With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from h...With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions.To address these challenges,this study presents an innovative framework that combines Graph Neural Networks(GNNs)with a Double Deep Q-Network(DDQN),utilizing dynamic graph structures and reinforcement learning.An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology,thereby improving decision accuracy and efficiency.Meanwhile,the framework models communication links as nodes and interference relationships as edges,effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information.Employing an aggregation mechanism based on the Graph Attention Network(GAT),it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance,ensuring more efficient and adaptive resource management.This design ensures reliable Vehicle-to-Vehicle(V2V)communication while maintaining high Vehicle-to-Infrastructure(V2I)throughput.The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment,allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions.Experimental results demonstrate that the proposed method significantly reduces computational overhead,mitigates latency,and improves resource utilization efficiency in vehicular networks under complex traffic scenarios.This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems,offering substantial theoretical significance and practical value.展开更多
Incorporating electric vehicles into smart grid,vehicle-to-Grid(V2G) makes it feasible to charge for large-scale electric vehicles,and in turn support electric vehicles,as mobile and distributed storage units,to disch...Incorporating electric vehicles into smart grid,vehicle-to-Grid(V2G) makes it feasible to charge for large-scale electric vehicles,and in turn support electric vehicles,as mobile and distributed storage units,to discharge to smart grid.In order to provide reliable and efficient services,the operator of V2 G networks needs to monitor realtime status of every plug-in electric vehicle(PEV) and then evaluate current electricity storage capability.Anonymity,aggregation and dynamic management are three basic but crucial characteristics of which the services of V2 G networks should be.However,few of existing authentication schemes for V2 G networks could satisfy them simultaneously.In this paper,we propose a secure and efficient authentication scheme with privacy-preserving for V2 G networks.The scheme makes the charging/discharging station authenticate PEVs anonymously and manage them dynamically.Moreover,the monitoring data collected by the charging/discharging station could be sent to a local aggregator(LAG)in batch mode.In particular,time overheads during verification stage are independent with the number of involved PEVs,and there is no need to update the membership certificate and key pair before PEV logs out.展开更多
The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This ...The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy.Fortunately,vehicular ad-hoc networks(VANET)offer an effective solution,where vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications are used to enhance location awareness.In V2I communications,the roadside units(RSU)transmit beacon packets,and the vehicle receives numerous packets from different RSUs to establish communication.To further improve localization accuracy,a cross-covariance matrices-alternating least square(CCM-ALS)algorithm is proposed.The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications.The algorithm is highly precise compared to traditional angle of arrival(AOA)positioning and not inferior to direct position determination(DPD)approaches while being low in complexity,which is crucial for moving vehicles.The numerical results verify the superiority of the proposed method.展开更多
With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a ...With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.展开更多
In highly dynamic and heterogeneous vehicular communication networks,it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safetyrelated applications.This paper in...In highly dynamic and heterogeneous vehicular communication networks,it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safetyrelated applications.This paper investigates machinelearning-assisted transmission design in a typical multi-user vehicle-to-vehicle(V2V)communication scenario.The transmission process proceeds sequentially along the discrete time steps,where several source nodes intend to deliver multiple different types of messages to their respective destinations within the same spectrum.Due to rapid movement of vehicles,real-time acquirement of channel knowledge and central coordination of all transmission actions are in general hard to realize.We consider applying multi-agent deep reinforcement learning(MADRL)to handle this issue.By transforming the transmission design problem into a stochastic game,a multi-agent proximal policy optimization(MAPPO)algorithm under a centralized training and decentralized execution framework is proposed such that each source decides its own transmission message type,power level,and data rate,based on local observations of the environment and feedback,to maximize its energy efficiency.Via simulations we show that our method achieves better performance over conventional methods.展开更多
As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicul...As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning(ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover management and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.展开更多
In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-con...In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.展开更多
