Autonomous systems in VEC-enabled ICVs face many challenges, such as self-organization, privacy breach risks, vehicle selection, and resource allocation. As a distributed training framework, Federated Meta-Learning (FML) provides a powerful tool for adaptive and efficient processing of vehicular tasks while securing vehicle data privacy in VEC-enabled ICVs. However, the high-speed mobility of vehicles leads to higher latency and communication interruptions. This article investigates the vehicle selection and resource allocation scheme, subject to the constraints on the number and the residence time of vehicles, the maximum transmission energy consumption, and the ratio of bandwidth resource allocation. It is proved to be a challenging mixed-integer nonlinear programming problem, and we formulate it as a Markov decision process (MDP). We proposed an adaptive Sum Tree-Deep Recurrent Q-network algorithm (ST-DRQN) to solve the optimal resource allocation. ST-DRQN employs an enhanced empirical selection rule and a proportional priority sampling method to address the problems of inefficient model training and slow convergence. Finally, we conducted experiments using intelligent cars equipped with Raspberry Pi to show the effectiveness of the proposed methodology. Experimental results demonstrate that ST-DRQN achieves adaptability and credibility among ICVs while reducing latency and energy costs incurred by long-term training of FML.

With the rapid development of vehicular edge computing (VEC) and artificial intelligence (AI), the emergence of vehicle edge intelligence meets the need for real-time vehicle intelligence applications. But the execution of deep neural networks (DNNs) requires a large amount of data input, which results in a large amount of computing resources required for the execution of DNN tasks. This also brings a certain burden to the deployment of DNN tasks and the resource allocation of edge servers. In addition, due to the high mobility of vehicles in the VEC, the backhaul delay of vehicle edge intelligent task results increases, affecting the vehicle’s quality of experience (QoE). We propose a joint optimization strategy for service migration and resource allocation aimed at minimizing the average task completion delay. This strategy comprehensively considers service migration actions and edge server resource allocation, which is proved to be a mixed integer nonlinear programming (MINLP) problem, and hence we formulate it as an Markov decision process (MDP). To solve this problem, we propose a service migration algorithm based on the self-attention mechanism-based double deep Q-network and deep deterministic policy gradient algorithm (SA-DDQN-DDPG) to solve it to obtain the optimal system service migration strategy. The experimental results show that the proposed SA-DDQN-DDPG algorithm has good performance in reducing latency. The average migration latency is reduced by 40.41%, 20.7%, and 14.50% compared with always, DQN and DDQN, respectively.

With the rapid development of intelligent transportation systems, multimodal applications such as autonomous driving and real-time video analytics are increasingly common. Cloud-edge-end computing has emerged as a promising solution to support these latency-sensitive tasks through distributed computing and edge content delivery. However, in urban hotspot areas, limited resources and frequent backhaul transmissions degrade network performance. Optimizing content caching to reduce task execution remains a key challenge. To address this, Unmanned Aerial Vehicles (UAVs) are introduced into vehicular networks due to their low cost and high mobility, serving as aerial base stations to assist ground infrastructure. We propose an cloud-edge-end collaborative caching framework, deploying algorithms on UAVs with computing and storage capabilities, working with Roadside Units (RSUs) and idle vehicles to alleviate resource constraints in hotspots. Within this framework, we apply the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm for UAV deployment optimization. Then, we propose a content request prediction model using Bidirectional Gated Recurrent Unit (Bi-GRU) and attention mechanisms. Finally, a content caching algorithm based on Soft Asynchronous Advantage Actor-Critic with Action Mask Module (SA3C-AM) is introduced to minimize latency. Experimental results show that compared to baseline methods, our approach improves cache hit rate by 15.4%, reduces content fetches by 34.08%, and lowers average request latency by 17.6%.

In recent years, deep neural networks (DNNs) have been widely used in Vehicular Edge Computing (VEC), becoming the core technology for most intelligent applications. However, these DNN inference tasks are usually computation-intensive and latency-sensitive. In urban autonomous driving scenarios, when a large number of vehicles offload tasks to roadside units (RSUs), they face the problem of computational overload of edge servers and inference delay beyond tolerable limits. To address these challenges, we propose an edge-vehicle collaborative inference acceleration mechanism, namely Model partitioning and Early-exit point selection joint Optimization for Collaborative Inference (MEOCI). Specifically, we dynamically select the optimal model partitioning points with the constraint of RSU computing resources and vehicle computing capabilities; and according to the accuracy threshold set to choose the appropriate early exit point. The goal is to minimize the average inference delay under the inference accuracy constraint. Therefore, we propose the Adaptive Dual-Pool Dueling Double Deep Q-Network (ADP-D3QN) algorithm, which enhances the exploration strategy and experience replay mechanism of D3QN to implement the proposed optimization mechanism MEOCI. We conduct comprehensive performance evaluations using four DNN models: AlexNet, VGG16, ResNet50, YOLOv10n. Experimental results show the proposed ADP-D3QN algorithm reduces average inference delay by 15.8% for AlexNet and 8.7% for VGG16 compared to Edgent algorithm.

