training tasks. The numerical results indicate that the proposed schemes can efficiently improve the performance of federated edge learning.

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.

In social and economic fields, the peer effect and its influence gradually attract public attention. In this paper, we explore the interactions between a load-serving entity and a group of households in a smart grid community and put forward a peer effect-based demand response (PEDR) scheme applying dynamic pricing. A two-stage Stackelberg game based framework is established in which the electricity price and consumption decisions are derived adopting backward induction. We obtain the closed-form solution of the game (i.e., the equilibrium) in each stage and prove its existence and uniqueness. Simulation results indicate that the PEDR scheme shows superiority in energy consumption and peak to average ratio (PAR) compared with the baseline scheme without considering peer effects. Additionally, we study the impacts of social network structure of users and show that by setting the central node to be a frugal consumer in star topology structure, the performance of PEDR can be further improved. Such evaluations, as we believe, shall provide useful insights for energy providers to devise rational demand response policies.

Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), is increasingly popular in the Internet of Vehicles (IoV) applications in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning for the development of IoV applications, e.g., for traffic prediction and car park occupancy management. Given the information asymmetry and incentive mismatches between the UAVs and model owner, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing and transmission costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design and shows the efficiency of our matching.

With the rapid development of the Industrial Internet of Things (IIoT), various IoT devices and sensors generate massive industrial sensing data. Sensing big data can be analyzed for insights that lead to better decisions and strategic industrial production by using advanced machine learning technologies. However, vulnerable IoT devices are easy to be compromised thus causing IoT devices failures (i.e., anomalies). The anomalies seriously affect the production of industrial products, thereby, it is increasingly important to accurately and timely detect anomalies. To this end, we first introduce a Federated Learning (FL) framework to enable decentralized edge devices to collaboratively train a Deep Anomaly Detection (DAD) model, which can improve its generalization ability. Second, we propose a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) model to accurately detect anomalies. The CNN-LSTM model uses CNN units to capture fine-grained features and retains the advantages of LSTM unit in predicting time series data. Third, to achieve real-time and lightweight anomaly detection in the proposed framework, a gradient compression mechanism is applied to reduce communication costs and improve communication efficiency. Extensive experiment results based on realworld datasets demonstrate that the proposed framework and mechanism can accurately and timely detect anomalies, and also reduce about 50% communication overhead when compared with traditional schemes.

Load balancing is a difficult online decision-making problem in the current network. Recently, with the development of the programmable data-plane, it is feasible to perform flexibly load balance directly inside the network. This in-network load balance scheme can quickly adapt to the volatility of network traffic. However, previous in-network solutions are largely relying on the manual process. Inspired by recent successes in applying machine learning in online control, automating the in-network load balance process is thus appealing. But as a distributed control system, it behooves us to ask the critical question: “Can the distributed switches learn globally optimal scheduling policy and still be deployed in a distributed fashion to allow rapid reaction in real-time?” To tackle this question, we adopt a centralized learning and distributed execution framework and propose a multi-agent actor-critic reinforcement learning algorithm in this paper. The centralized “critic” is reinforced with the global network state and joint actions of all agents to ease the training process whilst distributed switches can take actions relaying on their local observations. In addition, a baseline scheme is introduced to solve the credit assignment problem in the multi-agent system. The extensive simulations are conducted to evaluate our proposed algorithm in comparison to state-of-the-art schemes.

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning. However, the performance of the FL suffers from the failure of communication links and missing nodes. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework. The joint auctioncoalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profitmaximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer not to form any coalition.

The revolutions of computing and communication have opened up demands for the high quality of service (QoS), such as high data transmission, high reliability, and low latency. These new opportunities have spawned numerous studies on edge computing and artificial intelligence (AI), even the cooperation between them, referred to as edge intelligence. However, there are a number of handicaps that prevent edge intelligence from being used as a generic platform. The most intractable one is the heterogeneity and un-credibility among edges, hindering the way of sharing the learning results reliably, flexibly, and efficiently. In this paper, we propose a blockchain-assisted edge intelligence (B-EI) approach to solve the problem. The edge learning nodes train their local intelligence, followed by the improved blockchain to share the local intelligence, constructing edge intelligence among the heterogeneous and uncredible edges. Specifically, the improved blockchain employs a novel learning-measured consensus protocol, named Proof of Learning. The edges, also acted as the blockchain nodes, compete to have more superior local intelligence, instead of solving a hashed result. The superior local intelligence is then shared and distributed with other edges. It is not only beneficial to achieve edge intelligence, but also efficient to employ the computation resource, by replacing the hashing as the intelligence training. In order to show the potential benefits, we then use the proposed B-EI approach to solve a joint resource assignment problem. Simulation results show that our scheme outperforms the other state-of-art solutions, in terms of training episodes, and resource utility.

