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Data driven vehicular heterogeneity based intelligent collision avoidance system for Internet of Vehicles(IoV) 认领 引用 被引量：1
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作者 Iqra Adnan Tariq Umer +3 位作者 Ahmad Arsalan Maryam M.Al Dabel Ali Kashif Bashir Arooj Ansif 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期180-197,共18页
The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and... The Internet of Vehicles(IoV)is an emerging technology that aims to connect vehicles,infrastructure,and other devices to enable intelligent transportation systems.One of the key challenges in IoV is to ensure safe and efficient communication among vehicles of different types and capabilities.This paper proposes a data-driven vehicular heterogeneity-based intelligent collision avoidance system for IoV.The system leverages Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication to collect real-time data about the environment and the vehicles.The data is collected to acknowledge the heterogeneity of vehicles and human behavior.The data is analyzed using machine learning algorithms to identify potential collision risks and recommend appropriate actions to avoid collisions.The system takes into account the heterogeneity of vehicles,such as their size,speed,and maneuverability,to optimize collision avoidance strategies.The proposed system is experimented with real-time datasets and compared with existing collision avoidance systems.The results are shown using the evaluation metrics that show the proposed system can significantly reduce the number of collisions and improve the overall safety and efficiency of IoV with an accuracy of 96.5%using the SVM algorithm.The trial outcomes demonstrated that the new system,incorporating vehicular,weather,and human behavior factors,outperformed previous systems that only considered vehicular and weather aspects.This innovative approach is poised to lead transportation efforts,reducing accident rates and improving the quality of transportation systems in smart cities.By offering predictive capabilities,the proposed model not only helps control accident rates but also prevents them in advance,ensuring road safety. 展开更多
关键词 Internet of Vehicles Collision avoidance Machine learning Traffic safety Autonomous vehicles Vehicular networks Vehicular heterogeneity Smart transportation Traffic modeling
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A Link Quality Indicator(LQI)Based Closed-Form Localization Framework for Vehicular Ad Hoc Networks(VANETs) 认领 引用
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作者 Waqas Ahmad Shahzad Anwar +7 位作者 Abid Iqbal Abuzar Khan Saad Arif Ali S.Alzahrani Mohammed Al-Naeem Fatimah Alhayan Syed Hashim Raza Bukhari Ghassan Husnain 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期1140-1176,共37页
Accurate vehicle localization is essential for safety-critical vehicular ad hoc networks(VANETs),including emergency response,navigation,traffic monitoring,and cooperative driving.However,conventional GPS/GNSS positio... Accurate vehicle localization is essential for safety-critical vehicular ad hoc networks(VANETs),including emergency response,navigation,traffic monitoring,and cooperative driving.However,conventional GPS/GNSS positioning systems have often shown degradation in tunnels,dense urban corridors,and non-line-of-sight(NLOS)environments,where satellite visibility and signal reliability are limited.This paper proposes a calibrated Link Quality Indicator(LQI)-based closed-form localization framework for partially connected Roadside Unit(RSU)-assisted VANETs.The proposed framework first calibrates the LQI-to-range relationship using numerical regression parameters and then converts accepted LQI observations into distance estimates.The distances are processed through a variance-aware weighted least squares(WLS)estimator,after which scalar consistency refinement is applied to improve the geometric consistency of the final position estimate.Partial connectivity is explicitly modeled using communication radius,LQI-threshold,and packet-reception constraints,rather than assuming that all vehicles and anchors are fully connected.The proposed method is evaluated against least squares(LS),WLS,RSSI-WLS,Extended Kalman Filter(EKF),Unscented Kalman Filter(UKF),Particle Filter(PF),cooperative localization,hybrid GNSS/INS/RSS fusion,and machine-learning-based localization baselines.The evaluation includes Monte Carlo simulations under shadowing,fading,packet loss,anchor-geometry,and NLOS conditions,together with confidence interval,statistical-significance,runtime,ablation,and sensitivity analyses.In addition,trace-driven validation is performed using the NGSIM US-101 real-world vehicle trajectory dataset.The NGSIM dataset provides real vehicle mobility traces,while LQI observations are generated using the calibrated LQI-distance model because the dataset does not contain physical LQI measurements.In the 100-vehicle case,throughput improves from 0.614 to 1.169 successful localizations/s compared with LS,corresponding to a 90.39%gain and from 0.591 to 1.169 successful localizations/s compared with WLS,corresponding to a 97.80%gain.In the NGSIM trace-driven experiment,the proposed method achieves RMSE values of 2.39,2.55,and 3.14 m for 10,50,and 100 vehicles,respectively.These results indicate that calibrated LQI-based localization provides a low-cost,infrastructure-compatible,and computationally efficient positioning framework for ITS and safety-critical VANET applications. 展开更多
