Coprime arrays are a promising array structure for near-field THz massive MIMO communication. However, they pose a significant challenge for traditional channel estimation methods to acquire high-precision channel state information (CSI) and user location information (ULI). To address this problem, a super-resolution sparse reconstruction based near-field channel estimation method is proposed in this paper. First, leveraging the sparsity of the channel, a high-resolution location parameter estimation algorithm based on fourth-order cumulants is developed to acquire the angles and distances of users. In the algorithm, an observation matrix comprising the largest continuous virtual elements is constructed from the fourth-order cumulant matrix, after which the spatially smoothed MUSIC technology is applied to estimate super-resolution ULI. Then, to enhance the robustness under challenging conditions such as low signal-to-noise ratio (SNR) and weak path gains, a channel reconstruction algorithm based on iterative path separation is proposed for channel estimation by utilizing the estimated user location parameters. The algorithm employs successive cancellation of the strong path signals to sequentially estimate the gain of each path, and then reconstructs the MIMO channel matrix based on the estimated path parameters. Finally, simulation results show that the proposed method can achieve excellent performance in user positioning and channel estimation with moderate pilot overhead, verifying its effectiveness and advantages in near-field terahertz massive MIMO systems.

Vehicular Edge Computing (VEC) reduces latency by offloading vehicle generated tasks to Roadside Unit (RSU), unlocking vast opportunities for in vehicle electronics commercial services. Existing studies on task offloading in multi-RSU scenarios suffer from two major gaps. First, the similar sub-tasks across different tasks themselves have not been sufficiently investigated. This oversight leads to redundant computing, thereby undermining system efficiency. Second, and more critically, the load imbalance stemming from the uneven distribution of vehicles is further exacerbated by the neglect of similar sub-tasks. This paper focuses on redundant computation of similar sub-tasks in multi-RSU scenarios. We characterize similar sub-tasks of overlapping tasks, thereby capturing the redundancy that exists across these overlapping tasks. We then model the offloading of similar sub-tasks as a multi-objective Mixed Integer Nonlinear Program (MINLP) problem that simultaneously minimizes latency, energy consumption, and load imbalance. The original problem is approximated by a weighted-sum multi-objective problem, and we prove by contradiction that any optimal solution to this weighted-sum problem is a Pareto-optimal solution to the original multi-objective problem. To solve the constructed MINLP, we propose the Multi-RSU Distributed Shared Offloading (MRDSO) scheme. In this scheme, discrete variables are fixed based on Benders partitioning theorem, reducing the problem to a linear-programming sub-problem of the continuous variables. By proving the convexity of this sub-problem, we have demonstrated that a global optimum exists for the original MINLP problem. We then design two algorithms to solve it. Finally, experimental results confirm that this scheme reduces average system computing latency and energy consumption while maintaining multi-RSU load balance. Compared to the existing SSO, LAGO, RO, the proposed MRDSO has at least 54.8% improvement in the system cost.

In green transportation systems, Vehicular Edge Computing (VEC) is playing a critical role by enabling real-time responses to compute-intensive tasks. Furthermore, incentivizing diverse stakeholders in VEC to share resources and ensuring equitable resource allocation are pivotal to achieving sustainability. In existing studies, a key limitation is that resource price is often simplified to a static decision factor, thus failing to function as a dynamic indicator that reflects real-time load state of the RSUs and the essential balance between the total utility of RSUs and vehicle user experience (QoE). At the same time, the issue is more challenging when facing the load balancing within multi-RSU. To resolve this issue, the paper builds a differentiated resource price model and designs a pricing strategy that combines vehicle QoE and RSUs cost, in order to maximize the total utility of RSUs. Specifically, the paper propose an Incentive-Driven Collaborative Resource Management (IDCRM) algorithm, which integrates the Soft Actor-Critic (SAC) based Computing Resource Allocation and Pricing (SCRAP) algorithm for dynamic resource allocation and pricing by individual RSU, and RSUs cooperative offloading algorithm to address multi-RSU load balancing. The experimental results demonstrate that our proposed IDCRM effectively maximizes the provider’s revenue while ensuring vehicle QoE and significantly alleviates the resource bottleneck of a single RSU through a collaborative mechanism, achieving efficient load balancing across multi-RSU.