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.

One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm called Federated Learning (FL). However, communication inefficiency remains a key bottleneck in FL. To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this paper, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange of the data owners' participation, and the data owners are free to choose among any clusters to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, given that each cluster head can choose to serve a model owner, the model owners have to compete for the services of the cluster head. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head's services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing property of the deep learning based auction.

In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.

Mobile data rewards is now leading a new economic trend in wireless networks, where the operators stimulate mobile users to view ads with data rewards and ask for corresponding payments from advertisers. Yet, due to the uncertain nature of users' preferences, it is always challenging for the advertiser to find the best choice of data rewards to attain an optimum balance between ad revenue and rewards spent. In this paper, we develop a general contract-theoretic framework to address the problem of data rewards design in a realistic asymmetric information scenario, where each user is associated with multidimensional private information. Specifically, we model the interplay between the advertiser and users by using a multidimensional contract design approach, and theoretically analyze optimal data rewarding schemes. To ensure global incentive compatibility, we convert the multi-dimensional contract problem into an equivalent one-dimensional contract problem. Necessary and sufficient conditions for an optimal and feasible contract are then derived to provide incentives for engagement of users in data rewarding scheme. We leverage numerical results to evaluate the performance of the designed multi-dimensional contract for data rewarding scheme.

In recent years, the enhanced sensing and computation capabilities of Internet-of-Things (IoT) devices have opened the doors to several mobile crowdsensing applications. In mobile crowdsensing, a model owner announces a sensing task following which interested workers collect the required data. However, in some cases, a model owner may have insufficient data samples to build an effective machine learning model. To this end, we propose a federated learning (FL)-based privacy-preserving approach to facilitate collaborative machine learning among multiple model owners in mobile crowdsensing. Our system model allows collaborative machine learning without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in the contract theory under information asymmetry. For the latter, to ensure the stability of a federation through preventing free-riding attacks, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. Considering the inherent hierarchical structure of the involved entities, we propose a hierarchical incentive mechanism framework. Using the backward induction, we first solve the contract formulation and then proceed to solve the coalitional game with the merge and split algorithm. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design, in terms of incentive compatibility of our contract design and fair payoffs of model owners in stable federation formation.

A sponsored content policy enables a content provider (CP) to pay a network service provider (SP), and thereby mobile users (MUs) can access contents from the CP through network services from the SP with a lower charge. Thus, more users want to access the contents which potentially generates more profit gain to the CP. In this article, we study the interactions among three entities under the sponsored content policy, namely, the network SP, which is referred to as SP for brevity, the CP and MUs. We model the interactions as a hierarchical Stackelberg game, where the SP and the CP act as the leaders determining the pricing and sponsoring strategies, respectively, and the MUs act as the followers deciding on their content demand. The model incorporates the network effects in a social domain and congestion in a network domain which enables us to obtain insights from the sponsored content policy. In the model, we investigate the mutual interplay between the SP and the CP in three scenarios: 1) sequential competition, where the SP first optimizes its pricing strategy for maximizing its revenue, and then the CP optimizes its sponsoring strategy for maximizing its profit sequentially; 2) simultaneous competition, where the CP and the SP optimize their individual strategies separately and simultaneously; and 3) cooperation, where both providers jointly optimize their strategies with the purpose of maximizing their aggregate payoff. Through backward induction, we derive the unique Nash equilibrium among the MUs. Furthermore, the existence and uniqueness of the Stackelberg equilibrium under three proposed scenarios are validated analytically. Via extensive simulations, it is shown that the network effects significantly improve the utilities of MUs, the profit of the CP, and the revenue of the SP.