关键词 Vehicle localization link quality indicator vehicular ad hoc networks intelligent transportation systems Cramer-Rao lower bound
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Neural Adaptive Sliding-Mode Control of Vehicular Cyber-Physical Systems With Uniformly Quantized Communication Data and Disturbances 认领 引用
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作者 Yuan Zhao Mengchao Li +3 位作者 Zhongchang Liu Lichuan Liu Shixi Wen Lei Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期149-160,共12页
This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the ad... This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy. 展开更多
关键词 Neural adaptive sliding-mode control quantized communication string stability vehicular cyber-physical systems(VCPSs)
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Energy-Efficient and Load-Balanced Edge-Driven Vehicular Network Using Intelligent Task Offloading 认领 引用
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作者 Salahuddin Khalid Haseeb +2 位作者 Mansoor Nasir NZ Jhanjhi Mamoona Humayun 《Computers, Materials & Continua》 SCIE EI 2026年第7期2032-2046,共15页
Intelligent Transportation System(ITS)interconnects smart technologies for the advancement in communication and autonomous decision making in vehicle interactions.It manages traffic control infrastructure,analyses roa... Intelligent Transportation System(ITS)interconnects smart technologies for the advancement in communication and autonomous decision making in vehicle interactions.It manages traffic control infrastructure,analyses road conditions,and supports cooperative awareness in a crucial environment.The sensors continuously collect real-time vehicle data,process it,and forward it to analysis servers to predict the behavior of Vehicular Ad hoc Networks(VANETs).Many approaches have been proposed to address research challenges in routing and improve communication for vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)systems.However,due to dynamic topology,the network becomes disturbed and loses established connections,leading to instability in data transmission against unpredictable behavior of the network.This research presents a framework,referred to as Energy Efficient Load Balanced Edge-Driven Vehicular Networks(EELB-EVN),that aims to attain load-balanced communication across vehicles and the interconnected infrastructure,thereby preventing congestion and reducing computational cost.In addition,an Intelligent offloading technique is developed using the Analytical Hierarchy Process(AHP)to reduce the additional overhead on the devices,thus enabling energy-aware and latency-sensitive vehicular communication.Furthermore,trustworthiness strategies are explored to enhance reliability and ensure the credibility of information.The simulation tests revealed the significance of the proposed framework compared to related schemes in terms of energy consumption and latency by 20%to 25%,task success rate and network throughput by 30%to 40%,and computational complexity by 33%across dynamic vehicular scenarios. 展开更多
关键词 Internet of Things vehicular networks edge computing trustworthiness energy efficiency
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Enhanced multi-agent deep reinforcement learning for efficient task offloading and resource allocation in vehicular networks 认领 引用
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作者 Long Xu Jiale Tan Hongcheng Zhuang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期66-75,共10页
In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging ... In response to the rising demand for low-latency,computation-intensive applications in vehicular networks,this paper proposes an adaptive task offloading approach for Vehicle-to-Everything(V2X)environments.Leveraging an enhanced Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm with an attention mechanism,the proposed approach optimizes computation offloading and resource allocation,aiming to minimize energy consumption and service delay.In this paper,vehicles dynamically offload computing-intensive tasks to both nearby vehicles through V2V links and roadside units through V2I links.The adaptive attention mechanism enables the system to prioritize relevant state information,leading to faster convergence.Simulations conducted in a realistic urban V2X scenario demonstrate that the proposed Attention-enhanced MADDPG(AT-MADDPG)algorithm significantly improves performance,achieving notable reductions in both energy consumption and latency compared to baseline algorithms,especially in high-demand,dynamic scenarios. 展开更多
