As efficiency and security requirements emerge in computing offloading fields, trusted computing offloading has grabbed tremendous sights, especially in Air-Ground Integrated Networks (AGINs). Although traditional computing offloading studies have attempted to employ trust to identify malicious devices without mobility, the integration of trust management and computing offloading has not been explored in a high-hazardous environment. Then, with the increasing requirement of adaptation and security in new-generation network architectures (i.e., AGINs), trust management plays a pivotal role in expanding the implementation of trusted computing offloading. Therefore, we propose a trusted time-aware computing offloading mechanism in AGINs based on communication, computing offloading, and trust modeling. Specifically, a maximum reward optimization formulation is designed to generate an optimal offloading strategy, considering service utility trust, opinion service trust, computing efficiency trust, time constraints, rewards, and task volumes. Based on problem analysis, a solution paradigm is condensed to improve the probability of seeking an efficient solution. Moreover, a greedy search algorithm is designed to find the possible efficient solution, including the default solution setting stage, the boundary search stage, and the greedy reallocation stage. Due to the trust threshold affecting identification results, a dichotomy-based dynamic trust threshold method is employed to empower trusted computing offloading mechanism with the adaptive capability in AGINs. Extensive experiments show that our mechanism outperforms other baselines in terms of accuracy, precision, recall, F-measure, task success rate, and average response time.

Data collection and distribution through crowdsensing has become an emerging trend in smart city scenarios. By leveraging existing vehicle resources without deploying dedicated infrastructure, Vehicular CrowdSensing (VCS) provides low-cost and high-mobility data collection on road networks. Typically, the Crowdsensing Platform (CP) issues data collection tasks, recruits Sensing Vehicles (SVs) to complete tasks, and sells the collected data to Data Demanders (DDs). Here, the goal of CP is to maximize profits through data collection and sales, and the goal of DDs is to improve satisfaction by purchasing high-quality sensing data. It can be seen that both CP and DD hope that SVs can complete more sensing tasks at a limited cost (high efficiency) while ensuring the accuracy of data collection (high quality). However, due to individual rationality and selfishness, not all SVs are willing to complete the sensing task. Therefore, how to motivate SVs to complete sensing tasks with high quality and efficiency, while handling the relationship among CP, DDs, and SVs, is a problem that needs to be considered. To solve the above problems, this paper proposes a Reputation-based Hybrid Incentive Approach (RHIA), with the goal of maximizing the utility of CP, SVs, and DDs. Specifically, in order to improve the task completion quality of SVs, we introduce vehicle reputation to measure SVs. Then, we propose a one-to-one bargaining game between CP and each SV, and use the reputation value as the sequential basis of the game. Meanwhile, in order to improve the task completion efficiency of SVs, we also design a unique SV Trajectory Planning Algorithm (STPA). Further, in order to meet the needs of DDs, a one-to- multi Stackelberg game between CP and DDs is proposed. Here, the existence and uniqueness of Nash equilibrium is proved through backward induction. Finally, based on real-world datasets, the effectiveness of our proposed RHIA and STPA is verified. Our proposed method can ensure the long-term stability of the VCS system, which also improves the utility of participating individuals.

Federated Learning (FL) faces critical challenges in heterogeneous and resource-constrained environments, including device diversity, high communication overhead, and training delays. Therefore, we propose FedKDC, a federated learning framework that integrates knowledge distillation with data compression to jointly optimize server bandwidth, client computation resources, and compression ratios, thereby minimizing training latency. In particular, FedKDC employs a Generative Adversarial Network (GAN)-based generator to produce synthetic data for knowledge transfer across heterogeneous models without sharing raw data, mitigating privacy risks. Then, FedKDC uses a loss-driven adaptive compression mechanism to adjust the minimum compression threshold based on training stability, reducing communication volume while maintaining accuracy. In addition, we further discuss the problem of resource allocation under system constraints, and uses Particle Swarm Optimization (PSO) algorithm to solve it. Based on the three real world datasets (i.e., Fashion-MNIST, CIFAR-10, and CIFAR-100), the experimental results demonstrate that FedKDC reduces communication cost by up to 17% and training time by 8%. This shows that FedKDC is effective for large-scale heterogeneous FL deployment while maintaining the accuracy of the model.

The advent of 6G and the Metaverse has created a need for efficient real-time data processing and low-overhead communication. To address this challenge, we propose SemCom-MN, a semantic communication-enhanced Metaverse framework integrating an Edge Service Provider (ESP), Edge Sensing Units (ESUs), and Virtual Service Providers (VSPs). ESUs capture physical-world data, ESP manages semantic information, and VSPs create immersive virtual environments. To improve utility under heterogeneous information and computational requirements, we model semantic information trading as a three-stage Stackelberg game and prove the existence of a Nash equilibrium. Furthermore, to overcome high-dimensional dynamics and slow convergence in semantic trading, we develop a Diffusion Game Algorithm (DGA) combining strategic exploration with a game-theoretic denoising mechanism, achieving robust convergence. Simulation results show DGA increases system utility by 8.49%–33.94%.