The Internet of Things (IoT) connects mobile and wireless devices, and enables the IoT service providers to deliver IoT services to the mobile users in various applications, e.g., transportation and communications. In this paper, the problem of IoT service delivery management is studied with the consideration of substitutability, complementarity, and externalities of delivering IoT services due to the diversity of different IoT components in mobile systems. The substitutable IoT services have similar functionalities to serve IoT users, and the IoT users can switch to buy service from any IoT service provider. The complementary IoT services have different functionalities to serve IoT users, and the IoT users may request a bundle of IoT services from multiple IoT service providers as their IoT services can be integrated. Externalities represent the situation in which IoT users in the same system can affect the utilities of each other due to the connections and interference among the IoT users, which leads to the presence of network effect and congestion effect. To analyze the impact of these factors on the performance of IoT systems, a multi-leader multi-follower Stackelberg game model is introduced. Therein, the IoT service providers and IoT users make their strategic decisions in terms of pricing and service requests, respectively, toward their individual objectives in a distributed manner. A closed-form equilibrium solution is derived analytically through backward induction.

The mining process in public blockchains with the Nakamoto consensus protocol requires solving a computational puzzle, i.e., proof-of-work, which is resource expensive to implement in lightweight devices with limited computing resources and energy. Thus, renting mining service from cloud providers becomes a reasonable solution, which is called cloud mining. This enables users who want to mine, i.e., miners, to purchase and lease an amount of hashing power from the cloud/edge providers without any hassle of managing the infrastructure. In this paper, we study the interactions among the cloud/edge providers and miners in blockchain using a multi-leader multi-follower game-theoretic approach, in order to support proof-of-work based blockchains application. Due to the inherent complexity of the formulated game, we employ the Alternating Direction Method of Multipliers (ADMM) algorithm to investigate the optimum solution. Utilizing the decomposition characteristics and fast convergence of ADMM, we obtain the optimum results in a distributed manner. Simulation results demonstrate that with the proposed solutions, the optimization of the utilities of miners and the profits of providers can be jointly achieved.

Data rewarding is a novel business model leading a new economic trend in mobile networks, in which the operators stimulate mobile users to watch ads with data rewards and ask for corresponding payments from advertisers. Yet, due to the uncertain nature of users' preferences, it is always challenging for the advertiser to find the best choice of data rewards to attain an optimum balance between ad revenue and rewards spent. In this paper, we build a general contract-theoretic framework to address the problem of data rewards design in a realistic asymmetric information scenario, where each user is associated with multi-dimensional private information, i.e., data valuation, ad valuation, and ad sensitivity. In particular, we model the interplay between the advertiser and users by using a multi-dimensional contract approach, and theoretically analyze optimal data rewarding schemes. To ensure global incentive compatibility, we utilize the structural properties of our contract problem and convert the multi-dimensional contract into an equivalent one-dimensional contract. Necessary and sufficient conditions for an optimal and feasible contract are then derived to provide incentives for engagement of users in data rewarding scheme. Extensive numerical evaluations validate the efficiency of the designed multi-dimensional contract for data rewarding compared to other benchmark schemes.

Mobile data demand is increasing tremendously in wireless social networks, and thus an efficient pricing scheme for social-enabled services is urgently needed. Though static pricing is dominant in the actual data market, price intuitively ought to be dynamically changed to yield greater revenue. The critical question is how to design the optimal dynamic pricing scheme, with prospects for maximizing the expected long-term revenue. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile network operator in the social data market. In the market, the operator, i.e., the seller, individually offers each mobile user, i.e., the buyer, a certain price in multiple time periods sequentially and repeatedly. The proposed scheme exploits the network effects in the mobile users' behaviors that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Thereafter, we propose a modified sequential pricing policy in order to ensure social fairness among mobile users in terms of their individual utilities. To gain more insights, we further study a simultaneous dynamic pricing scheme in which the operator offers the data price simultaneously. We analytically demonstrate that the proposed dynamic pricing scheme can help the operator gain greater revenue and users achieve higher total utilities than those of the baseline static pricing scheme. We construct the social graph using Erdös-Rényi (ER) model and the real dataset based social network for performance evaluation. The numerical results corroborate that the dynamics of pricing schemes over static ones can significantly improve the revenue of the operator.

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.