Vehicular networks are expected to empower auto mated driving and intelligent transportation via vehicle-to-everything(V2X)communications and edge/cloud-assisted computation,and in the meantime Cellular V2X(C-V2X)is g...Vehicular networks are expected to empower auto mated driving and intelligent transportation via vehicle-to-everything(V2X)communications and edge/cloud-assisted computation,and in the meantime Cellular V2X(C-V2X)is gaining wide support from the global industrial ecosystem.The 5G NR-V2X technology is the evolution of LTE-V2X,which is expected to provide ultra-Reliable and Low-Latency Communications(uRLLC)with 1ms latency and 99.999%reliability.Nevertheless,vehicular networks still face great challenges in supporting many emerging time-critical applications,which comprise sensing,communication and computation as closed-loops.展开更多
Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management applications.For safety appl...Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management applications.For safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic map.BSMs are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc manner.However,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety applications.Moreover,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further aggravated.This paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node interferers.The proposed protocol works by dividing road segments into clusters and subclusters.Several sub-frames are allocated to a cluster and these sub-frames are reused after a certain distance.Within a cluster,sub-channels are allocated to sub-clusters.We implement the proposed SDMA protocol and evaluate its performance in a highway vehicular network.Simulation results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.展开更多
There is a significant increase in the rates of vehicle accidents in countries around the world and also the casualties involved ever year. New technologies have been explored relating to the Vehicular Ad Hoc Network ...There is a significant increase in the rates of vehicle accidents in countries around the world and also the casualties involved ever year. New technologies have been explored relating to the Vehicular Ad Hoc Network (VANET) due to the increase in vehicular traffic/congestions around us. Vehicular communication is very important as technology has evolved. The research of VANET and development of proposed systems and implementation would increase safety among road users and improve the comfort for the corresponding passengers, drivers and also other road users, and a great improvement in the traffic efficiency would be achieved. This research paper investigates the current and existing security issues associated with the VANET and exposes any slack amongst them in order to lighten possible problem domains in this field.展开更多
The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system usi...The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system using Intelligent Transportation System(ITS)along with the limitation of personal vehicles using sharing economy model.The SUT Smart Transit is utilized as a major public transportation system,while MoreSai@SUT(electric motorcycle services)is a minor public transportation system in this work.They are called Multi-Mode Transportation system as a combination.Moreover,a Vehicle toNetwork(V2N)is used for developing theMulti-Mode Transportation system in the campus.Due to equipping vehicles with On Board Unit(OBU)and 4G LTE modules,the real time speed and locations are transmitted to the cloud.The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival(ETA).In terms of vehicle classifications and counts,we deployed CCTV cameras,and the recorded videos are analyzed by using You Only Look Once(YOLO)algorithm.The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed.Contrary to the existing researches,the proposed system is implemented in the real environment.The final results unveil the attractiveness and satisfaction of users.Also,due to the proposed system,the CO_(2) gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.展开更多
This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context...This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context of the first traffic light in London in 1868 to the modern automated systems,the study explores the complexity and adaptability of traffic lights in Shanghai.Through field surveys and interviews with traffic engineers,the paper debunks common misconceptions about traffic light operation,revealing a sophisticated network that responds to real-time traffic dynamics using software like the Sydney Coordinated Adaptive Traffic System(SCATS)6.The study also discusses the importance of pedestrian safety,suggesting future enhancements such as Global Positioning System(GPS)based emergency systems and accommodations for color-blind individuals.The paper further delves into the potential of Artificial Intelligence(AI)and Vehicle-to-Infrastructure(V21)technology in revolutionizing traffic light systems,emphasizing their role in improving traffic flow and safety.The findings underscore Shanghai’s progressive approach to traffic management,showcasing the city’s commitment to optimizing traffic control solutions for the benefit of both vehicles and pedestrians.展开更多
In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image gener...In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image generation,such as the loss of important information features during the encoding and decoding processes,as well as a lack of constraints during the training process.To address these issues and improve the quality of Pix2Pixgenerated images,this paper introduces two key enhancements.Firstly,to reduce information loss during encoding and decoding,we utilize the U-Net++network as the generator for the Pix2Pix model,incorporating denser skip-connection to minimize information loss.Secondly,to enhance constraints during image generation,we introduce a specialized discriminator designed to distinguish differential images,further enhancing the quality of the generated images.We conducted experiments on the facades dataset and the sketch portrait dataset from the Chinese University of Hong Kong to validate our proposed model.The experimental results demonstrate that our improved Pix2Pix model significantly enhances image quality and outperforms other models in the selected metrics.Notably,the Pix2Pix model incorporating the differential image discriminator exhibits the most substantial improvements across all metrics.An analysis of the experimental results reveals that the use of the U-Net++generator effectively reduces information feature loss,while the Pix2Pix model incorporating the differential image discriminator enhances the supervision of the generator during training.Both of these enhancements collectively improve the quality of Pix2Pix-generated images.展开更多