Enabled by Mobile Edge Computing (MEC) equipped on Base Station (BS), Collaborative Vehicle-Infrastructure Systems (CVIS) can provide efficient and reliable computing services for mobile vehicles. Vehicles can achieve intelligent applications such as autonomous driving by forward offloading tasks to base stations. However, most existing studies focus on the BS merely as a task receiver and integrator in CVIS, and neglecting its role as a task generator for information processing. When the BS is overloaded, CVIS will deteriorate drastically. Reverse offloading from BS to idle vehicles can relieve the pressure. Nonetheless, with the huge volume of tasks generated by both some task vehicles and the BS, how to select appropriate offloading and resource allocation strategies is a challenge. The situation will become more complex when facing heterogeneous nodes, i.e., task vehicles, the BS, and the idle vehicles in the range of the task vehicle or in the range of the BS but out of the task vehicle in dynamic scenarios. Thus, we propose an optimization problem joint multiple task offloading and resource partitioning to maximize the average task satisfaction of the system. To address the above proposed optimization problem, we propose Two-way Offloading & Partitioning (TOP) strategy, where a Two-way Collaborative Edge Node Dividing and Offloading Algorithm determines the cooperative edge nodes for different tasks and obtains the offloading strategy for each task set. Furthermore, we optimize the resource partitioning using the Genetic Algorithm to avoid resource wastage while enhancing the overall satisfaction of the system. Extensive experimental results show that our proposed TOP strategy improves average system satisfaction by up to 33% compared to other baseline strategies.

The rapid advancement of the Internet of Vehicular has enabled a wide range of vehicular applications, intensifying the demand for low-latency and high-efficiency computation. However, the limited onboard computing capabilities and the highly dynamic nature of vehicular networks present significant challenges for task offloading and resource allocation. To tackle these challenges, Vehicular Edge Computing (VEC) incorporates Roadside Units (RSU) not only as computing nodes but also as relay nodes capable of multi-hop task forwarding, effectively extending the offloading scope and mitigating transmission constraints in sparse or high-mobility scenarios. Moreover, due to the diversity of task demands and fluctuating resource availability, it becomes essential to dynamically adjust task priorities to ensure timely task completion and efficient resource utilization. In this paper, we propose a novel Computing Offloading and Resource Allocation strategy under Relay Collaboration (CORA-RC). CORARC exploits the dual roles of RSU as computing and relay nodes to extend the offloading range and improve flexibility, while jointly optimizing task offloading, dynamic priority adjustment, and resource allocation. Simulation results demonstrate that CORA-RC ensures the timely completion of security-related tasks while effectively reducing the average task delay, thereby achieving the lowest average weighted cost among all compared strategies.

With the development of Vehicle Edge Computing (VEC), mobile payment for vehicles has become feasible. However, challenges like the lack of incentive mechanisms for Service Providers (SPs) hinder its widespread adoption. To address this, we propose an incentive mechanism for SPs that operates independently of offloading. Our approach constructs an optimization problem aimed at maximizing the total utility of SPs while meeting users’ basic Quality of Experience (QoE) constraints, considering server latency, energy consumption, and bandwidth costs. We introduce the Soft Actor-Critic based Computational Resource Allocation and Pricing (SCRAP) algorithm to solve this problem. Extensive experiments demonstrate that SCRAP improves SP utility by at least 7.5