The burgeoning vehicular networks generate a huge amount of sensing data. Data sharing among vehicles enables a number of valuable vehicular applications to improve driving safety and enhance vehicular services. To ensure security and traceability of data sharing, an efficient Delegated Proof-of-Stake (DPoS) consensus algorithm is utilized to establish Blockchain-Enabled VEhicular Networks (BEVENs). Miners in DPoS include active miners and standby miners. The active miners are responsible for block generation and block verification. However, due to the limited number of active miners in DPoS, the compromised active miners may collude with each other to generate maliciously manipulated results of block verification. To prevent the internal collusion among the active miners, a newly generated block can be further verified and audited by the standby miners. To incentivize the participation of the miners in block verification, we adopt the contract theory to model the interactions between active miners and standby miners, where both block verification security and delay are taken into consideration. Numerical results demonstrate the security and efficiency of our schemes for data sharing in BEVENs.

Federated learning is an emerging machine learning technique that enables distributed model training using local datasets from large-scale nodes, e.g., mobile devices, but shares only model updates without uploading the raw training data. This technique provides a promising privacy preservation for mobile devices while simultaneously ensuring high learning performance. The majority of existing work has focused on designing advanced learning algorithms with an aim to achieve better learning performance. However, the challenges, such as incentive mechanisms for participating in training and worker (i.e., mobile devices) selection schemes for reliable federated learning, have not been explored yet. These challenges have hindered the widespread adoption of federated learning. To address the above challenges, in this article, we first introduce reputation as the metric to measure the reliability and trustworthiness of the mobile devices. We then design a reputation-based worker selection scheme for reliable federated learning by using a multiweight subjective logic model. We also leverage the blockchain to achieve secure reputation management for workers with nonrepudiation and tamper-resistance properties in a decentralized manner. Moreover, we propose an effective incentive mechanism combining reputation with contract theory to motivate high-reputation mobile devices with high-quality data to participate in model learning. Numerical results clearly indicate that the proposed schemes are efficient for reliable federated learning in terms of significantly improving the learning accuracy.

Public blockchain networks using proof of work (PoW)-based consensus protocols are considered as a promising platform for decentralized resource management with financial incentive mechanisms. In order to maintain a secured, universal state of the blockchain, PoW-based consensus protocols financially incentivize the nodes in the network to compete for the privilege of block generation through cryptographic puzzle solving. For rational consensus nodes, i.e., miners with limited local computational resources, offloading the computation load for PoW to the cloud/fog providers (CFPs) becomes a viable option. In this paper, we study the interaction between the CFPs and the miners in a PoW-based blockchain network using a game theoretic approach. In particular, we propose a lightweight infrastructure of the PoW-based blockchains, where the computation-intensive part of the consensus process is offloaded to the cloud/fog. We formulate the computation resource management in the blockchain consensus process as a two-stage Stackelberg game, where the profit of the CFP and the utilities of the individual miners are jointly optimized. In the first stage of the game, the CFP sets the price of offered computing resource. In the second stage, the miners decide on the amount of service to purchase accordingly. We apply backward induction to analyze the subgame perfect equilibria in each stage for both uniform and discriminatory pricing schemes. For uniform pricing where the same price applies to all miners, the uniqueness of the Stackelberg equilibrium is validated by identifying the best response strategies of the miners. For discriminatory pricing where the different prices are applied, the uniqueness of the Stackelberg equilibrium is proved by capitalizing on the variational inequality theory. Further, the real experimental results are employed to justify our proposed model.

In sponsored content and service markets, the content and service providers are able to subsidize their target mobile users through directly paying the mobile network operator to lower the price of the data/service access charged by the network operator to the mobile users. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the content/service providers. In this paper, we study the interactions among the three parties in the market, namely, the mobile users, the content/service providers, and the network operator, as a two-level game with multiple Stackelberg (i.e., leader) players. Our study is featured by the consideration of global network effects owning to consumers' grouping. Since the mobile users may have bounded rationality, we model the service-selection process among them as an evolutionary-population follower sub-game. Meanwhile, we model the pricing-then-sponsoring process between the content/service providers and the network operator as a non-cooperative equilibrium searching problem. By investigating the structure of the proposed game, we reveal a few important properties regarding the equilibrium existence and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence of the proposed algorithm and demonstrate how sponsoring helps improve both the providers' profits and the users' experience.

