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
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 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
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 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
<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I t...<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID. </div>展开更多
Employing cooperative communication in multihop wireless sensor networks provides the network with significant energy efficiency. However, the lifetime of such a network is directly dependant upon the lifetime of each...Employing cooperative communication in multihop wireless sensor networks provides the network with significant energy efficiency. However, the lifetime of such a network is directly dependant upon the lifetime of each of its individual sections (or clusters). Ignoring the fact that those sections close to sink have to forward more data (their own data plus the data received from the previous sections) and hence die sooner with considering equal section sizes, leads to a sub-optimal lifetime. In this paper, we optimize the section sizes of a multihop cooperative WSN so that it maximizes the network lifetime. Simulation results demonstrate a significant lifetime enhancement for the proposed optimal sectoring.展开更多
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 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 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.
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<div style="text-align:justify;"> Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID. </div>
文摘Employing cooperative communication in multihop wireless sensor networks provides the network with significant energy efficiency. However, the lifetime of such a network is directly dependant upon the lifetime of each of its individual sections (or clusters). Ignoring the fact that those sections close to sink have to forward more data (their own data plus the data received from the previous sections) and hence die sooner with considering equal section sizes, leads to a sub-optimal lifetime. In this paper, we optimize the section sizes of a multihop cooperative WSN so that it maximizes the network lifetime. Simulation results demonstrate a significant lifetime enhancement for the proposed optimal sectoring.