In urban temporary congestion or hotspot scenarios, fixed roadside units (RSUs) often fail to provide reliable and efficient communication services due to their static deployment. Unmanned Aerial Vehicles (UAVs) can be rapidly deployed to provide flexible and on-demand communication and sensing support, effectively complementing ground infrastructure. However, UAVs are faced with challenges such as limited coverage, high deployment complexity, and unbalanced communication-sensing performance. These challenges give rise to increased energy consumption and reduced communication efficiency. To address these issues, we propose a UAV energy-efficient deployment method based on Integrated Sensing and Communication (ISAC), which balances performance and energy consumption. Specifically, under UAV energy constraints, we jointly optimize UAV deployment positions and beamforming to maximize communication capacity. We decompose the problem into two subproblems: UAV deployment and beamforming strategy optimization. During the iteration process, the subproblems are respectively solved by using the sparrow search algorithm based on refraction-based learning and successive convex approximation-based iterative algorithm and the first order Taylor expansion method. Simulation results show that the proposed method outperforms the benchmark schemes, achieving approximately a 10.51% improvement in average coverage rate and a 19.83% reduction in UAV energy consumption, while maintaining an effective trade-off between communication coverage and energy efficiency.

Unmanned Aerial Vehicle (UAV)-assisted Vehicular Edge Computing (VEC) has emerged as a novel communication paradigm for compute-intensive and latency-sensitive applications in Intelligent Connected Vehicles (ICVs). In urban hotspots with traffic congestion, UAVs can alleviate the overload on edge servers, enhancing the ICVs’ quality of service (QoS) and safety. However, due to the high mobility of vehicles, effective solutions that can support low-latency and higher QoS for ICVs still lacked. Based on this, we first investigated the UAV deployment problem of maximizing vehicle coverage and system energy efficiency, and we proposed an adaptive population differential evolution (APDE) algorithm to address it. Then, we investigate the service caching and computation offloading problem of minimizing the system energy consumption and task response delay. DRL has effectively solved sequential decision-making in dynamic ICV environments, and we proposed a DRL-based Bias Correction-A3C-Gradient Sharing (BC-A3C-GS) algorithm to address it, which improves the traditional A3C algorithm in bias correction and global model update. Finally, experiment results show that the proposed UAV deployment algorithm can bring about 11.58% vehicle coverage rate improvement, and BC-A3C-GS algorithm can decrease about 30.08% task response delay and 17.4% energy consumption, and increase about 10.73% service cache hit rate.

As a distributed embedded system, vehicular edge computing (VEC) completes various complex Deep neural network (DNN) tasks through network collaboration and communication. However,due to the limited computing power of vehicle processors, vehicles cannot handle increasingly complex DNN tasks. To accurately estimate the execution latency of each layer across different DNN models on heterogeneous devices, we proposed the Extreme Gradient Boosting Tree (XGBoost) algorithm to predict DNN task inference latency. Furthermore, we proposed partitioning and offloading algorithms for both chained DNN tasks and Directed Acyclic Graph (DAG)-type DNN tasks, addressing their unique computational characteristics. For chained DNN tasks, we employ a linear search to determine optimal partitioning points based on predictions from the DNN latency prediction model. For the partitioning and offloading of DAG-type DNN tasks, we construct it as a minimum cut problem under the network flow graph and propose a DNN task partitioning and offloading algorithm based on the highest label pre-stream push (HLPP) algorithm to effectively reduce the cost of task partitioning and offloading. Finally, we used an experimental vehicle equipped with Raspberry and a RSU equipped with Jetson Nano to verify the results. The experiment shows that the DNN latency prediction model based on the XGBoost we proposed can effectively improve the latency prediction accuracy of DNN layer-by-layer execution. At the same time, the division and offloading algorithms for different types of DNN inference tasks can achieve higher task completion rate, lower latency, and lower energy consumption.

The development of novel applications causes increased demands on the computational capabilities of Vehicular Edge Computing (VEC). Current works have introduced Unmanned Aerial Vehicles (UAVs) into VEC to solve the resource-constrained problem. However, given the limited storage of UAVs, the key question is to design the offloading strategy and determine which service programs should be cached. In this article, we propose a three-stage game model that aims at providing a precise analysis of the interaction among the Base Station (BS), the UAV, and the User Vehicle (UV). In stage I, the BS is responsible for determining the cache strategy of the UAV and communicating the price strategy to the UVs. In stage II, the UAV communicates the price strategy to the UVs. In stage III, each UV determines its offloading decision based on the price strategy, to minimize the task execution delay and cost. Compared with current approaches, we cache the frequently requested services in the UAV to satisfy the real-time requirements and use game theory to solve the decision-making, which achieves the effect of reducing the delay and cost. The experiment results are performed to assess the convergence and effectiveness of the proposed algorithm.