Mobile crowdsensing has shown great potential in addressing large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive a satisfactory reward. In this paper, to effectively and efficiently recruit a sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium is analytically validated by identifying the best response strategies of the mobile users. The numerical results corroborate the fact that the network effects significantly stimulate a higher mobile participation level and greater revenue for the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider achieve greater revenue gain.

In a sponsored content scheme, a wireless network operator negotiates with a sponsored content service provider where the latter can pay the former to lower the cost of the mobile subscribers/users to access certain content. As such, the scheme motivates the entities in the sponsored content ecosystem to be more actively involved. Meanwhile, with the forthcoming 5G cellular networks, edge caching becomes a promising technology for traffic offloading to reduce cost and improve service quality of the content service. The key idea is that an edge caching content service provider caches content on edge networks. The cached content is then delivered to mobile users locally, reducing latency substantially. In this paper, we propose the joint sponsored and edge caching content service market model. We investigate an interplay between the sponsored content service provider and the edge caching content service provider under the non-cooperative game framework. Furthermore, the interactions among the wireless network operator, content service providers, and mobile users are modeled as a hierarchical three-stage Stackelberg game. In the game model, we analyze the sub-game perfect equilibrium in each stage through backward induction analytically. Additionally, the existence of the proposed Stackelberg equilibrium is validated by capitalizing on the bilevel optimization programming. Based on the analysis of the game properties, we propose a sub-gradient-based iterative algorithm, which guarantees to converge to the Stackelberg equilibrium.

Benefit from the capabilities of providing decentralized tamper-proof ledgers and platforms for data-driven autonomous organization, open-access blockchains based on proof-of-work protocols have gained tremendous popularity. Yet, the proof-of-work based consensus protocols under threats, e.g., double-spending. In this paper, by adopting the cyber-insurance as an economic tool to neutralize cyber risks, we propose a novel approach of cyber risk management for blockchain-based service. The blockchain service market under our consideration is composed of four entities, i.e., the infrastructure provider, blockchain provider, cyber-insurer, and users. The blockchain provider purchases the computing resources, e.g., a cloud, from the infrastructure provider to maintain the blockchain consensus and then offers blockchain services to the users. The blockchain provider optimize its profit by strategizing its investment in the infrastructure in order to improve the security of the blockchain and the service price charged to the users. In the meantime, to prevent the potential damage incurred by the attacks and then fully secure the cyber-space, the blockchain provider purchases a cyber-insurance from the cyber-insurer. In return, the cyber- insurer adjusts the insurance premium according to the perceived risk level of the blockchain service and will pay the claim to the blockchain provider once attacks happen. Based on the rationality of the market entities, we model the interaction among the blockchain provider, users, and cyber-insurer as a two- stage Stackelberg game. Specifically, the blockchain provider and cyber-insurer lead to set their pricing/investment strategies in the upper level subgame, and then the users follow to determine their demand of the blockchain service in the lower level subgame. Specifically, we consider the scenario of double-spending attacks and provide a series of analytical results about the Stackelberg equilibrium in the market game.

Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In this paper, in order to effectively and efficiently recruit sufficient MUs, i.e., participants, we investigate an optimal reward mechanism of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and participating as a two-stage game, and analyze the MUs' participation level and the CSP's optimal reward mechanism using backward induction. At the same time, the reward is designed taking the underlying social network effects amid the mobile social network into account, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbours in social networks. We derive the analytical expressions for the discriminatory reward as well as uniform reward with complete information, and approximations of reward incentive with incomplete information. Performance evaluation reveals that the network effects tremendously stimulate higher mobile participation level and greater revenue of the CSP. In addition, the discriminatory reward enables the CSP to extract greater surplus from this Crowdsensing service market.

With a sponsored content scheme in a wireless network, a sponsored content service provider can pay to a network operator on behalf of the mobile users/subscribers to lower down the network subscription fees at the reasonable cost in terms of receiving some amount of advertisements. As such, content providers, network operators and mobile users are all actively motivated to participate in the sponsored content ecosystem. Meanwhile, in 5G cellular networks, caching technique is employed to improve content service quality, which stores potentially popular contents on edge networks nodes to serve mobile users. In this work, we propose the joint sponsored and edge caching content service market model. We investigate an interplay between the sponsored content service provider and the edge caching content service provider under the non-cooperative game framework. Furthermore, a three-stage Stackelberg game is formulated to model the interactions among the network operator, content service provider, and mobile users. Sub-game perfect equilibrium in each stage is analyzed by backward induction. The existence of Stackelberg equilibrium is validated by employing the bilevel optimization programming. Based on the game properties, we propose a sub-gradient based iterative algorithm, which ensures to converge to the Stackelberg equilibrium.