Vehicular Edge Computing is a new computing paradigm that enables real-time response to vehicular applications and servers by performing data processing on edge computing devices near the vehicle. However, on the one hand, the random distribution and the mobility of vehicles may lead to load unbalance among different Roadside Units (RSUs), and some tasks may not be able to get timely response due to inadequate computing resources and communication resources in the high-load RSU areas. On the other hand, considering the different urgency of the tasks, the service quality of the system will be seriously affected if these tasks are not treated indistinguishably. To address the above challenges, this paper constructs a priority-aware task offloading and computing&communication resources allocation problem in a general scenario of unbalanced load among multi-RSUs, aiming at minimising the average delay. In the problem, considering the absence of communication resources, the relay vehicle is used to offload the subtasks of splittable tasks to the RSUs that are in the neighbouring and low-load. Moreover, to take full advantage of computing resources, the task can be reasonably split into at most four parts and processed in parallel on a relay vehicle, a current RSU, a neighbouring RSU and a local vehicle. To solve the problem, a Split-Hop Offloading and Resources Allocation Strategy (SHORAS) based on an improved particle swarm optimisation algorithm is proposed, which uses a penalty function to incline resources towards high priority tasks. Simulation results show that SHORAS improves 24% in terms of the total system delay and effectively reduces the processing delay in the high-load areas compared to other strategies, while ensuring the delay requirements of high priority tasks.

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviors and ensure robustness.

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud-edge-end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCORAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Mobile Edge Computing (MEC) effectively alleviates the pressure on limited in-vehicle computing resources and energy supply caused by computation-intensive vehicular applications. However, the uneven spatial distribution of users leads to load imbalance among adjacent MEC servers, significantly increase the latency and energy consumption costs for vehicles. Therefore, achieving optimal configuration of available computing resources in MEC servers to accomplish the goal of low-latency and low-energy task offloading has become a critical issue to address. To tackle this problem, this study proposes a Multi-RSU Load Balancing (MRLB) strategy based on multi-hop network technology. This strategy dynamically allocates computing tasks to neighboring RSU server clusters with available computing resources through task segmentation and computation offloading mechanisms. Meanwhile, adaptive resource allocation strategies are implemented based on task quantity and task scale characteristics. Specifically, this study designs a multi-RSU collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) to solve the optimal offloading decision. Additionally, by integrating the Lagrange multiplier method and Sequential Quadratic Programming (SQP) algorithm, the joint optimization of imbalanced task segmentation decisions and optimal CPU frequency allocation decisions for RSU servers is achieved. Experimental results demonstrate that the proposed method can achieve efficient multi-RSU resource allocation and ensure coordinated optimization of both system latency and energy consumption costs across diverse device conditions and varying network scenarios, particularly in load-imbalanced situations.

The existing Siamese trackers express visual tracking through the cross-correlation operation between two neural networks. Although they dominated the tracking field, their adopted pattern caused two main problems. One is the adoption of the deep architecture that drives the Siamese tracker to sacrifice speed for performance, and the other is that the template is fixed to the initial features; namely, the template cannot be updated timely, making performance entirely dependent on the Siamese network’s matching ability. In this work, we propose a tracker called SiamMLG. Firstly, we adopt the lightweight ResNet-34 as the backbone to improve the proposed tracker’s speed by reducing the computational complexity, and then, to compensate for the performance loss caused by the lightweight backbone, we embed the SKNet from the attention mechanism to filter out the valueless features, and finally, we utilize the gradient-guide strategy to update the template timely. Extensive experiments on four large tracking datasets, including VOT-2016, OTB100, GOT-10k, and UAV123, confirming SiamMLG satisfactorily balance performance and efficiency, where it scores 0.515 on GOT-10k while running at 55 frames per second, which is nearly 3.6 times that of the state-of-the-art method.

In recent years,federated learning(FL)has been adopted in mobile edge computing(MEC)to protec tuser privacy.However,insomecases,inaddition to performing FL to process private data,users also need to process other non-private data.Therefore,how to integrate private data and non-private data in a MEC system for comprehensive processing is an issue worth studying. In this paper, we propose an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN) to process both FL tasks and offloadable MEC tasks of UEs, where the UAV charges UEs via wireless power transfer (WPT) technology, executes offloaded tasks, and aggregates the FL model parameters. To minimize energy consumption of the UAV, we formulate a problem to jointly optimize the hovering position of UAV, the WPT power, the proportion of UAV's computing resources, the percentage of offloaded tasks, and the time scheduling of FL under the energy harvesting constraint. The problem is divided into three subproblems with the aid of block coordinate descent (BCD) method. These subproblems are solved by Lagrange method and heuristic algorithm respectively. Numerical results show our algorithm can reduce the energy consumption of the UAV with low time complexity compared with several benchmarks.