Recently, For-Hire Vehicles (FHVs) have emerged as major players in Vehicular CrowdSensing (VCS). However, the heterogeneity of tasks issued by Data Requesters (DRs) and the heterogeneity of sensors equipped on FHVs under different Vehicle Platforms (VPs) bring difficulties to task allocation and execution. It can be concluded that it is important to reasonably analyze the relationship among DRs, VPs, and FHVs, as well as to motivate VPs and FHVs to complete sensing tasks. Therefore, taking advantage of the real-time simulation and intelligent decision-making of Digital Twins (DT), this paper proposes a DT-drivenDouble-layerReverseAuctionMethod (DRAM). In the first layer, the reverse auction is established between each DR and VPs, and in the second layer, the reverse auction is established between each VP and FHVs. Meanwhile, we also introduce a sensing fairness index to ensure the sensing balance of different sub-regions and consider it in the DRAM process. Here, the idea of backward induction is used to solve the above problems, with the goal of minimizing the overhead of winning VP and the average overhead of all DRs. Finally, the effectiveness of the DRAM proposed in this paper is verified based on the real data set. Compared with the baseline method, DRAM can reduce the average overhead of DR by about 4%-25%. Meanwhile, in terms of sensing fairness, it can be improved by up to 55%.

The Internet of Vehicles (IoV) has witnessed a significant growth in the number of participants. This rapid expansion has increased demands for computing resources and quality of service (QoS), posing challenges for mobile edge computing (MEC) in the IoV domain. Efficiently allocating computing power to meet these service demands has become a crucial concern. Therefore, joint optimization of offloading decisions and power allocation is required to achieve the tradeoff between task latency and energy consumption. To address the above challenge, we propose a multi-agent reinforcement learning (MARL) method called multi-agent twin delayed deep deterministic policy gradient (MA-TD3) in this paper. Compared to its predecessor, multi-agent deep deterministic policy gradient (MADDPG), this algorithm improves performance and execution speed. It solves the slow convergence problem caused by Q-value overestimation and reduces the computational cost. The experimental results illustrate that the proposed algorithm reaches an observable performance improvement.

By utilizing on-board sensors and computing resources in intelligent vehicles, vehicular crowdsensing can collect a series of sensing data. Typically, sensing vehicles can be divided into opportunistic vehicles with fixed trajectories and participatory vehicles with changeable trajectories. Therefore, to complete sensing tasks more effectively, how to combine the advantages of the mobility characteristics of the two vehicles is a challenging problem. To solve this problem, this paper innovatively proposes a joint scheduling and incentive-driven two-stage hybrid sensing method. Specifically, the method is divided into two stages: opportunistic vehicle selection and participatory vehicle scheduling. In particular, both types of vehicles are managed through the Crowd Sensing Platform (CSP). For the first stage, this paper proposes a reverse auction-based incentive mechanism to select the lowest-cost set of vehicles to complete sensing tasks. This mechanism mainly consists of two steps: winning vehicle selection and reward payment. It is also verified that the proposed mechanism can ensure the individual rationality and truthfulness of opportunistic vehicles. For the second stage, based on the first-stage sensing results, this paper proposes a Soft Actor-Critic (SAC) based approach to scheduling participatory vehicle trajectories to complete sensing tasks. In addition, this paper also considers sensing fairness to ensure the balance of sensing task completion in different sub-regions. Through the two-stage hybrid sensing method, this paper aims to minimize the CSP overhead while ensuring sensing fairness. Finally, extensive evaluation results based on Roma taxi data sets demonstrate that the proposed method works effectively and outperforms other benchmark schemes in different working scenarios.

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide additional computation capability and wide coverage for mobile users (MUs), especially when MUs are not within the communication range of the infrastructure. In this article, a UAV-aided mobile-edge computing (MEC) network, including one UAV-MEC server, one BS-MEC server, and several MUs, is investigated for computation offloading, in which the edge service provider (ESP) manages two kinds of servers. It is considered that MUs have a large number of computation tasks to conduct, while the ESP has idle computational resources. MUs can choose to offload their tasks to the ESP to reduce their pressure and cost, and the ESP can make a profit by selling computational resources. The interaction among the ESP and MUs is modeled as a Stackelberg game, and both the ESP and MUs want to maximize their utility. The proposed game is analyzed by using the backward induction method, and it is proved that a unique Nash equilibrium can be achieved in the game. Then, a gradient-based dynamic iterative search algorithm (GDISA) is proposed to get the approximate optimal solution. Finally, the effectiveness of GDISA is verified by extensive simulations, and the results show that GDISA performs better than other benchmark methods under different scenarios.