In Vehicular Edge Computing (VEC), the high mobility of vehicles and periodic of traffic flow present challenges to the effectiveness of roadside units. Unmanned Aerial Vehicles (UAVs) can serve as aerial base stations to address this issue. Federated Learning (FL) is employed to reduce backhaul load. However, the limited battery and bandwidth of UAVs constrain long-term training capabilities. We propose a collaborative deployment of multiple UAVs to maximize communication coverage, utilizing a Particle Swarm Optimization (PSO) algorithm for optimal deployment decisions. We take into account the mobility of vehicles during vehicle selection to prevent network interruptions. Furthermore, knowledge distillation is used to compress the local model without sacrificing accuracy, thereby reducing transmission overhead and accelerating model convergence. Finally, the Deep Deterministic Policy Gradient - Double Dueling Deep Q-Network (DDPG-D3QN) algorithm addresses optimal vehicle selection and resource allocation in dynamic scenarios. Experimental results demonstrate that our approach effectively meets communication needs in urban areas while enhancing training efficiency and accuracy.

Federated learning (FL) provides an applicable solution for computation offloading in Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing (VEC) by preserving privacy. However, the heterogeneity of clients brings challenges to the generalization of models. Therefore, we propose a federated meta-learning (FML) framework to solve computation offloading for UAV-assisted VEC. In this paper, we are concerned with computation offloading of temporary hotspot regions due to traffic congestion. Firstly, we construct a computation offloading problem with energy-delay tradeoffs and convert the problem to a Markov Decision Process (MDP). Then, we use FML to train personalized models for different vehicles while enhancing the generalization, we propose a Graph neural network-based FL Probabilistic Embedding for Actor-critic RL (GFL-PEARL) algorithm. We model the context as a Directed Acyclic Graph (DAG) and use GNN to reconstruct the inference network of the PEARL algorithm to extract the correlation between contexts fully. We dynamically adjust the task priority during the FML training process to improve the sampling efficiency. Finally, we verify the performance of the algorithm through simulation and physical experiments. Experimental results show that our algorithm can reduce average cost and task overtime rate by 31% and 56% respectively compared with the benchmarks.

As autonomous driving technology advances, the intelligence levels of vehicles continue to increase. However, meeting the demands of autonomous driving in various scenarios requires improved wireless communication and vehicle perception capabilities. Integrated sensing and vehicular edge computing (VEC) technology can provide collaborative perception and computing resources for vehicles. Nevertheless, the high-speed mobility of vehicles leads to frequent changes in channel state information and distances between vehicles and roadside units (RSUs), which poses challenges for low-latency perception processing. Additionally, most research overlooks the impact of vehicle mobility on perception accuracy and lacks effective resource allocation strategies for multi-source perception data fusion tasks. Addressing existing research shortcomings, this paper proposes a deep reinforcement learning(DRL)-based resource allocation method. It first adopts Integrated Sensing and Communication (ISAC) technology in the same frequency band to improve spectrum efficiency and integration. Secondly, it constructs a data fusion model to enhance vehicle perception capabilities and describes the data fusion process between vehicle terminals and RSU terminals. Furthermore, this paper designs a resource allocation algorithm for multi-source perception data fusion tasks with the optimization goal of minimizing task completion delay and system average energy consumption. Considering the mobility of vehicles and the frequent changes in communication channel states, this paper transforms the constructed problem into a Markov decision process (MDP). It solves it using the Improved Dueling Twin Delayed Deep Deterministic policy gradient (ID-TD3) algorithm. Experiment results demonstrate that the proposed strategy can reasonably allocate system resources, effectively reducing task completion delay and system average energy consumption.

In Vehicular Edge Computing (VEC), a reasonable and efficient user association and resource allocation approach is a worthwhile research issue. However, most studies in Internet of Vehicles (IoV) only consider vehicle mobility and IoV communication. Therefore, we propose a user association and resource allocation strategy in Integrated Sensing and Communication (ISAC)-aided VEC. Compared with existing solutions, we consider constraints such as sensing and communication interference, vehicle mobility, Road Side Unit (RSU) sensing performance, and vehicle user quality of service (QoS). By quantifying the sensing and communication performance of RSUs, we construct a user association and resource allocation model with the optimisation objective of maximising the average sensing performance and communication performance of the system. Then, combining double dueling deep Q-network (D3QN) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm, we propose a DRL algorithm based on D3QN-TD3. We represent the user association and resource allocation problem as a Markov Decision Process (MDP) and solve it using the proposed algorithm to obtain the optimal user association, channel allocation, and power allocation strategies. Experimental results show that the proposed algorithm has better performance in terms of downlink transmission rate, radar sensing mutual information, system utility, and task completion rate.