Logical behavioral arrangements are a class of conventional arrangements to illustrate the happening of incidents in an appropriate and structured approach in vehicular ad hoc network （VANET）. These incidents are ch...Logical behavioral arrangements are a class of conventional arrangements to illustrate the happening of incidents in an appropriate and structured approach in vehicular ad hoc network （VANET）. These incidents are characterized as a list of path segments that are passed through by the vehicles for the duration of their journeys from a pre-decided local source to a local destination in a structured manner. A set of proper description illustrating the paths traversed by the vehicles as logical behavioral arrangements is describedin this paper. The data gathering scheme based on secure authentication to gather the data from the vehicles is proposed in this paper. This proposed data gathering scheme based on secure authentication is compared with the existing data gathering schemes by using veins framework and the results of analysis reflect that the proposed scheme outperforms among others. The data collected from the vehicles by the proposed data gathering scheme is stored at distributed road side units （RSUs）. From these collected paths, the common and frequent paths opted by the vehicles in a certain region are determined by using frequent arrangement mining approach. An estimation model is used to decidethe next path and the whole path map opted by the vehicles in unusual situations like accident, jams, or a particular time of day. The proposed scheme will helpthe society in reducing the waiting time in vent of emergency or normal working days.展开更多
Recently,big data becomes evitable due to massive increase in the generation of data in real time application.Presently,object detection and tracking applications becomes popular among research communities and finds u...Recently,big data becomes evitable due to massive increase in the generation of data in real time application.Presently,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,etc.This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data environment.The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT model.The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast R-CNN.In addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature map.Lastly,softmax layer is applied to perform classification task.The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.展开更多
Vehicle-to-Everything(V2X)communication is expected to accomplish a long-standing goal of the Connected and Autonomous Vehicle(CAV)community to bring connected vehicles to roads on a large scale.A major challenge,and ...Vehicle-to-Everything(V2X)communication is expected to accomplish a long-standing goal of the Connected and Autonomous Vehicle(CAV)community to bring connected vehicles to roads on a large scale.A major challenge,and perhaps the biggest hurdle on the path towards this goal,is the scalability issues associated with it,especially when vehicular safety is concerned.As a major stakeholder,Cellular V2X(C-V2X)community,which is based on the 3rd Generation Partnership Project(3GPP),has long been trying to research on whether vehicular networks are able to support the safety-critical applications in high-density vehicular scenarios.This paper attempts to answer this question by first presenting an overview on the scalability challenges faced by 3GPP Release 14 Long Term Evolution C-V2X(LTE-V2X)using the PC5 sidelink interface for low and heavy-density traffic scenarios.Next,it demonstrates a series of solutions that address network congestion,packet losses,and other scalability issues associated with LTE-V2X to enable this communication technology for commercial deployment.In addition,a brief survey is provided into 3GPP Release 165G New Radio V2X(NR-V2X)that utilizes the NR sidelink interface and works as an evolution of C-V2X towards better performance for V2X communications,including new enhanced V2X(eV2X)scenarios that possess ultra-low-latency and high-reliability requirements.展开更多
基金Project ZR2023MF111 supported by Shandong Provincial Natural Science Foundation。
文摘With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions.To address these challenges,this study presents an innovative framework that combines Graph Neural Networks(GNNs)with a Double Deep Q-Network(DDQN),utilizing dynamic graph structures and reinforcement learning.An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology,thereby improving decision accuracy and efficiency.Meanwhile,the framework models communication links as nodes and interference relationships as edges,effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information.Employing an aggregation mechanism based on the Graph Attention Network(GAT),it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance,ensuring more efficient and adaptive resource management.This design ensures reliable Vehicle-to-Vehicle(V2V)communication while maintaining high Vehicle-to-Infrastructure(V2I)throughput.The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment,allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions.Experimental results demonstrate that the proposed method significantly reduces computational overhead,mitigates latency,and improves resource utilization efficiency in vehicular networks under complex traffic scenarios.This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems,offering substantial theoretical significance and practical value.