Blockchain, an emerging decentralized security system, has been applied in many applications, such as bitcoin, smart grid, and Internet-of-Things. However, running the mining process may cost too much energy consumption and computing resource usage on handheld devices, which restricts the use of blockchain in mobile environments. In this paper, we consider deploying edge computing service to support the mobile blockchain. We propose an auction-based edge computing resource allocation mechanism for the edge computing service provider. Since there is competition among miners, the allocative externalities are taken into account in the model. In our auction mechanism, we maximize the social welfare while guaranteeing the truthfulness, individual rationality and computational efficiency. Through extensive simulations, we evaluate the performance of our auction mechanism which shows that the proposed mechanism can efficiently solve the social welfare maximization problem for the edge computing service provider.

Blockchain has recently been applied in many applications such as bitcoin, smart grid, and Internet of Things (IoT) as a public ledger of transactions. However, the use of blockchain in mobile environments is still limited because the mining process consumes too much computing and energy resources on mobile devices. Edge computing offered by the Edge Computing Service Provider (ECSP) can be adopted as a viable solution for offloading the mining tasks from the mobile devices, i.e., miners, in the mobile blockchain environment. However, a mechanism for edge resource allocation to maximize the revenue for the ECSP and to ensure incentive compatibility and individual rationality is still open. In this paper, we develop an optimal auction based on deep learning for the edge resource allocation. Specifically, we construct a multi-layer neural network architecture based on an analytical solution of the optimal auction. The neural networks first perform monotone transformations of the miners' bids. Then, they calculate allocation and conditional payment rules for the miners. We use valuations of the miners as the training data to adjust parameters of the neural networks so as to optimize the loss function which is the expected, negated revenue of the ECSP.We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue.

As the core issue of blockchain, the mining requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power needed. Thus, the development of blockchain in mobile applications is restricted. In this paper, we, for the first time, consider the edge computing as the network enabler for mobile blockchain. In particular, we study optimal pricing-based edge computing resource management to support mobile blockchain applications where the mining process can be offloaded to an Edge computing Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly maximize the profit of the ESP and the individual utilities of different miners. In Stage~I, the ESP sets the price of edge computing services. In Stage~II, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for uniform and discriminatory pricing schemes. Further, the existence and uniqueness of Stackelberg game are validated for both pricing schemes. At last, the performance evaluation shows that the ESP intends to set the maximum possible value as optimal price for profit maximization under uniform pricing. In addition, the discriminatory pricing helps the ESP to encourage higher total service demand from miners and achieve greater profit correspondingly.

Cyber-insurance has been employed as the mean to transfer cyber risks to an insurance company, i.e., insurer. Thereby the users are covered by the insurance to alleviate the loss from cyber threats. In this work, we consider the security vendors (e.g., Symantec) as cyber-insurers selling cyber-insurance in the market. Security service will be attached to the cyber-insurance by the cyber-insurers for the purpose to reduce the probability of paying claims, where the security level of the security service is measured as the security quality. Our proposed model consists of two stages, i.e., the Stackelberg game. In the first stage, cyber-insurers set the price of cyber-insurance charging to the users while security service will be attached to these cyber-insurance. In the second stage, the users decide on the amount of these cyber-insurances to purchase based on the observed prices and the qualities of the security service. The existence and uniqueness for the equilibrium of the Stackelberg game are validated analytically. The performance evaluation presents some interesting results. For example, the cyber-insurer, who provides the security service with higher quality than other cyber-insurers, earns more profit in the market with strong interdependency than that in the market with weak interdependency while other cyber-insurers earn less profit simultaneously. This is due to the fact that the users can be influenced more easily by their peers, when one cyber-insurer provides the security service with higher quality, it can attract more users easily and be more competitive.