Internet of Vehicles (IoV) is paving the road for the new generation of Intelligent Transportation Systems (ITS), and Mobile Edge Computing (MEC) is enabling IoV to efficiently handle the computation-intensive and time-sensitive tasks. However, this has introduced new challenges such as maximizing computing resources, allocating resources fairly for multi-source tasks concurrently, and dividing tasks for parallelly processing to minimize the latency. To face these challenges, a three-dimensional road vehicle mobility model is constructed, and the problem of offloading strategy and resource allocation among multiple vehicles served by one Road Side Unit (RSU) is investigates to minimize the average latency of multi-source tasks while satisfying the quality of service requirements. To address the Non-deterministic Polynomial-time hardness (NP-hardness) of the problem, we design a Relay-Assisted Parallel Offloading (RAPO) strategy to obtain the optimization solution. Extensive experimental results show that the RAPO strategy introducing relay-assisted nodes can enhance performance in poor scenarios and ensure low-latency multi-tasking under various conditions, especially reducing latency by 39% compared to local computing.

In Internet of Vehicles, a multi-factor weighted relay-node selection method is proposed to address the problem that single-hop relay node performance is optimal but its multi-hop is not optimal, which exists on the greedy algorithm in 3D mountain scenarios. The ration for the algorithm is demonstrated by conducting a comprehensive analysis of the impact of 3D mountain features on the relay-node selection method. The experimental results in simulations on real-world map show that the designed algorithm performs better compared with the improved greedy algorithm.

The Beyond 5th Generation/6th Generation (B5G/6G) wireless communication technology, characterized by ultra-low latency and ultra-multiple connections, and B5G/6G edge networks provide a new approach to solve delay-sensitive and computation-intensive vehicle applications in Intelligent Transportation Systems (ITS). However, due to the high mobility of vehicles, it becomes challenging to provide mobility-enabled resource management and delivery tasks from multiple vehicle users to Base Station (BS) in B5G/6G edge networks. Therefore, we investigate a multi-vehicle user and multi-BS collaborative offloading system in B5G/6G edge networks, and propose a joint optimization scheme for collaborative offloading, unequal task splitting and CPU resource allocation. In this scheme, tasks from vehicle users can be partially offloaded to associated BS, and can be further split and offloaded to adjacent BS with multi-hop network technology, thereby minimizing the weighted sum of latency and energy consumption. Thus, a Mixed Integer Nonlinear Optimization Problem (MINLP) is constructed. To address this issue, we propose a two-level alternating iterative framework based on a two-layer co-offloading architecture and Sequential Quadratic Programming algorithm (SQP). In the upper level, we introduce a multi-BS collaboration algorithm at the edge layer and develop a collaborative offloading strategy between vehicle users and the edge layer, utilizing Game Theory (GT). In the lower level, based on the SQP algorithm, the optimal task splitting ratio and the optimal CPU frequency allocation strategy for each vehicle user task are solved. Simulation results demonstrate that the proposed algorithm not only effectively reduces system costs, but also excels in reducing the system latency or energy consumption when considered separately.

In the vehicular networks (VN) assisted by the integration of sensing and communication (ISAC), rapid processing of data from sensors is a necessary condition to ensure safe driving and enhance user experience. Utilizing the computational resources of the roadside unit (RSU) can effectively reduce the task processing delay. However, in some areas of the road, uneven distribution of task-vehicles can lead to severe load imbalance in neighbouring RSUs, and these tasks often have different delay requirements. The tasks in the high-load area can be offloaded to the low-load area to balance the load. We use the idle-vehicles in the low-load RSU area that are close to the task-vehicles as relays to hop and offload the tasks to the low-load RSUs. On the other hand, in order to satisfy the delay requirements of the heterogeneous tasks, this paper proposes the priority ordering of the heterogeneous tasks, the more delay-sensitive tasks require more resources to meet their delay requirements, i.e., the higher the priority. In order to both satisfy the delay requirements of heterogeneous tasks and maintain a small average system delay, we establish the optimization problem of minimizing the weighted average system delay and solve it by using the Relay Hopping and Differentiated Task Prioritization (RHATP) algorithm. Simulation results show that under the condition of guaranteeing the delay requirement of high-priority tasks, the strategy can achieve lower system delay and effectively reduce the processing delay in high-load areas. And it still maintains stable performance in different scenarios.