基金the Natural Science Foundation of China(61102056,61201132)Fundamental Research Funds for the Central Universities of China(K5051301013)the 111 Project of China(B08038)
文摘Incorporating electric vehicles into smart grid,vehicle-to-Grid(V2G) makes it feasible to charge for large-scale electric vehicles,and in turn support electric vehicles,as mobile and distributed storage units,to discharge to smart grid.In order to provide reliable and efficient services,the operator of V2 G networks needs to monitor realtime status of every plug-in electric vehicle(PEV) and then evaluate current electricity storage capability.Anonymity,aggregation and dynamic management are three basic but crucial characteristics of which the services of V2 G networks should be.However,few of existing authentication schemes for V2 G networks could satisfy them simultaneously.In this paper,we propose a secure and efficient authentication scheme with privacy-preserving for V2 G networks.The scheme makes the charging/discharging station authenticate PEVs anonymously and manage them dynamically.Moreover,the monitoring data collected by the charging/discharging station could be sent to a local aggregator(LAG)in batch mode.In particular,time overheads during verification stage are independent with the number of involved PEVs,and there is no need to update the membership certificate and key pair before PEV logs out.
基金supported by the National Natural Science Foundation of China(62371225).
文摘The Global Position System(GPS)is a reliable method for positioning in most scenarios,but it falls short in harsh environments like urban vehicular scenarios,where numerous trees or flyovers obstruct the signals.This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy.Fortunately,vehicular ad-hoc networks(VANET)offer an effective solution,where vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications are used to enhance location awareness.In V2I communications,the roadside units(RSU)transmit beacon packets,and the vehicle receives numerous packets from different RSUs to establish communication.To further improve localization accuracy,a cross-covariance matrices-alternating least square(CCM-ALS)algorithm is proposed.The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications.The algorithm is highly precise compared to traditional angle of arrival(AOA)positioning and not inferior to direct position determination(DPD)approaches while being low in complexity,which is crucial for moving vehicles.The numerical results verify the superiority of the proposed method.
基金supported in part by the National Natural Science Foundation of China under Grant 62071283,Grant 61771296,Grant 61872228 and Grant 62271513in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2018JQ6048 and Grant 2018JZ6006+3 种基金in part by Shaanxi Key Industrial Innovation Chain Project in Industrial Domain under Grant 2020ZDLGY15-09in part by Guang Dong Basic and Applied Basic Research Foundation under Grant 2021A1515012631in part by China Postdoctoral Science Foundation under Grant 2016M600761in part by the Fundamental Research Funds for the Central Universities under Grant GK202003075 and Grant GK202103016。
文摘With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.
基金supported in part by the National Natural Science Foundation of China(62171322,62006173)the 2021-2023 China-Serbia Inter-Governmental S&T Cooperation Project(No.6)+1 种基金support of the Sino-German Center of Intelligent Systems,Tongji University。
文摘In highly dynamic and heterogeneous vehicular communication networks,it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safetyrelated applications.This paper investigates machinelearning-assisted transmission design in a typical multi-user vehicle-to-vehicle(V2V)communication scenario.The transmission process proceeds sequentially along the discrete time steps,where several source nodes intend to deliver multiple different types of messages to their respective destinations within the same spectrum.Due to rapid movement of vehicles,real-time acquirement of channel knowledge and central coordination of all transmission actions are in general hard to realize.We consider applying multi-agent deep reinforcement learning(MADRL)to handle this issue.By transforming the transmission design problem into a stochastic game,a multi-agent proximal policy optimization(MAPPO)algorithm under a centralized training and decentralized execution framework is proposed such that each source decides its own transmission message type,power level,and data rate,based on local observations of the environment and feedback,to maximize its energy efficiency.Via simulations we show that our method achieves better performance over conventional methods.