Cyber insurance has been introduced as the mean to transfer cyber risks to an insurance company, namely, insurer. The users are thus covered by the insurance to alleviate the damage from cyber threats. In this paper, we investigate the joint pricing and security investment in a cloud-insurance market. The market is composed of users, cloud providers, and cloud-insurers. The users subscribes to use the cloud service (platform) from the cloud providers. To protect from the damage, the users can buy a cloud-insurance product from the cloud-insurers which will pay a claim to the users if an attack happens to the cloud service. The users are interdependent in which they can take advantage of the positive security effects generated by other users' investments in security. We assume that the cloud provider and cloud-insurer are the business partners. Therefore, the cloud-insurers can invest in the cloud platform to improve the security level, i.e., quality, of the cloud service and hence reduce the probability of paying claim. Our proposed model consists of two stages, i.e., the Stackelberg game. In the first stage, cloud-insurers set the price charging to the users and decide on the investment for improving the cloud security quality. In the second stage, the users decide on the amount of these cloud-insurances to purchase based on the observed prices and qualities. The existence and uniqueness for the equilibrium of the Stackelberg game are proved analytically. The performance evaluation shows some interesting results. For example, when the users have strong interdependency, the price of the cloud-insurance becomes lower. This is from the fact that the users can be influenced more easily by their peers, when one cloud-insurer decreases the price, it can attract more users easily.

The data sponsored scheme allows the content provider to cover parts of the cellular data costs for mobile users. Thus the content service becomes appealing to more users and potentially generates more profit gain to the content provider. In this paper, we consider a sponsored data market with a monopoly network service provider, a single content provider, and multiple users. In particular, we model the interactions of three entities as a two-stage Stackelberg game, where the service provider and content provider act as the leaders determining the pricing and sponsoring strategies, respectively, in the first stage, and the users act as the followers deciding on their data demand in the second stage. We investigate the mutual interaction of the service provider and content provider in two cases: (i) competitive case, where the content provider and service provider optimize their strategies separately and competitively, each aiming at maximizing the profit and revenue, respectively; and (ii) cooperative case, where the two providers jointly optimize their strategies, with the purpose of maximizing their aggregate profits. We analyze the sub-game perfect equilibrium in both cases. Via extensive simulations, we demonstrate that the network effects significantly improve the payoff of three entities in this market, i.e., utilities of users, the profit of content provider and the revenue of service provider. In addition, it is revealed that the cooperation between the two providers is the best choice for all three entities.

Internet of Things (IoT) technology enables various physical devices to collect, process and exchange information. Market oriented models become important for IoT systems to efficiently utilize information, as IoT network nodes operate in a highly distributed and autonomous manner. In this work, we propose a three-player game theoretic market model for IoT information trading, considering direct and indirect externalities among market participants. In the model, an IoT service provider collects and processes IoT information, and then delivers the processed information as IoT services to IoT users. Then, an IoT content vendor senses and generates raw information for the IoT service provider to collect, and receives rewards from the provider. Finally, an IoT user pays a fixed service fee to the IoT service provider to access the IoT services. To jointly derive the optimal market decisions of the three participants in the model, we employ a Stackelberg game approach. The equilibria are obtained as the closed form solutions of the game, with which the existence and uniqueness properties are proved. The analytical results show that the IoT service provider operates as an intermediary agent between the IoT content vendor and users, reducing the information trading complexity of both user and vendor sides.

With the advancement of wireless energy harvesting and transfer techniques, an Internet of Things (IoT) node equipped with a wireless charging facility can request and receive energy from wireless chargers deployed at different locations. This provides more opportunity for the mobile IoT node to replenish its battery and be able to operate without interruption due to shortage of energy supply. In this paper, we develop an optimal energy charging scheme for the mobile IoT node, considering the states of location, traffic generation, and energy storage. We formulate the problem of energy charging as a Markov decision process (MDP) to obtain the mobile IoT node’s optimal policy. The objective is to maximize the expected utility. Furthermore, we prove that the optimal policy of the proposed MDP has a threshold structure. The numerical results show the performances of the mobile IoT node under various scenarios and parameter setting. Furthermore, the proposed MDP-based wireless energy charging scheme outperforms conventional baseline schemes.