In recent years, Internet of Vehicles (IoV), as a supporting technology for Intelligent Transportation System (ITS), is flourishing with the emergence and development of new technologies such as edge computing, 5G communication, and Artificial Intelligence (AI). However, the more complexity of wireless channels and vehicle distribution in 3D scenario brings a great challenge for relay-node selection in ITS. In this paper, we focus on how to alleviate the problem that the decline of two-hop distance and two-hop connection probability caused by the relative fading of inter-layer communication radius in 3D scenario with Vehicle-to-Vehicle (V2V) communication. To face this challenge, we develop a Relay-node Selection method based on Weighted Strategy for Overpass scenario (RSWSO). The simulation results show that RSWSO achieves improvement in two-hop distance, and an increase of up to 11.2% in terms of two-hop connection probability.

The rise of artificial intelligence and the Internet of Things (AIoT) has paved the way for the resource utilization in Vehicular Edge Computing (VEC) networks. However, vehicles willingness to participate in collaboration still needs to be investigated due to the high speed dynamics of the network. In this paper, we discuss the case of multiple Task Vehicles (TaVs) competing for resources on multiple Service Vehicles (SeVs) proxied by an RSU. To maximize the utility of both parties, reasonable resource prices and task offloading volume are necessary. Firstly, we formulate the interactions between SeVs and TaVs as a two-stage Stackelberg game. Then, we propose an Optimal Differentiated Pricing Approach (ODPA) to find the optimal solution. It consists of two parts. The first part determines the optimal resource price and task offloading volume by taking into account the energy consumption of SeVs and the delay-energy savings of TaVs. The second part matches SeVs with suitable TaVs to maximize the utility of TaVs and SeVs. Simulation results demonstrate that ODPA increases the overall utility of SeV and TaV by at least 6% and 10%, respectively, reduces the energy consumption and task latency of TaV compared to other benchmark approaches.

Vehicle Edge Computing (VEC) is a promising paradigm for efficiently processing massive amounts of data from sensors and stable responses to diverse applications from vehicles. In the usual scenario, the data needs to be transmitted between different nodes and the applications can be represented as inter-dependent tasks. Moreover, the subtasks in each task have diverse interdependencies and each task has a different deadline. Therefore, in order to make the task offloading decision, two questions must be answered: when and where each subtask should be offloaded according to its dependencies. This paper formulates the execution sequence of subtasks by modeling the relationships between subtasks with Directed Acyclic Graph (DAG). Meanwhile, to satisfy the deadline of applications, the computing resources of idle vehicles are sufficiently utilized, and the computing resources on the VEC server are efficiently allocated to tasks. To optimize the overall performance of the system, this paper constructs an optimization problem of minimizing average task completion delay, and proposes the Optimal Offloading Strategy and Resource Allocation for Multi-Dependent Tasks (OSRA-MDT) scheme to solve this optimization problem. Simulation results show that our proposed OSRA-MDT scheme has achieved up to $24 \%$ improvement in reducing the average task completion latency of the system compared to the baseline scheme.

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles, people, transportation infrastructure, and networks, thereby realizing a more intelligent and efficient transportation system.The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topological structure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3D roads can benefit the description of topological changes for IoV.This paper proposes a 3D general road model based on discrete points of roads obtained from GIS.First, the constraints imposed by 3D roads on moving vehicles are analyzed.Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed and acceleration are studied.Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features of the 3D road network model and the road features.Real GIS data from a specific region of Beijing is adopted to create the simulation scenario, and the simulation results validate the general 3D road network model and the recognition method.Therefore, this work makes contributions to the field of intelligent transportation by providing a comprehensive approach to modeling the 3D road network and its topological changes in achieving efficient traffic flow and improved road safety.