基金supported by U.K.EPSRC(EP/S02476X/1)"Resource Orchestration for Diverse Radio Systems(REORDER)".
文摘As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning(ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover management and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.
基金supported by the West Light Foundation of the Chinese Academy of Sciences(2019-XBQNXZ-A-007)the National Natural Science Foundation of China(12071458,71731009).
文摘In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.
文摘Vehicular networks are expected to empower auto mated driving and intelligent transportation via vehicle-to-everything(V2X)communications and edge/cloud-assisted computation,and in the meantime Cellular V2X(C-V2X)is gaining wide support from the global industrial ecosystem.The 5G NR-V2X technology is the evolution of LTE-V2X,which is expected to provide ultra-Reliable and Low-Latency Communications(uRLLC)with 1ms latency and 99.999%reliability.Nevertheless,vehicular networks still face great challenges in supporting many emerging time-critical applications,which comprise sensing,communication and computation as closed-loops.
文摘Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management applications.For safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic map.BSMs are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc manner.However,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety applications.Moreover,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further aggravated.This paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node interferers.The proposed protocol works by dividing road segments into clusters and subclusters.Several sub-frames are allocated to a cluster and these sub-frames are reused after a certain distance.Within a cluster,sub-channels are allocated to sub-clusters.We implement the proposed SDMA protocol and evaluate its performance in a highway vehicular network.Simulation results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.
文摘There is a significant increase in the rates of vehicle accidents in countries around the world and also the casualties involved ever year. New technologies have been explored relating to the Vehicular Ad Hoc Network (VANET) due to the increase in vehicular traffic/congestions around us. Vehicular communication is very important as technology has evolved. The research of VANET and development of proposed systems and implementation would increase safety among road users and improve the comfort for the corresponding passengers, drivers and also other road users, and a great improvement in the traffic efficiency would be achieved. This research paper investigates the current and existing security issues associated with the VANET and exposes any slack amongst them in order to lighten possible problem domains in this field.
基金This work was supported by Suranaree University of Technology(SUT).The authors would also like to thank SUT Smart Transit and Thai AI for supporting the experimental and datasets.
文摘The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system using Intelligent Transportation System(ITS)along with the limitation of personal vehicles using sharing economy model.The SUT Smart Transit is utilized as a major public transportation system,while MoreSai@SUT(electric motorcycle services)is a minor public transportation system in this work.They are called Multi-Mode Transportation system as a combination.Moreover,a Vehicle toNetwork(V2N)is used for developing theMulti-Mode Transportation system in the campus.Due to equipping vehicles with On Board Unit(OBU)and 4G LTE modules,the real time speed and locations are transmitted to the cloud.The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival(ETA).In terms of vehicle classifications and counts,we deployed CCTV cameras,and the recorded videos are analyzed by using You Only Look Once(YOLO)algorithm.The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed.Contrary to the existing researches,the proposed system is implemented in the real environment.The final results unveil the attractiveness and satisfaction of users.Also,due to the proposed system,the CO_(2) gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.
文摘This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context of the first traffic light in London in 1868 to the modern automated systems,the study explores the complexity and adaptability of traffic lights in Shanghai.Through field surveys and interviews with traffic engineers,the paper debunks common misconceptions about traffic light operation,revealing a sophisticated network that responds to real-time traffic dynamics using software like the Sydney Coordinated Adaptive Traffic System(SCATS)6.The study also discusses the importance of pedestrian safety,suggesting future enhancements such as Global Positioning System(GPS)based emergency systems and accommodations for color-blind individuals.The paper further delves into the potential of Artificial Intelligence(AI)and Vehicle-to-Infrastructure(V21)technology in revolutionizing traffic light systems,emphasizing their role in improving traffic flow and safety.The findings underscore Shanghai’s progressive approach to traffic management,showcasing the city’s commitment to optimizing traffic control solutions for the benefit of both vehicles and pedestrians.
