Vehicular metaverses, blending traditional vehicular networks with metaverse technology, are expected to revolutionize fields such as autonomous driving. As virtual intelligent assistants in vehicular metaverses, Artificial Intelligence (AI) agents empowered by large language models can create immersive 3D virtual spaces for passengers to enjoy on-board vehicular applications and services. To provide users with seamless and engaging virtual interactions, resource-limited vehicles offload AI agents to RoadSide Units (RSUs) with adequate communication and computational capabilities. Due to the mobility of vehicles and the limited coverage of RSUs, AI agents need to migrate from one RSU to another. However, potential network attacks pose significant challenges to ensuring reliable and efficient AI agent migration. In this paper, we first explore specific network attacks, including traffic-based attacks (i.e., DDoS attacks) and infrastructure-based attacks (i.e., malicious RSU attacks). Then, we model the AI agent migration process as a Partially Observable Markov Decision Process (POMDP) and apply multi-agent proximal policy optimization algorithms to mitigate DDoS attacks. In addition, we propose a trust assessment mechanism to counter malicious RSU attacks. Numerical results demonstrate that the proposed solutions effectively defend against these network attacks and reduce the total latency of AI agent migration by approximately 12.8%.

The integration of emerging uncrewed aerial vehicle (UAV) with artificial intelligence (AI) and ground-embedded robots (GERs) has transformed emergency rescue operations in unknown environments. However, the high computational demands of such missions often exceed the capacity of a single UAV, making it difficult for the system to continuously and stably provide high-level services. To address these challenges, this paper proposes a novel cooperation framework involving UAVs, GERs, and airships. This framework enables resource pooling through UAV-to-GER (U2G) and UAV-to-airship (U2A) communications, providing computing services for UAV offloaded tasks. Specifically, we formulate the multi-objective optimization problem of task assignment and exploration optimization in UAVs as a dynamic long-term optimization problem. Our objective is to minimize task completion time and energy consumption while ensuring system stability over time. To achieve this, we first employ the Lyapunov optimization method to transform the original problem, with stability constraints, into a per-slot deterministic problem. We then propose an algorithm named HG-MADDPG, which combines the Hungarian algorithm with a generative diffusion model (GDM)-based multi-agent deep deterministic policy gradient (MADDPG) approach, to jointly optimize exploration and task assignment decisions. In HG-MADDPG, we first introduce the Hungarian algorithm as a method for exploration area selection, enhancing UAV efficiency in interacting with the environment. We then innovatively integrate the GDM and multi-agent deep deterministic policy gradient (MADDPG) to optimize task assignment decisions, such as task offloading and resource allocation. Simulation results demonstrate the effectiveness of the proposed approach, with significant improvements in task offloading efficiency, latency reduction, and system stability compared to baseline methods.

The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing resources, improving efficiency and scalability. Efficient IIoT operations require a trade-off between model quality and training latency. Consequently, a primary challenge of FL in IIoT is to optimize overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency for meta-computing. Additionally, the satisfaction function is incorporated into the utility function to incentivize IIoT nodes to participate in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for IIoT. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves utility by at least 23.7% compared to existing FL schemes without compromising model accuracy.

Domain shifts pose significant challenges to video-based Facial Expression Recognition (FER), resulting in reduced robustness and fairness in real-world deployments. We present HOT-SPT, a framework that addresses cross-domain adaptation through structured distribution alignment. Our approach combines a Hierarchical Optimal Transport (HOT) loss with Spatiotemporal Prompt Tuning (SPT). The HOT loss performs dual-level alignment: frame-level spatial features handle appearance variations, while video-level temporal features capture expression dynamics. SPT enables parameter-efficient adaptation by introducing learnable prompts to frozen backbone models, requiring less than 1% of total parameters. Experiments across challenging benchmarks demonstrate that HOT-SPT achieves superior performance in accuracy, robustness, and fairness compared to existing methods.

uncrewed aerial vehicles (UAVs) offer high mobility and flexible deployment capabilities, making them ideal for Internet of Things (IoT) applications. However, the substantial amount of data generated by various applications within the existing low-altitude network requires processing through deep neural networks (DNN) on UAVs, which is challenging due to their limited computational resources. To address this issue, we propose a two-stage optimization method for flight path planning and task allocation based on a mother-child UAV swarm system. In the first stage, we employ a greedy algorithm to solve the path planning problem by considering the task size of the target area to be inspected and the shortest flight path as constraints. The goal is to minimize both the flight path of the UAV and the overall cost of the system. In the second stage, we introduce a novel DNN task assignment algorithm that combines multiagent deep deterministic policy gradient (MADDPG) and generative diffusion models (GDMs), named GDM-MADDPG. This algorithm takes advantage of the reverse denoising process of GDM to replace the actor network in MADDPG. It enables UAVs to generate specific DNN task assignment actions based on agents’ observations in a dynamic environment, thereby improving the efficiency of task assignment and overall system performance. The simulation results demonstrate that our algorithm outperforms the benchmarks in terms of path planning, Age of Information (AoI), task completion rate, and system utility, demonstrating its effectiveness.