关键词 Computation offloading Vehicular networks Deep reinforcement learning Adaptive offloading Spectrum and power allocation
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Graph-Based Constrained PPO for Low-Latency and Energy-Aware AI Agent Migration in Internet of Vehicular Agents 认领 引用
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作者 Kanyang Jiang Yingkai Kang Ming Li 《Computers, Materials & Continua》 SCIE EI 2026年第8期1472-1486,共15页
The Internet of Vehicular Agents(IoVA)interconnects distributed AI agents across vehicular networks to deliver real-time intelligent services for vehicular users.Due to the limited computing capacity of vehicles,AI ag... The Internet of Vehicular Agents(IoVA)interconnects distributed AI agents across vehicular networks to deliver real-time intelligent services for vehicular users.Due to the limited computing capacity of vehicles,AI agents are deployed on nearby RoadSide Units(RSUs)to perform computation-intensive inference.As vehicles traverse RSU coverage boundaries,AI agents must migrate to target RSUs to maintain service continuity.However,the communication and computing resources at each RSU are shared among multiple co-served vehicles,creating coupled allocation decisions that jointly determine system latency and energy consumption.To address this challenge,we propose a low-latency and energy-aware AI agent migration framework that models the end-to-end system latency and vehicle energy consumption in the IoVA.Since the cumulative nature of energy consumption introduces long-term constraints that cannot be handled by instantaneous optimization,we formulate the resource allocation problem as a constrained Markov decision process and develop a Graph-based Constrained Proximal Policy Optimization(GCPPO)algorithm to solve it.GCPPO employs a bidirectional graph attention network to extract the relational features between heterogeneous vehicles and RSUs,thereby enabling topology-aware resource allocation,and adopts a Lagrangian dual mechanism to adaptively enforce the long-term energy constraints.Simulation results demonstrate the effectiveness and scalability of the proposed algorithm,which achieves a 31.3%reduction in average system latency over baselines while attaining a 96.4%constraint satisfaction rate. 展开更多
关键词 Internet of vehicular agents AI agent migration constrained deep reinforcement learning graph attention network resource allocation
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Evolution of STBC-Based OFDM-IM for Wireless Vehicular Communication 认领 引用
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作者 Tanairat Mata Pisit Boonsrimuang 《China Communications》 SCIE EI CSCD 2026年第2期51-68,共18页
This paper studies wireless vehicular communication(VehCom)in intelligent transportation systems using an orthogonal frequency division multiplexing with index modulation(OFDM-IM).In the concept of IM,data is transmit... This paper studies wireless vehicular communication(VehCom)in intelligent transportation systems using an orthogonal frequency division multiplexing with index modulation(OFDM-IM).In the concept of IM,data is transmitted not only through the modulated symbols but also via the indices of the active subcarriers.In contrast to the original OFDM,OFDM-IM activates only non-zero subcarriers,increasing energy efficiency.However,the pilotassisted channel estimation(CE)method is a significant challenge in OFDM-IM,where the desired pilot subcarrier interval is related to the OFDM-IM subblock length.This paper proposes a walsh-scattered pilot-assisted CE for OFDM-IM VehCom.The optimum walsh-scattered pilot assignment is proposed to improve the transmission efficiency.Furthermore,a space-time block code with a high transmit diversity gain is employed for OFDM-IM VehCom to enhance VehCom's signal quality.The results show that the proposed method performs higher CE accuracy and better bit-error rate with significant spectral and energy efficiencies than conventional methods. 展开更多
关键词 index modulation intelligent transportation systems pilot-assisted channel estimation space-time block code wireless vehicular communication
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Key Agreement and Management Scheme Based on Blockchain for 5G-Enabled Vehicular Networks 认领 引用
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作者 Wang Zhihua Wang Shuaibo +4 位作者 Wang Haofan Li Jiaze Yao Yizhe Wang Yongjian Yang Xiaolong 《China Communications》 SCIE EI CSCD 2025年第3期270-287,共18页
5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large nu... 5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular networks.However,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular networks.Moreover,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable requirement.In order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on blockchain.Firstly,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication security.Secondly,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving vehicles.Finally,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular networks.Performance analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation. 展开更多