Vehicular edge computing (VEC) has emerged to address the increasing demands on wireless networks posed by massive data and diverse applications in intelligent vehicular services. However, challenges such as low spectrum utilization due to massive sensor deployment, signal degradation from high-speed mobility, and computational resource allocation issues hinder the real-time and secure operation of intelligent vehicles. Therefore, we propose a resource allocation optimization method for VEC based on Integrated Sensing and Communication (ISAC) and Orthogonal Time Frequency Space (OTFS) technologies. Specifically, OTFS is leveraged to multiplex roadside unit (RSU) radar resources, improving spectrum efficiency. We develop comprehensive models for communication, vehicle mobility, sensing, delay, energy consumption, and formulate a delay-minimization resource allocation problem. The problem is modeled as a Markov Decision Process and solved with an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which incorporates prioritized experience sampling and dynamic parameter update to accelerate training and enhance agent-environment interaction. Extensive simulations are conducted in a VEC environment, where the proposed algorithm is compared with DDQN, MADDPG, and MRL-DDPG. The results demonstrate that our method effectively mitigates the impact of vehicle mobility on signal transmission and significantly reduces task completion delay compared with existing algorithms.

In congested road environments, the spectrum resources available for Roadside Units (RSUs) are often insufficient to meet the communication needs of a large number of users simultaneously. To address this, Unmanned Aerial Vehicles (UAVs) can be deployed to supplement spectrum resources temporarily. This article proposes a UAV-assisted Vehicle Edge Computing (VEC) system, integrating UAVs to enhance RSU capabilities in congested scenarios. Traditional spectrum sensing techniques, however, struggle to autonomously monitor vehicular movements and maintain stable spectrum performance. To overcome this, we introduce radar sensing devices into the RSUs to improve perception accuracy and consistency. The integration of radar sensors, while beneficial, creates additional competition for limited system resources. We, therefore, formulate the resource allocation problem considering computation delay, communication rate, and perception data, constrained by spectrum resources, offloading decisions, and time-slot allocations. The problem is modeled as a Markov Decision Process (MDP), and we propose an Improved Multi-Agent Proximal Policy Optimization (IMAPPO) algorithm to optimize resource allocation under these constraints. The experimental results show that compared to baseline algorithms such as A3C, our proposed algorithm reduces the average task processing delay by 15.53%, increases the radar estimation mutual information (MI) by 9.52%, and improves the task completion rate by 4.1%.

UAV-assisted VEC can provide content caching services for vehicles by flying close to the vehicles for vehicle's QoS. However, in real-world scenarios with traffic congestion, due to the battery capacity and cache space limitations of UAVs, low content response speed and high response latency may occur. Based on this, we proposed a dynamic energy consumption-based content caching strategy in UAV-assisted VEC. We use the PSO algorithm to solve the problem and obtain the optimal UAV deployment location. For content caching, we construct a content caching model by considering UAV deployment, vehicle user preference, UAV cache capacity, and UAV energy consumption with the goal of minimizing content request latency. In addition, we propose an IAFSA-based content caching strategy. We reduce the solution space of the fish swarm algorithm, decrease the number of caching decisions, and improve the convergence performance of AFSA by employing dynamic horizons and step sizes. Experimental results show that the proposed IAFSA effectively reduces the average content request latency of the vehicle, improves the cache hit rate, and reduces the number of content return trips. Particularly, the proposed strategy reduces the average content request latency by more than 9.84% compared to the baseline algorithm.

Abstract The Spark computing framework provides an efficient solution to address the major requirements of big data processing, but data partitioning and job scheduling in the Spark framework are the two major bottlenecks that limit Spark’s performance. In the Spark Shuffle phase, the data skewing problem caused by unbalanced data partitioning leads to the problem of increased job completion time. In response to the above problems, a balanced partitioning strategy for intermediate data is proposed in this article, which considers the characteristics of intermediate data, establishes a data skewing model and proposes a dynamic partitioning algorithm. In Spark heterogeneous clusters, because of the differences in node performance and task requirements, the default task scheduling algorithm cannot complete scheduling efficiently, which leads to low system task processing efficiency. In order to deal with the above problems, an efficient job scheduling strategy is proposed in this article, which integrates node performance and task requirements, and proposes a task scheduling algorithm using greedy strategy. The experimental results prove that the dynamic partitioning algorithm for intermediate data proposed in this article effectively alleviates the problem that data skew leads to the decrease of system task processing efficiency and shortens the overall task completion time. The efficient job scheduling strategy proposed in this article can efficiently complete the job scheduling tasks under heterogeneous clusters, allocate jobs to nodes in a balanced manner, decrease the overall job completion time and increase the system resource utilization.