Offloading computation tasks through cloud–edge collaboration has been a promising way to improve the Quality of Service (QoS) of applications. Usually, cloud server (CS) and edge server (ES) are selfish and rational and, therefore, it is imperative to develop incentive mechanisms, which can encourage idle ESs or the CS to participate in the task offloading process. In this article, we propose a computation offloading method based on the game theory, which is suitable for cloud–edge computing networks. It is considered that the CS has a lot of computation tasks to conduct, and ESs usually have idle computational resources. The CS can offload computation tasks to ESs with idle computational resources to reduce its own cost and pressure, and ESs can profit by selling their computational resources. The interaction between the CS and ESs is modeled as a Stackelberg game, and the proposed game is analyzed by using the backward induction method. It is proved that the game can achieve a unique Nash equilibrium. Then, a gradient-based iterative search algorithm (GISA) is proposed to obtain the optimal solution in order to maximize the utility of the CS and ESs. Finally, numerical simulation results show that our proposed method greatly outperforms other benchmark schemes under different scenarios, and can encourage ESs to trade their computational resources with the CS effectively.

In this paper, we propose a computation offloading method based on the game theory, which is suitable for cloud-edge computing networks. We consider that the Cloud Server (CS) can offload the computation tasks to wireless Access Points (APs) associated with Edge Servers (ESs) to accelerate processing. ESs can gain benefits through computation offloading, while the CS can reduce its cost and computing pressure. We model the interaction between the CS and ESs as a Stackelberg game, and use the backward induction method to analyze the proposed game. We prove that the game can achieve a unique Nash equilibrium. Then, we propose a Gradient-based Iterative Search Algorithm (GISA) to maximize the utility of the CS and ESs. Finally, numerical simulation results show that our proposed method greatly outperforms other benchmark schemes under different scenarios, and can encourage ESs to trade their computation resources with the CS effectively.

Existing techniques for improving datacenter utilization while guaranteeing the QoS are based on the assumption that queries have similar behaviors. However, user queries in emerging compute demanding services demonstrate significantly diverse behavior and require adaptive parallelism. Our study shows that the end-to-end latency of the compute demanding query is determined together by the system-wide load, its workload, its parallelism, contention on shared cache, and memory bandwidth. When hosting such new services, the current cross-query resource allocation results in either severe QoS violation or significant resource under-utilization.

GPUs have been widely adopted in data centers to provide acceleration services to many applications. Sharing a GPU is increasingly important for better processing throughput and energy efficiency. However, quality of service (QoS) among concurrent applications is minimally supported. Previous efforts are too coarse-grained and not scalable with increasing QoS requirements. We propose QoS mechanisms for a fine-grained form of GPU sharing. Our QoS support can provide control over the progress of kernels on a per cycle basis and the amount of thread-level parallelism of each kernel. Due to accurate resource management, our QoS support has significantly better scalability compared with previous best efforts. Evaluations show that, when the GPU is shared by three kernels, two of which have QoS goals, the proposed techniques achieve QoS goals 43.8% more often than previous techniques and have 20.5% higher throughput.

Studies show that non-graphics programs can be less optimized for the GPU hardware, leading to significant resource under-utilization. Sharing the GPU among multiple programs can effectively improve utilization, which is particularly attractive to systems where many applications require access to the GPU (e.g., cloud computing). However, current GPUs lack proper architecture features to support sharing. Initial attempts are preliminary: They either provide only static sharing, which requires recompilation or code transformation, or they do not effectively improve GPU resource utilization. We propose Simultaneous Multikernel (SMK), a fine-grain dynamic sharing mechanism, that fully utilizes resources within a streaming multiprocessor by exploiting heterogeneity of different kernels. We propose several resource allocation strategies to improve system throughput while maintaining fairness. Our evaluation shows that for shared workloads with complementary resource occupancy, SMK improves GPU throughput by 52% over non-shared execution and 17% over a state-of-the-art design.

The Transient Chaotic Neural Network (TCNN) and the Noisy Chaotic Neural Network (NCNN) have been proved their searching abilities for solving combinatorial optimization problems(COPs). The chaotic dynamics of the TCNN and the NCNN are believed to be important for their searching abilities. However, in this paper, we propose a strategy which cuts off the rich dynamics such as periodic and chaotic attractors in the TCNN and just utilizes the nonchaotic converge dynamics of the TCNN to save the time needed for computation. The strategy is named as nonchaotic simulated annealing (NCSA). Experiments on the traveling salesman problems exibit the effectiveness of NCSA. The NCSA saves over half of the time needed for the computation while maintaining the searching ability of the TCNN.