关键词 blockchain certificateless public key cryptography 5G vehicular networks key agreement key management
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Lightweight deep reinforcement learning for dynamic resource allocation in vehicular edge computing 认领 引用
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作者 Dapeng Wu Sijun Wu +4 位作者 Yaping Cui Ailing Zhong Tong Tang Ruyan Wang Xinqi Lin 《Digital Communications and Networks》 SCIE EI CSCD 2025年第5期1530-1542,共13页
Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for r... Vehicular Edge Computing(VEC)enhances the quality of user services by deploying wealth of resources near vehicles.However,due to highly dynamic and complex nature of vehicular networks,centralized decisionmaking for resource allocation proves inadequate within VECs.Conversely,allocating resources via distributed decision-making consumes vehicular resources.To improve the quality of user service,we formulate a problem of latency minimization,further subdividing this problem into two subproblems to be solved through distributed decision-making.To mitigate the resource consumption caused by distributed decision-making,we propose Reinforcement Learning(RL)algorithm based on sequential alternating multi-agent system mechanism,which effectively reduces the dimensionality of action space without losing the informational content of action,achieving network lightweighting.We discuss the rationality,generalizability,and inherent advantages of proposed mechanism.Simulation results indicate that our proposed mechanism outperforms traditional RL algorithms in terms of stability,generalizability,and adaptability to scenarios with invalid actions,all while achieving network lightweighting. 展开更多
关键词 Vehicular edge computing Resource allocation Reinforcement learning Lightweight
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Optimized Resource Allocation for Dual-Band Cooperation-Based Edge Computing Vehicular Network 认领 引用
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作者 Cheng Kaijun Fang Xuming 《China Communications》 SCIE EI CSCD 2025年第9期352-367,共16页
With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further impro... With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further improve network perfor-mance due to its limited spectrum resources.High-frequency(HF)band has plentiful spectrum resources which is adopted as one of the operating bands in 5G.To achieve low latency and sustainable development,a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side,or at macro-cell base station or at road side unit through LF or HF band to achieve sta-ble and high-speed task offloading.Moreover,a utility function including latency and energy consumption is minimized by optimizing computing and spectrum re-sources,transmission power and task scheduling.Ow-ing to its non-convexity,an iterative optimization algo-rithm is proposed to solve it.Numerical results eval-uate the performance and superiority of the scheme,proving that it can achieve efficient edge computing in vehicular networks. 展开更多
关键词 dual-band cooperation edge computing resource allocation task processing vehicular network
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Joint offloading decision and resource allocation in vehicular edge computing networks 认领 引用 被引量：2
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作者 Shumo Wang Xiaoqin Song +3 位作者 Han Xu Tiecheng Song Guowei Zhang Yang Yang 《Digital Communications and Networks》 SCIE EI CSCD 2025年第1期71-82,共12页
With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications a... With the rapid development of Intelligent Transportation Systems(ITS),many new applications for Intelligent Connected Vehicles(ICVs)have sprung up.In order to tackle the conflict between delay-sensitive applications and resource-constrained vehicles,computation offloading paradigm that transfers computation tasks from ICVs to edge computing nodes has received extensive attention.However,the dynamic network conditions caused by the mobility of vehicles and the unbalanced computing load of edge nodes make ITS face challenges.In this paper,we propose a heterogeneous Vehicular Edge Computing(VEC)architecture with Task Vehicles(TaVs),Service Vehicles(SeVs)and Roadside Units(RSUs),and propose a distributed algorithm,namely PG-MRL,which jointly optimizes offloading decision and resource allocation.In the first stage,the offloading decisions of TaVs are obtained through a potential game.In the second stage,a multi-agent Deep Deterministic Policy Gradient(DDPG),one of deep reinforcement learning algorithms,with centralized training and distributed execution is proposed to optimize the real-time transmission power and subchannel selection.The simulation results show that the proposed PG-MRL algorithm has significant improvements over baseline algorithms in terms of system delay. 展开更多
关键词 Computation offloading Resource allocation Vehicular edge computing Potential game Multi-agent deep deterministic policy gradient