Abstract Radio frequency identification (RFID) is widely applied due to its fast identification speed and non-contact detection. However, the identification process of RFID tags is susceptible to interference from other tags and environmental factors, resulting in inaccurate identification data. To overcome these problem, this paper proposes an improved sensing data cleaning scheme for object localization in edge computing environment. In tag level data cleaning, we use adaptive sliding window and further consider dynamic tags and read rate in continuous reading cycle to adjust the window size timely and appropriately. In the reader level data cleaning, we estimate the tag number based on Chebyshev’s inequality through Markov chain for cyclic control and optimize different time slot lengths to improve the recognition rate. We build an edge computing environment and combine the proposed tag-level cleaning method and reader-level cleaning method to form a comprehensive RFID data cleaning process. Comparative experimental results show that the RFID data cleaning method proposed in this paper can effectively reduce redundant and missing data and improve the accuracy of tag recognition.

Computation offloading in UAV-assisted MEC has been an interesting research issue. However, in the temporary hotspot areas with traffic explosion, effective solutions that support user privacy security and higher quality of service (QoS) are still lacking. Compared with the previous works, we first constructed a distributed federated learning-based computation offloading method for user privacy security to maximize the normalized weighted sum of task response delay and user energy consumption. We formulated a computation offloading problem considering the UAV hover position, resource allocation, computation offloading decision, and user transmission power allocation, which is a challenging time-series mixed integer nonlinear programming problem. Then, we proposed an improved federated deep deterministic policy gradient (IF-DDPG) algorithm to address it effectively, which improved the traditional federated DDPG algorithm in empirical pooling and exploring noise. Finally, experiment results show that the IF-DDPG can significantly decrease the task response delay and user energy consumption.

Intelligent Connected Vehicles (ICVs) need to obtain real-time traffic data from nearby ICVs or remote content providers to ensure safe driving. However, providers are hesitant to share their data due to privacy and benefits concerns. To ensure privacy while improving efficiency of obtaining data, we proposed smart contract-based data sharing among ICVs, and content delivery between ICVs and remote content provider. To solve low willingness to vehicles due to untrustworthy third-party platforms, we use smart contracts to implement access control during data upload and transaction. Then, we propose a one-to-many sharing model based on Stackelberg game to model the interaction between consumers and owners. Consumers adjust their reward strategies with the owners’ optimal strategies to maximize its utility, thus obtaining the nash equilibrium solution. To provide reliable quality of service (QoS) and security guarantee for content delivery, smart contracts regulate the delivery process, facilitating automatic execution under specific conditions. Transaction records audited and stored on blockchain enhance transparency and trustworthiness. Utilizing a delivery utility model that considers benefits, costs, and mining profits, proposed quantum particle swarm optimization (QPSO) algorithm is used to find the optimal solution. We built an EdgeChain testbed, and used BDD-100K dataset to evaluate the performance in utility, access delay, etc. Compared to CTM and MFPA, proposed data sharing algorithm achieves maximum consumer utility. Compared to LRU, PCCM and MARL, when content is 400, proposed content delivery algorithm reduces average access delay by 30.88%, 18.92% and 4.86%, and reduce backhaul load by 50.04%, 47.23% and 3.16%.

In this paper, we plan to use ground-based stations in mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) to provide communication and computation offloading services in disaster areas. However, optimizing the initial number and three-dimensional position of deployed UAVs is a prerequisite for providing computing services to users. Additionally, due to the limited battery and computing power of UAVs, it is a major challenge to rationally design the UAV trajectory during the computational offloading period to ensure communication quality for mobile users and reduce the energy consumption for completing tasks. Thus, we propose a cooperative computation offloading strategy with on-demand deployment of multi-UAV in UAV-aided MEC. The strategy utilizes the predicted user trajectory for UAV deployment on the premise of the minimum path loss of users. Then, to minimize total energy consumption for completing tasks, a joint optimization problem comprising user association strategy, computing resource allocation strategy, and UAV trajectory is proposed, which is a mixed-integer nonlinear program (MINLP). Therefore, to find the suboptimal solution, we use the block coordinate descent method to solve the problem. Numerical results show that the proposed algorithm can efficiently reduce the path loss by up to 18.55% and the total energy consumption by 18.28% compared to the benchmarks.

The convergence of unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks and blockchain transforms the existing mobile networking paradigm. However, in the temporary hotspot scenario for intelligent connected vehicles (ICVs) in UAV-aided MEC networks, deploying blockchain-based services and applications in vehicles is generally impossible due to its high computational resource and storage requirements. One possible solution is to offload part of all the computational tasks to MEC servers wherever possible. Unfortunately, due to the limited availability and high mobility of the vehicles, there is still lacking simple solutions that can support low-latency and higher reliability networking services for ICVs. In this article, we study the task offloading problem of minimizing the total system latency and the optimal task offloading scheme, subject to constraints on the hover position coordinates of the UAV, the fixed bonuses, flexible transaction fees, transaction rates, mining difficulty, costs and battery energy consumption of the UAV. The problem is confirmed to be a challenging linear integer planning problem, we formulate the problem as a constrained Markov decision process. Deep Reinforcement Learning (DRL) has excellently solved sequential decision-making problems in dynamic ICVs environment, therefore, we propose a novel distributed DRL-based P-D3QN approach by using Prioritized Experience Replay strategy and the dueling double deep Q-network (D3QN) algorithm to solve the optimal task offloading policy effectively. Finally, experiment results show that compared with the benchmark scheme, the P-D3QN algorithm can bring about 26.24% latency improvement and increase about 42.26% offloading utility.