The rapid proliferation of Internet of Things (IoT) is generating an unprecedented volume of distributed data, necessitating efficient decentralized learning paradigms. Federated learning (FL) has emerged as a compelling distributed collaborative intelligence framework, renowned for its privacy protection benefits. However, the communication overhead associated with intermediate model exchanges remains a critical bottleneck in FL. Aiming at reducing the communication cost of FL equipped with promising over-the-air computation (AirComp) technique, this work designs specialized model compression schemes for both uplink and downlink communications. For uplink transmission with AirComp, we analyze its unique constraints and propose a hybrid global sparsification scheme that combines the benefits of conventional Top-k and Rand-k algorithms. We further develop an algorithm to strategically allocate transmission budgets between the two concatenated sparsification operations, accounting for both model temporal correlation and the cost of index synchronization. For downlink transmission, we introduce a group-based mixed-precision quantization (MPQ) scheme and integrates the broadcast of grouping information with uplink sparsification pattern to further mitigate communication burden. Moreover, we conduct theoretical analysis under realistic channel conditions and typical FL settings to validate the advantages and establish convergence guarantees of our approaches. Experimental results demonstrate that, compared to existing schemes, the proposed methods significantly improve communication efficiency and ensure client scalability, and concurrently verify the benefits of the uplink-downlink synergistic design.

The fusion of the Internet of Things (IoT) with sixth-generation (6G) technology has significant potential to revolutionize the IoT landscape. With the ultrareliable and low-latency communication capabilities of 6G, 6G-IoT networks can transmit high-quality and diverse data to enhance edge learning. Artificial intelligence-generated content (AIGC) harnesses advanced artificial intelligence (AI) algorithms to automatically generate various types of content. The emergence of edge AIGC integrates with edge networks, facilitating real-time provision of customized AIGC services by deploying AIGC models on edge devices. However, the current practice of edge devices as AIGC service providers (ASPs) lacks incentives, hindering the sustainable provision of high-quality edge AIGC services amidst information asymmetry. In this article, we develop a user-centric incentive mechanism framework for edge AIGC services in 6G-IoT networks. Specifically, we first propose a contract theory model for incentivizing ASPs to provide AIGC services to clients. Recognizing the irrationality of clients toward personalized AIGC services, we utilize prospect theory (PT) to capture their subjective utility better. Furthermore, we adopt the diffusion-based soft actor-critic algorithm to generate the optimal contract design under PT, outperforming traditional deep reinforcement learning algorithms. Our numerical results demonstrate the effectiveness of the proposed scheme.

By using the intelligent edge computing technologies, a large number of computing tasks of end devices in Industrial Internet of Things (IIoT) can be offloaded to edge servers, which can effectively alleviate the burden and enhance the performance of IIoT. However, in large-scale multi-service-oriented IIoT scenarios, offloading service resources are heterogeneous and offloading requirements are mutually exclusive and time-varying, which reduce the offloading efficiency. In this paper, we propose a cloud-edge-end collaboration intelligent service computation offloading scheme based on Digital Twin (DT) driven Edge Coalition Formation (DECF) approach to improve the offloading efficiency and the total utility of edge servers, respectively. Firstly, we establish a DT model to obtain accurate digital representations of heterogeneous end devices and network state parameters in dynamic and complex IIoT scenarios. The DT model can capture time-varying requirements in a low latency manner. Secondly, we formulate two optimization problems to maximize the offloading throughput and total system utility. Finally, we convert the multi-objective optimization problems to a Stackelberg coalition game model and develop a distributed coalition formation approach to balance the two optimizing objectives. Simulation results indicate that, compared with the nearest coalition scheme and non-coalition scheme, the proposed approach achieves offloading throughput improvements of 11.5% and 148%, and enhances the overall utility by 12% and 170%, respectively.

Most existing multi-unmanned aerial vehicle (multi-UAV) systems focus on fly path or energy consumption for task assignment, while little attention has been paid to the dynamic feature of the task, resulting in poor task completion ratio. The machine learning (ML) paradigm provides new methodologies for task assignment. However, ML methods are usually of heavy resource-consumption that cannot be directly applied in the UAV. In this paper, a digital twin (DT) assisted task assignment approach is proposed to improve the resource-intensive utilization and the efficiency of deep reinforcement learning (DRL) in multi-UAV system. The approach has a three-layer network structure which can dynamically assign tasks based on the task time constraints. Moreover, the approach is divided into two stages of initial task-assignment and task-reassignment. In the first stage, airship divides a task into multiple subtasks according to the shortest distance based on genetic algorithm and assigns them to UAVs. In the second stage, the DT can be leveraged to enable the airships to learn from the features of tasks and to generate the Q-value of the estimated value network of DRL for UAVs via pre-train of DT. The Q-value can be directly applied for deep Q-learning network (DQN) in the UAVs to reduce the training episode. Furthermore, the DQN is adopted to train task-reassignment strategy. Simulation results indicate that the DQN with DT can significantly reduce the training episode, improving 30% of the task completion ratio and 19% of the system energy efficiency compared with that of the baseline methods.

Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns related to the training data and unfair behaviors of some decisions with regard to certain attributes (e.g., sex, race) are becoming more critical. Thus, constructing a fair machine learning model while simultaneously providing privacy protection becomes a challenging problem. In this paper, we focus on the design of classification model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. In order to enforce ϵ-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint. We further propose an utility-enhancement scheme, called relaxed functional mechanism by adding Gaussian noise instead of Laplace noise, hence achieving (ϵ, δ)-differential privacy. Based on the relaxed functional mechanism, we can design (ϵ, δ)-differentially private and fair classification model. Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms.

The fast penetration of Intelligent Connected Vehicles (ICVs) has become the primary growth engine of the automotive industry in recent years. Urban vehicular network consisting of ICVs is evolving towards a distributed intelligent platform for pervasive sensing, connecting and computing in Intelligent Transportation System (ITS) and smart cities. In this paper, we propose that parked vehicles (PVs) could be exploited for environment perception and model inference. We describe the system architecture and its typical application scenarios of distributed environment perception for city roads, parking lots, as well as for commercial and residential buildings. PVs are motivated to assist in deep learning model inference for the captured image data in such applications. Regarding the diversity of PVs in deep learning capability, a differential incentive mechanism is elaborately designed based on contract theory to emulate PVsparticipation. The experiment on the dataset of German Traffic Sign Recognition Benchmark is conducted to verify the effectiveness and efficiency of the proposed approach.

With the repaid development of Internet of Vehicles (IoV), more available resources and energy-efficient optimizations in resources scheduling are exactly required for large-scale network implementation for sustainable development. We observe that parked vehicles (PVs) have rich and underutilized resources for task execution. By scheduling them as general computing nodes to undertake computation tasks, we introduce a new computing paradigm, named by parked vehicular computing (PVC). There exists some challenging issues to be addressed for the facilitation of PVC. In particular, an incentive mechanism is needed to offer optimized rewards for PVs with the consideration of their parking time and energy consumption. In this paper, we investigate an energy-efficient PVC paradigm, and we design a contract-based incentive mechanism to motivate PVs to contribute their idle on-board resources. The PVs are classified into different types according to their parking time. Then, the designed contracts are assigned to different types of PVs. To realize the incentive mechanism, the optimization problem with the contract design is formulated to maximize the utility of the service provider. For optimal contract design, we solve the simplified problem by using Lagrangian multiplier method. Numerical results indicate that the proposed PVC with optimal contract design outperforms existing work in improving social welfare of resource scheduling, which takes quality-of-service and overall energy consumption into consideration. We also demonstrate that the contract-based incentive mechanism is energy-efficient and effective.

In this paper, a novel system model named as massive multiple-input multiple-output (MIMO) cognitive radio system (CRS) is built and an efficient uplink power control algorithm based on noncooperative game theory with a self-adaptive power threshold scheme is proposed to improve the power efficiency. And then, we give an analytical model for the massive MIMO CRS and the detail of the self-adaptive power threshold scheme. Moreover, we prove the existence of the Nash Equilibrium and the convergence of the proposed algorithm. To evaluate the performance of the proposed algorithm, we do simulations and compare the system performance with two other classical power control algorithms. Simulation results demonstrate that the proposed algorithm can achieve a preferable performance in signal-to-noise-plus interference ratio (SINR) and a higher utility with lower transmission power and faster convergence. It implies that the novel way can achieve higher power efficiency and a better overall system performance.

In vehicle‐to‐vehicle (V2V) communications, low delay and long propagation distance are very important for multi‐hop safety‐related message broadcasting. Most earlier studies focused on one‐hop broadcasting while little attention has been paid to multi‐hop delay and propagation distance. In this study, a new model for analysing the connectivity probability, average hop count and one‐hop delay of multi‐hop safety‐related message broadcasting in V2V communications is built, taking into account the following factors: propagation distance, one‐hop transmission range, distribution of vehicles, vehicle density, average length of vehicles and minimum safe distance between vehicles. Simulation results demonstrate that the proposed model can provide better performance in terms of multi‐hop delay and there exists an optimal one‐hop transmission range to minimise the multi‐hop delay. After that, A new scheme is proposed to track the optimal one‐hop transmission range by using a Genetic Algorithm. With this scheme, vehicles are allowed to adjust the one‐hop transmission range based on vehicle density to reduce the multi‐hop delay. The proposed scheme is validated by simulations using realistic vehicular traces.