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An Efficient Content Caching Strategy for Fog-Enabled Road Side Units in Vehicular Networks 认领 引用 被引量：1
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作者 Faareh Ahmed Babar Mansoor +1 位作者 Muhammad Awais Javed Abdul Khader Jilani Saudagar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期3783-3804,共22页
Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(... Vehicular networks enable seamless connectivity for exchanging emergency and infotainment content.However,retrieving infotainment data from remote servers often introduces high delays,degrading the Quality of Service(QoS).To overcome this,caching frequently requested content at fog-enabled Road Side Units(RSUs)reduces communication latency.Yet,the limited caching capacity of RSUs makes it impractical to store all contents with varying sizes and popularity.This research proposes an efficient content caching algorithm that adapts to dynamic vehicular demands on highways to maximize request satisfaction.The scheme is evaluated against Intelligent Content Caching(ICC)and Random Caching(RC).The obtained results show that our proposed scheme entertains more contentrequesting vehicles as compared to ICC and RC,with 33%and 41%more downloaded data in 28%and 35%less amount of time from ICC and RC schemes,respectively. 展开更多
关键词 Vehicular networks fog computing content caching infotainment services
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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction 认领 引用 被引量：1
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作者 Feng Zeng Zheng Zhang Jinsong Wu 《Digital Communications and Networks》 SCIE EI CSCD 2025年第2期537-546,共10页
In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements o... In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service delay.In this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling.Then,a Bi-LSTM-based model is proposed to predict the trajectories of vehicles.The service area is divided into several equal-sized grids.If the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory prediction.Moreover,we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction.Considering the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading.Simulation results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays. 展开更多
关键词 Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model
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AMulti-Objective Joint Task Offloading Scheme for Vehicular Edge Computing 认领 引用 被引量：1
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作者 Yiwei Zhang Xin Cui Qinghui Zhao 《Computers, Materials & Continua》 SCIE EI 2025年第8期2355-2373,共19页
The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial veh... The rapid advance of Connected-Automated Vehicles(CAVs)has led to the emergence of diverse delaysensitive and energy-constrained vehicular applications.Given the high dynamics of vehicular networks,unmanned aerial vehicles-assisted mobile edge computing(UAV-MEC)has gained attention in providing computing resources to vehicles and optimizing system costs.We model the computing offloading problem as a multi-objective optimization challenge aimed at minimizing both task processing delay and energy consumption.We propose a three-stage hybrid offloading scheme called Dynamic Vehicle Clustering Game-based Multi-objective Whale Optimization Algorithm(DVCG-MWOA)to address this problem.A novel dynamic clustering algorithm is designed based on vehiclemobility and task offloading efficiency requirements,where each UAV independently serves as the cluster head for a vehicle cluster and adjusts its position at the end of each timeslot in response to vehiclemovement.Within eachUAV-led cluster,cooperative game theory is applied to allocate computing resourceswhile respecting delay constraints,ensuring efficient resource utilization.To enhance offloading efficiency,we improve the multi-objective whale optimization algorithm(MOWOA),resulting in the MWOA.This enhanced algorithm determines the optimal allocation of pending tasks to different edge computing devices and the resource utilization ratio of each device,ultimately achieving a Pareto-optimal solution set for delay and energy consumption.Experimental results demonstrate that the proposed joint offloading scheme significantly reduces both delay and energy consumption compared to existing approaches,offering superior performance for vehicular networks. 展开更多
关键词 Vehicular edge computing cooperative game theory multi-objective optimization computation offloading
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A Task Offloading Method for Vehicular Edge Computing Based on Reputation Assessment 认领 引用
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作者 Jun Li Yawei Dong +2 位作者 Liang Ni Guopeng Feng Fangfang Shan 《Computers, Materials & Continua》 SCIE EI 2025年第5期3537-3552,共16页