In vehicular edge computing, Unmanned Aerial Vehicles (UAVs) have become a feasible solution for addressing high deployment costs faced by base stations in congested roads during peak hours. However, UAVs cannot cache all requested content due to limited storage. Hence, we propose a content caching strategy based on user preference predictions. To address resource consumption and user privacy concerns during the training process, we propose a user preference prediction model based on hierarchical federated learning training. Specifically, we employ a hierarchical clustering approach to partition user vehicles and UAVs into multiple clusters and utilize hierarchical federated learning to train prediction models within each cluster. Furthermore, to tackle the joint optimization problem of content caching and bandwidth allocation, we propose I-MADDPG, an improved multi-agent deep deterministic policy gradient algorithm. It determines the next continuous action based on the reward value at the current moment and the average reward value in the iteration period as reference parameters. The experimental results demonstrate that the proposed algorithm has significantly enhanced training efficiency compared to the baselines. Additionally, it has improved cache hit rate and reduced content request delay through effective resource allocation.

Massive map data transmission and the strict demand for the privacy of high-precision maps have brought significant challenges to the cache of high-precision maps in intelligent connected vehicles (ICV). Federal learning (FL) was introduced to reduce the pressure on the edge network and protect privacy. But the high dynamics of cars and limited resources lead to low accuracy and high training delay. We propose a joint optimization scheme of participant selection and resource allocation for federated learning. In each time slice, vehicles are determined whether to participate in training, which minimizes long-term training delay with limited energy consumption. To meet the delay and privacy requirements of high-precision map caching, we present an edge cooperative caching scheme based on federated deep reinforcement learning (F-DRL), which aims to achieve dynamic adaptive edge caching while protecting user privacy. The collaborative caching model is formulated as a Markov decision process (MDP). Dueling Deep Q Network (Dueling-DQN) is used to solve the optimal strategy, and federal learning is used for training. Enough comparative experiments to evaluate the performance of the proposed schemes. The aspects of reliability, cache hit rate, and training accuracy prove that the method effectively improves the training parameters of federated learning while meeting a high-precision map cache’s delay and reliability requirements.

The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model's security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system's multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100% successful transmission probability and 0.01~0.05 λ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.

Abstract In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds.

The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access control’s accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.

Abstract There are many research problems in cloud replica management such as low data reliability, unbalanced node load and large resource consumption. The strategy and status of replica creation, replica placement and replica selection are analyzed. The replica creation based on access tendency (DRC-AT), the replica placement based on user request response time and storage capacity (DRP-RS) and the replica selection based on response time (DRS-RT) are proposed. The DRC-AT algorithm introduces the two parameters of file popularity and period value of file popularity, calculates the file access tendency periodically and decides the creation and deletion of the replica of the file according to the size of the file access tendency. The DRP-RS algorithm evaluates the user’s request response time and storage capacity to select the best node set to place the replica. The DRS-RT algorithm returns to the user the node with the strongest service capability that contains the user’s requested data. Experiments show that the algorithm can improve the speed of data reading by the client, improve the resource utilization, balance the load of the node and improve the overall performance of the system.

In recent years, Sina microblog becomes an important social network platform for users to not only form personal website but share, deliver and keep interest messages via the relationships between users. However, with larger number of users in social networks, the problem of ‘information overload’ has been increasingly highlighted. In this case, community discovery has been the urgent requirements in microblog social networks. In this paper, a microblog community discovery algorithm based on tag correlation and interactive behaviors is presented. For the selection of exact user tags, the model of maximal marginal relevance is used and improved to enrich the variousness and novelty of user interest tags. The method of edge-similarity computation is improved to suit for microblog community discovery. Hadoop platform is applied for data process, which greatly decreases the running time of experiments. Finally, extensive experiments are provided to demonstrate the performance of proposed algorithm. The results indicate that the performance of proposed algorithm is better than that of benchmark algorithms for microblog social networks. The proposed algorithm for community discovery can identify the users belonging to multi communities or no community so that the results of community discovery are more accurate and more suitable for actual environment.

How to reduce energy consumption, improve resource utilization and put forward efficient resource management model so as to improve the media cloud performance and mobile users’ quality of service (QoS) is the problem needed to be addressed. Our proposed media cloud distributed scheduling model aims to maximize the utility of media cloud. The media cloud distributed scheduling policy for resource-intensive mobile application includes media service provisioning and cloud resource scheduling among media cloud datacenter. The media cloud service scheduling optimization algorithms include two sub-algorithms. The practical example of video streaming service for mobile users is also given. The experiments study the performance of media cloud distributed scheduling algorithm and related algorithms. The experiment results show that proposed algorithm has better performance than related algorithms.