基金supported in part by the Xinjiang Natural Science Foundation of China(2021D01C078).
文摘In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image generation,such as the loss of important information features during the encoding and decoding processes,as well as a lack of constraints during the training process.To address these issues and improve the quality of Pix2Pixgenerated images,this paper introduces two key enhancements.Firstly,to reduce information loss during encoding and decoding,we utilize the U-Net++network as the generator for the Pix2Pix model,incorporating denser skip-connection to minimize information loss.Secondly,to enhance constraints during image generation,we introduce a specialized discriminator designed to distinguish differential images,further enhancing the quality of the generated images.We conducted experiments on the facades dataset and the sketch portrait dataset from the Chinese University of Hong Kong to validate our proposed model.The experimental results demonstrate that our improved Pix2Pix model significantly enhances image quality and outperforms other models in the selected metrics.Notably,the Pix2Pix model incorporating the differential image discriminator exhibits the most substantial improvements across all metrics.An analysis of the experimental results reveals that the use of the U-Net++generator effectively reduces information feature loss,while the Pix2Pix model incorporating the differential image discriminator enhances the supervision of the generator during training.Both of these enhancements collectively improve the quality of Pix2Pix-generated images.
文摘Logical behavioral arrangements are a class of conventional arrangements to illustrate the happening of incidents in an appropriate and structured approach in vehicular ad hoc network （VANET）. These incidents are characterized as a list of path segments that are passed through by the vehicles for the duration of their journeys from a pre-decided local source to a local destination in a structured manner. A set of proper description illustrating the paths traversed by the vehicles as logical behavioral arrangements is describedin this paper. The data gathering scheme based on secure authentication to gather the data from the vehicles is proposed in this paper. This proposed data gathering scheme based on secure authentication is compared with the existing data gathering schemes by using veins framework and the results of analysis reflect that the proposed scheme outperforms among others. The data collected from the vehicles by the proposed data gathering scheme is stored at distributed road side units （RSUs）. From these collected paths, the common and frequent paths opted by the vehicles in a certain region are determined by using frequent arrangement mining approach. An estimation model is used to decidethe next path and the whole path map opted by the vehicles in unusual situations like accident, jams, or a particular time of day. The proposed scheme will helpthe society in reducing the waiting time in vent of emergency or normal working days.
文摘Recently,big data becomes evitable due to massive increase in the generation of data in real time application.Presently,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,etc.This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data environment.The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT model.The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast R-CNN.In addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature map.Lastly,softmax layer is applied to perform classification task.The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.
文摘Vehicle-to-Everything(V2X)communication is expected to accomplish a long-standing goal of the Connected and Autonomous Vehicle(CAV)community to bring connected vehicles to roads on a large scale.A major challenge,and perhaps the biggest hurdle on the path towards this goal,is the scalability issues associated with it,especially when vehicular safety is concerned.As a major stakeholder,Cellular V2X(C-V2X)community,which is based on the 3rd Generation Partnership Project(3GPP),has long been trying to research on whether vehicular networks are able to support the safety-critical applications in high-density vehicular scenarios.This paper attempts to answer this question by first presenting an overview on the scalability challenges faced by 3GPP Release 14 Long Term Evolution C-V2X(LTE-V2X)using the PC5 sidelink interface for low and heavy-density traffic scenarios.Next,it demonstrates a series of solutions that address network congestion,packet losses,and other scalability issues associated with LTE-V2X to enable this communication technology for commercial deployment.In addition,a brief survey is provided into 3GPP Release 165G New Radio V2X(NR-V2X)that utilizes the NR sidelink interface and works as an evolution of C-V2X towards better performance for V2X communications,including new enhanced V2X(eV2X)scenarios that possess ultra-low-latency and high-reliability requirements.