The sponsored content/service market is an emerging platform, where the Content/Service Providers (CSPs) pay the Mobile Network Operator (MNO) and subsidize the Mobile Users (MUs) to access their services at a lower price. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the CSPs. In this paper, we study the interactions among the three entities in the market, namely, the MUs, the CSPs and the MNO, as a two-level hierarchical game. Our study is featured by the consideration of global network effects owning to consumers' grouping. We model the service- selection process among the MUs as an evolutionary population sub-game, and the sponsoring-pricing process between the CSPs and the MNO as a non- cooperative sub-game. By investigating the structure of the proposed game, we discover a few important properties regarding the existence of the hierarchical equilibrium, and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence property of the proposed algorithm, and demonstrate how sponsoring helps to improve both the CSPs' profits and the MUs' experience.

Mobile data demand is increasing tremendously in wireless social networks, and thus efficient pricing for socialenabled services is urgently needed. In this paper, we study the sequential dynamic pricing scheme of a monopoly mobile service provider in a social data market, where the provider, i.e., the seller, individually offers each user, i.e., the buyer, a certain price in multiple time periods dynamically and repeatedly. The proposed scheme exploits the network effects in the behavior model of mobile users that boost the social data demand. Furthermore, due to limited radio resource, the impact of wireless network congestion is taken into account in the pricing scheme. Through both the mathematical analysis and simulation, we demonstrate that our proposed sequential dynamic pricing can help the service provider to achieve greater revenue and mobile users achieve higher total utilities than those of existing optimal static pricing scheme.

Sponsored content policy enables a content provider to pay a network operator, and thereby their users access contents from the content provider through network services from the network operator with lower charge. In this paper, we study the interaction among three entities under the sponsored content policy, namely, the network operator or service provider, the content provider and the end-users. We consider a hierarchical three-stage setting to formulate the game theoretic model to analyze the interaction. Using the game model, we derive the user content demand, optimal sponsoring of content provider, and pricing of service provider based on backward induction. The model incorporates the network effects in social domain and congestion in network domain which enables us to obtain insights from the sponsored content policy. We derive the closed-form solution, i.e., equilibrium, and prove its existence and uniqueness in each stage of the game. Additionally, we develop an iterative algorithm to obtain the Stackelberg equilibrium of the entire three-stage game. The simulation results indicate that the revenue, profit, and utility of the service provider, content provider, and end-users have been improved to a large extent under the sponsored content policy because of the network effects.

In cloud computing, cloud services can be allocated to users upon requests in an on-demand basis. Heterogeneous cloud service providers may join the cloud systems to serve various types of users. Cloud services can be complementary or substitutable. For the complementary services, users may request for a bundle of the services, e.g., CPU and storage, to gain higher benefit from requesting them alone. The substitutable services have similar functionalities to serve users, e.g., different cloud database services, obtaining one of them can replace another one. Furthermore, the users of the cloud systems also influence each other because of externalities, particularly, network effect and congestion effect. From the perspective of each user, the existence of other users may introduce positive or negative impacts on the user utility, in the case of network and congestion effects, respectively. In this work, the participants in the cloud systems are treated as social enabled rational individuals. We model the complementarity, substitutability and externalities in cloud services by employing a multiple-leader multiple- follower Stackelberg game approach, including a two-stage service transaction process where service providers and users make their transaction decisions in a distributed manner. The analytical expressions of equilibria, service pricing strategies, and service allocations are derived with numerical results. We also find in the numerical results that both collusive and competitive service pricing schemes may lead to the optimized provider and user performances simultaneously.

An important issue in Wireless Sensor Networks (WSNs) is to maximize the network lifetime while guaranteeing the desired coverage requirement. Recent studies have demonstrated that using rechargeable sensor nodes with energy harvesting capability has a great potential to extend the network lifetime. However, due to the high cost of rechargeable nodes, equipping every node with an energy harvesting unit is not practical in a large scale WSN. In this paper, we study the problem of maximizing the network lifetime of a hybrid WSN consisting of both common nodes and rechargeable nodes. Furthermore, we consider a new confident information coverage model which is more efficient for environment monitoring applications. We propose a novel priority-based greedy scheduling (PGS) algorithm to schedule the sensor nodes into a series of set covers that are activated sequentially, each satisfying the required coverage. Also, it can effectively exploit the advantages of using collaboration among sensors for the coverage requirement. Our simulations validate that the PGS algorithm can provide substantial performance improvement as compared to other peer algorithms.