The paper proposes an efficient load-balancing aware cloud resource scheduling approach for mobile users.The proposed approach augments local cloud service pools with public cloud to increase the probability of meeting the service level agreements.The proposed problem is divided by public cloud service scheduling and local cloud service scheduling.The system status information is used in the hybrid mobile cloud system such as the preferences of mobile applications, energy, server load in cloud data center to improve resource utilization and quality of experience of mobile user.Therefore, the system status of hybrid mobile cloud is monitored continuously.The mathematical model of the system and optimization problem is given.An example for hybrid cloud-based mobile multimedia application is also presented.Through extensive experiments, the paper evaluates our algorithm and other approaches from the literature under different conditions.The results of the experiments show our approach has better performance than the approaches from the literature.

The paper presents a hybrid cloud service provisioning and selection optimization scheme, and proposes a hybrid cloud model which consists of hybrid cloud users, private cloud and public cloud. This scheme aims to effectively provide cloud service and allocate cloud resources, such that the system utility can be maximized subject to public cloud resource constraints and hybrid cloud users constraints. The paper makes use of a utility-driven approach to solve interaction among private cloud user, hybrid cloud service provider and public cloud provider in hybrid cloud environment. The paper presents hybrid cloud service provisioning and selection algorithm in hybrid cloud. The hybrid cloud market consists of hybrid cloud user agent, hybrid cloud service agent and hybrid cloud agent, which represent the interests of different roles. The experiments are designed to compare the performance of proposed algorithm with the other related work.

In recent years, the research on caching in cloud environment has become an important research topic, and it has profound meaning to research the cache replacement algorithm in hybrid Cloud. There aren't enough considerations on some aspects, such as the selection of pending cache files, the prefetching of pending cache files among different clouds and the cost of recovery of files. Considering those shortages, this paper proposes an optimized LRU algorithm based on pre-selection and cache prefetching of files. This algorithm determines whether the file is to meet the pre-selection and cache prefetching conditions before adding a cache file, and it implements the LRU cache replacement algorithm which is based on priority. The algorithm divides the cache into multiple priority queues, and uses the LRU cache replacement algorithm to select the replacement file in each queue. Then select the files in each priority and put them together, select the file to perform replacement operation which has minimum probability of being accessed again. Compared with three typical cache replacement algorithm GD-Size, LRU, LFU, experimental results show that the cache replacement algorithm in this paper not only effectively save cost, but also greatly enhance the byte hit rate, delay savings rate and cache hit rate.

Research on MapReduce tasks scheduling method for the hybrid cloud environment to meet QoS is of great significance. Considering that traditional scheduling algorithms cannot fully maximize efficiency of the private cloud and minimize costs under the public cloud, this paper proposes a MapReduce task optimal scheduling algorithm named MROSA to meet deadline and cost constraints. Private cloud scheduling improves the Max-Min strategy, reducing job execution time. The algorithm improves the resource utilization of the private cloud and the QoS satisfaction. In order to minimize the public cloud cost, public cloud scheduling based on cost optimization selects the best public cloud resources according to the deadline. Experimental results show that the proposed algorithm in this paper has less job execution time, higher QoS satisfaction than the Fair scheduler and FIFO scheduler. It also has more cost savings and shorter job completion time than recent similar studies.

Hadoop is a popular cloud computing software, and its major component MapReduce can efficiently complete parallel computing in homogeneous environment. But in practical application heterogeneous cluster is a common phenomenon. In this case, it's prone to unbalance load. To solve this problem, a model of heterogeneous Hadoop cluster based on dynamic load balancing is proposed in this paper. This model starts from MapReduce and tracks node information in real time by using its monitoring module. A maximum node hit rate priority algorithm (MNHRPA) is designed and implemented in the paper, and it can achieve load balancing by dynamic adjustment of data allocation based on nodes' computing power and load. The experimental results show that the algorithm can effectively reduce tasks' completion time and achieve load balancing of the cluster compared with Hadoop's default algorithm.

The optimization of task scheduling in Hadoop environment is an important research topic. The result of task scheduling affects the system performance and resource utilization. The existing task scheduling algorithm is lack of consideration at the cache level, which makes the performance of the task greatly affected. Therefore, this paper proposes an improved task scheduling algorithm based on cache locality and data locality. Firstly section matrix and weighted bipartite graph are constructed according to the relation between resources and tasks. Then the bipartite graph matching is used to realize map task scheduling for optimizing the local cache and data locality and reducing the data transmission amount during task execution process. The experimental results show that the proposed algorithm can effectively improve the data locality and system performance, which is better than other two algorithms.