With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)of... With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure,thereby reducing the computational burden on connected vehicles.However,this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes.Existing vehicular edge computing platforms have not adequately considered themisbehavior of vehicles.We propose a practical task offloading algorithm based on reputation assessment to address the task offloading problem in vehicular edge computing under an unreliable environment.This approach integrates deep reinforcement learning and reputation management to address task offloading challenges.Simulation experiments conducted using Veins demonstrate the feasibility and effectiveness of the proposed method. 展开更多
关键词 Vehicular edge computing task offloading reputation assessment
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Robust Control and Stabilization of Autonomous Vehicular Systems under Deception Attacks and Switching Signed Networks 认领 引用
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作者 Muflih Alhazmi Waqar Ul Hassan +5 位作者 Saba Shaheen Mohammed M.A.Almazah Azmat Ullah Khan Niazi Nafisa A.Albasheir Ameni Gargouri Naveed Iqbal 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期1903-1940,共38页
This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study inclu... This paper proposes a model-based control framework for vehicle platooning systems with secondorder nonlinear dynamics operating over switching signed networks,time-varying delays,and deception attacks.The study includes two configurations:a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus(FNTBC)and Fixed-Time Bipartite Consensus(FXTBC),and a leader—follower structure ensuring structural balance and robustness against deceptive signals.In the leaderless model,a bipartite controller based on impulsive control theory,gauge transformation,and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays.The FNTBC achieves finite-time convergence depending on initial conditions,while the FXTBC guarantees fixed-time convergence independent of them,providing adaptability to different operating states.In the leader—follower case,a discontinuous impulsive control law synchronizes all followers with the leader despite deceptive attacks and switching topologies,maintaining robust coordination through nonlinear corrective mechanisms.To validate the approach,simulations are conducted on systems of five and seventeen vehicles in both leaderless and leader—follower configurations.The results demonstrate that the proposed framework achieves rapid consensus,strong robustness,and high resistance to deception attacks,offering a secure and scalable model-based control solution for modern vehicular communication networks. 展开更多
关键词 Autonomous vehicles vehicle platooning stabilization decision and control systems switching signed networks leader–follower coordination gauge transformation Lyapunov stability deception and cybe-security attacks secure vehicular networks
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Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks 认领 引用 被引量：50
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作者 Xiaohua Ge Shunyuan Xiao +2 位作者 Qing-Long Han Xian-Ming Zhang Derui Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第1期31-46,共16页
This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is pr... This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks(VANETs)subject to finite communication resource.First,a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles in the simultaneous presence of unknown external disturbances and an unknown leader control input.Under such a platoon model,the central aim is to achieve robust platoon formation tracking with desired inter-vehicle spacing and same velocities and accelerations guided by the leader,while attaining improved communication efficiency.Toward this aim,a novel bandwidth-aware dynamic event-triggered scheduling mechanism is developed.One salient feature of the scheduling mechanism is that the threshold parameter in the triggering law is dynamically adjusted over time based on both vehicular state variations and bandwidth status.Then,a sufficient condition for platoon control system stability and performance analysis as well as a co-design criterion of the admissible event-triggered platooning control law and the desired scheduling mechanism are derived.Finally,simulation results are provided to substantiate the effectiveness and merits of the proposed co-design approach for guaranteeing a trade-off between robust platooning control performance and communication efficiency. 展开更多
关键词 Automated vehicles dynamic event-triggered communication information flow topology platooning control vehicular ad-hoc networks(VANETs)
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Trust Access Authentication in Vehicular Network Based on Blockchain 认领 引用 被引量：11
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作者 Shaoyong Guo Xing Hu +3 位作者 Ziqiang Zhou Xinyan Wang Feng Qi Lifang Gao 《China Communications》 SCIE CSCD 2019年第6期18-30,共13页