This article proposes context-aware integrated scheme for mobile cloud service allocation, which can provide desirable cloud services to mobile cloud clients according to the mobile cloud contexts. The article makes use of various contexts information in the mobile cloud environment, such as the mobile cloud user's preferences, the battery levels and the parameters of cloud datacenter servers to improve the performance of mobile cloud. Interplay coupling of the mobile cloud users and the cloud datacenter supplier is used to achieve global optimization of the mobile cloud system. The article integrates energy-based service provisioning, cloud virtual resource allocation and dynamic load balancing. The integrated scheme can adapt to dynamic context information changes of the mobile cloud system such as device energy consumption, bandwidth and server load without compromising mobile application's quality of service. Based on the proposed model, the context-aware integrated mobile cloud service allocation algorithm is proposed, it uses the mobile cloud service profile to select the services among the available service suppliers to enhance the mobile cloud user's quality of experience. The efficiency of the context-aware integrated mobile cloud service allocation algorithm is tested by the experiments.

In the paper, we consider the hybrid cloud model used for cloud bursting, when the computational capacity of the private cloud provider is insufficient to deal with the peak number of customers’ applications, the private cloud will rely on the resources leased from public cloud providers for the execution of private cloud applications. The paper proposes the model and algorithm of hybrid cloud scheduling optimization for cloud bursting. Public cloud providers and private cloud users communicate by the hybrid cloud marketplace. The hybrid cloud scheduling optimization is conducted at different levels. According to the model formulation and mathematic solutions of hybrid cloud scheduling, hybrid cloud scheduling algorithms for cloud bursting are proposed, which includes the routines of public cloud optimization, private cloud application optimization and private cloud job optimization. In the simulations, compared with other related algorithm, our proposed hybrid cloud scheduling algorithms achieve the better performance.

The explosive growth of cloud computing technology, accompanied with the wide spread use of virtualization technology has brought data centers major problems such as power or energy consumption. with these problems beside, It is imminent to take measures to do energy saving work. In this paper, we'll present some recent researches on power/energy management and analyze some strategies of energy saving.

Energy consumption is the main obstacle to the green cloud computing, particularly with the global climate warming and data center scale expanding. The green computing is gaining more and more attention because energy consumption increased rapidly. We propose a cloud computing management framework in order to ensure energy consumption of cloud computing to be a minimum. The framework uses virtual date center instead of virtual machine service mode of traditional service providers, and partition the virtual data center used in the management framework is based on the network communications of virtual machines. Then the virtual data center partition is placed into corresponding green data center in order to maximize revenue of providers and minimize the carbon emissions.

Abstract This paper presents an optimization approach for decentralized Quality of Service (QoS)‐based scheduling based on utility and pricing in Grid computing. The paper assumes that the quality dimensions can be easily formulated as utility functions to express quality preferences for each task agent. The utility values are calculated by the user‐supplied utility function that can be formulated with the task parameters. The QoS constraint Grid resource scheduling problem is formulated into a utility optimization problem. The QoS‐based Grid resource scheduling optimization is decomposed into two subproblems by applying the Lagrangian method. In the Grid, a Grid task agent acts as a consumer paying for the Grid resource and the resource providers receive profits from task agents. A pricing‐based QoS scheduling algorithm is used to perform optimally decentralized QoS‐based resource scheduling. The experiments investigate the effect of the QoS metrics on the global utility and compare the performance of the proposed algorithm with other economical Grid resource scheduling algorithms. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents multiple QoS-bsed Grid resource scheduling models and solves the scheduling problems using optimization techniques. Each of grid task agent’s diverse requirements is modeled as a quality of service (QoS) dimension, associated with each QoS dimension is a utility function that defines the benefit that is perceived by a user with respect to QoS choices in that dimension. The paper proposes the idea of decomposing a global optimization problem in multiple QoS based resource scheduling into two subproblems: task agent optimization and resource agent optimization. It simplifies the problem and makes it mathematically tractable.The experiments show that optimal multiple QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation.

The paper presents a market oriented resource allocation strategy for grid resource. The proposed model uses the utility functions for calculating the utility of a resource allocation. This allows the integration of different optimization objectives into allocation process. This paper is target to solve above issues by using utility-based optimization scheme. We decompose the optimization problem into two levels of subproblems so that the computational complexity is reduced. Two market levels converge to its optimal points; a globally optimal point is achieved. Total user benefit of the computational grid is maximized when the equilibrium prices are obtained through the service market level optimization and resource market level optimization. The economic model is the basis of an iterative algorithm that, given a finite set of requests, is used to perform optimal resource allocation. The experiments show that scheduling based on pricing directed resource allocation involves less overhead and leads to more efficient resource allocation than conventional round robin scheduling.

In this paper, we apply market mechanism and agent to build grid resource management, where grid resource consumers and providers can buy and sell computing resource based on an underlying economic architecture. All market participants in the grid environment including computing resources and services can be represented as agents. Market participant is registered with a Grid Market Manager. A grid market participant can be a service agent that provides the actual grid service to the other market participants. Grid market participants communicate with each other by communication space that is an implementation of tuple space. In this paper, Grid agent model description is given. Then, the structure of Grid Market is described in detail. The design and implementation of agent oriented and market oriented grid resource management are presented in this paper.