Data sharing and privacy securing present extensive opportunities and challenges in vehicular network.This paper introducestrust access authentication scheme’as a mechanism to achieve real-time monitoring and promote... Data sharing and privacy securing present extensive opportunities and challenges in vehicular network.This paper introducestrust access authentication scheme’as a mechanism to achieve real-time monitoring and promote collaborative sharing for vehicles.Blockchain,which can provide secure authentication and protected privacy,is a crucial technology.However,traditional cloud computing performs poorly in supplying low-latency and fast-response services for moving vehicles.In this situation,edge computing enabled Blockchain network appeals to be a promising method,where moving vehicles can access storage or computing resource and get authenticated from Blockchain edge nodes directly.In this paper,a hierarchical architecture is proposed consist of vehicular network layer,Blockchain edge layer and Blockchain network layer.Through a authentication mechanism adopting digital signature algorithm,it achieves trusted authentication and ensures valid verification.Moreover,a caching scheme based on many-to-many matching is proposed to minimize average delivery delay of vehicles.Simulation results prove that the proposed caching scheme has a better performance than existing schemes based on central-ized model or edge caching strategy in terms of hit ratio and average delay. 展开更多
关键词 blockchain vehicular network edge computing authentication mechanism many-to-many matching
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Joint Allocation of Wireless Resource and Computing Capability in MEC-Enabled Vehicular Network 认领 引用 被引量：13
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作者 Yanzhao Hou Chengrui Wang +3 位作者 Min Zhu Xiaodong Xu Xiaofeng Tao Xunchao Wu 《China Communications》 SCIE EI CSCD 2021年第6期64-76,共13页
In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as we... In MEC-enabled vehicular network with limited wireless resource and computation resource,stringent delay and high reliability requirements are challenging issues.In order to reduce the total delay in the network as well as ensure the reliability of Vehicular UE(VUE),a Joint Allocation of Wireless resource and MEC Computing resource(JAWC)algorithm is proposed.The JAWC algorithm includes two steps:V2X links clustering and MEC computation resource scheduling.In the V2X links clustering,a Spectral Radius based Interference Cancellation scheme(SR-IC)is proposed to obtain the optimal resource allocation matrix.By converting the calculation of SINR into the calculation of matrix maximum row sum,the accumulated interference of VUE can be constrained and the the SINR calculation complexity can be effectively reduced.In the MEC computation resource scheduling,by transforming the original optimization problem into a convex problem,the optimal task offloading proportion of VUE and MEC computation resource allocation can be obtained.The simulation further demonstrates that the JAWC algorithm can significantly reduce the total delay as well as ensure the communication reliability of VUE in the MEC-enabled vehicular network. 展开更多
关键词 vehicular network delay optimization wireless resource allocation matrix spectral radius MEC computation resource allocation
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V2X Offloading and Resource Allocation in SDN-Assisted MEC-Based Vehicular Networks 认领 引用 被引量：24
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作者 Haibo Zhang Zixin Wang Kaijian Liu 《China Communications》 SCIE CSCD 2020年第5期266-283,共18页
As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing(MEC) sinks cloud services to the ed... As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing(MEC) sinks cloud services to the edge of network, which reduces the delay jitter caused by remote cloud computing. Software-defined networking(SDN) is an emerging network paradigm with the features of logic centralized control and programmability. In this paper, we construct an SDN-assisted MEC network architecture for the vehicular network. By introducing SDN controller, the efficiency and flexibility of vehicular network are improved, and the network state can be perceived from the global perspective. To further reduce the system overhead, the problem of vehicle to everything(V2X) offloading and resource allocation is proposed, where the optimal offloading decision, transmission power control, subchannels assignment, and computing resource allocation scheme are given. The optimization problem is transformed into three stages because of the heterogeneity of the offloaded tasks and the NP-hard property of the problem. Firstly, the analytic hierarchy process is used to select initial offloading node, then stateless Q-learning is adopted to allocate transmission power, subchannels and computing resources. In addition, the offloading decision is modeled as a potential game, and the Nash equilibrium is proved by the potential function construction. Finally, the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others’ algorithms. 展开更多
关键词 vehicular network mobile edge computing software-defined networking resource allocation
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