Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic ...Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic environment.The specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network coverage.Nowa-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network time.The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration failure.As the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital role.To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC.The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.展开更多
In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many ...In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.展开更多
Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices w...Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.展开更多
Vehicular networks have been envisioned to provide us with numerous interesting services such as dissemination of real-time safety warnings and commercial advertisements via car-to-car communication. However, efficien...Vehicular networks have been envisioned to provide us with numerous interesting services such as dissemination of real-time safety warnings and commercial advertisements via car-to-car communication. However, efficient routing is a research challenge due to the highly dynamic nature of these networks. Nevertheless, the availability of connections imposes additional constraint. Our earlier works in the area of efficient dissemination integrates the advantages of middleware operations with muhicast routing to de- sign a framework for distributed routing in vehicular networks. Cloud computing makes use of pools of physical computing resourc- es to meet the requirements of such highly dynamic networks. The proposed solution in this paper applies the principles of cloud computing to our existing framework. The routing protocol works at the network layer for the formation of clouds in specific geo- graphic regions. Simulation results present the effieiency of the model in terms of serviee discovery, download time and the queu- ing delay at the controller nodes.展开更多
车载激光扫描(light detection and ranging,LiDAR)技术因其可快速高效获取道路及两侧的地理信息,在智慧道路中有很大的应用前景,其所采集的LiDAR点云,实现了“所见即所得”,但其数据量大和噪点多等特点导致道路中线提取相关方法还不够...车载激光扫描(light detection and ranging,LiDAR)技术因其可快速高效获取道路及两侧的地理信息,在智慧道路中有很大的应用前景,其所采集的LiDAR点云,实现了“所见即所得”,但其数据量大和噪点多等特点导致道路中线提取相关方法还不够成熟。提出了一种面向高速立交桥中线提取的点云数据处理方法:采用多重滤波算法(梯度滤波、高斯滤波和双边滤波)过滤非路面点云,应用Alpha shapes算法识别道路边界,最终,引入B样条曲线拟合算法拟合道路中线。通过实例高速立交桥车载LiDAR数据验证了所提出方法的有效性和可行性。相关研究成果可服务于智慧道路改扩建和维养等领域。展开更多
随着道路拥堵等交通问题的日益严峻,智能交通系统成为解决当前交通问题的有效途径。车载自组织网络及车辆作为智能交通的重要组成部分也成为了当前的研究热点。首先介绍了车联网在国内外的发展历史和现状,然后分别从端系统、管系统、云...随着道路拥堵等交通问题的日益严峻,智能交通系统成为解决当前交通问题的有效途径。车载自组织网络及车辆作为智能交通的重要组成部分也成为了当前的研究热点。首先介绍了车联网在国内外的发展历史和现状,然后分别从端系统、管系统、云系统3个方面对车联网进行了分析研究,着重介绍了管系统中的V2V(vehicle to vehicle)和V2R(vehicle to roadside)两种通信技术。此外,对于面向车联网的交通云和大数据技术进行了概括介绍。最后,探讨了车载自组织网络的应用场景和未来发展趋势。展开更多
传统的基于专用短程通信(dedicated short range communication,DSRC)的车载网络(vehicular ad hoc network,VANET)通信架构难以满足车联网数据传输的服务质量(quality of service,QoS)需求,通过移动网关将数据上传至服务器,由服务器决...传统的基于专用短程通信(dedicated short range communication,DSRC)的车载网络(vehicular ad hoc network,VANET)通信架构难以满足车联网数据传输的服务质量(quality of service,QoS)需求,通过移动网关将数据上传至服务器,由服务器决策传输给目标车辆,可以扩大数据广播域,极大减少数据远程传输时延.结合移动云服务的思想,提出了一种新的车联网架构和数据传输方法.首先给出了网关服务者(gateway server,GWS)向云端注册服务信息的具体流程;其次提出了一种云端服务网关选取方法,该方法结合云端的历史数据和实时数据,动态决定参与服务的网关服务者及其服务范围,网关消费者(gateway consumer,GWC)在获取服务广播消息后,综合考虑通信负载、链路稳定度、信道质量等性能参数来选出最优的网关服务者,并将数据传输给网关服务者,再由其上传到云端;最后在OMNeT++实验环境下,针对不同的交通场景,对该方法传输性能进行了评估.结果表明该方法获得较低传输延迟的同时,能够保证较高的传输成功率,理论分析也证明了该方法的有效性.展开更多
文摘Nomadic Vehicular Cloud(NVC)is envisaged in this work.The predo-minant aspects of NVC is,it moves along with the vehicle that initiates it and functions only with the resources of moving vehicles on the heavy traffic road without relying on any of the static infrastructure and NVC decides the initiation time of container migration using cell transmission model(CTM).Containers are used in the place of Virtual Machines(VM),as containers’features are very apt to NVC’s dynamic environment.The specifications of 5G NR V2X PC5 interface are applied to NVC,for the feature of not relying on the network coverage.Nowa-days,the peak traffic on the road and the bottlenecks due to it are inevitable,which are seen here as the benefits for VC in terms of resource availability and residual in-network time.The speed range of high-end vehicles poses the issue of dis-connectivity among VC participants,that results the container migration failure.As the entire VC participants are on the move,to maintain proximity of the containers hosted by them,estimating their movements plays a vital role.To infer the vehicle movements on the road stretch and initiate the container migration prior enough to avoid the migration failure due to vehicles dynamicity,this paper proposes to apply the CTM to the container based and 5G NR V2X enabled NVC.The simulation results show that there is a significant increase in the success rate of vehicular cloud in terms of successful container migrations.
文摘In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.
基金supported in part by the National Science Foundation of China under Grant 61272397,Grant 61572538,Grant 61174152,Grant 61331008in part by the Guangdong Natural Science Funds for Distinguished Young Scholar under Grant S20120011187
文摘Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.
文摘Vehicular networks have been envisioned to provide us with numerous interesting services such as dissemination of real-time safety warnings and commercial advertisements via car-to-car communication. However, efficient routing is a research challenge due to the highly dynamic nature of these networks. Nevertheless, the availability of connections imposes additional constraint. Our earlier works in the area of efficient dissemination integrates the advantages of middleware operations with muhicast routing to de- sign a framework for distributed routing in vehicular networks. Cloud computing makes use of pools of physical computing resourc- es to meet the requirements of such highly dynamic networks. The proposed solution in this paper applies the principles of cloud computing to our existing framework. The routing protocol works at the network layer for the formation of clouds in specific geo- graphic regions. Simulation results present the effieiency of the model in terms of serviee discovery, download time and the queu- ing delay at the controller nodes.
文摘车载激光扫描(light detection and ranging,LiDAR)技术因其可快速高效获取道路及两侧的地理信息,在智慧道路中有很大的应用前景,其所采集的LiDAR点云,实现了“所见即所得”,但其数据量大和噪点多等特点导致道路中线提取相关方法还不够成熟。提出了一种面向高速立交桥中线提取的点云数据处理方法:采用多重滤波算法(梯度滤波、高斯滤波和双边滤波)过滤非路面点云,应用Alpha shapes算法识别道路边界,最终,引入B样条曲线拟合算法拟合道路中线。通过实例高速立交桥车载LiDAR数据验证了所提出方法的有效性和可行性。相关研究成果可服务于智慧道路改扩建和维养等领域。
文摘随着道路拥堵等交通问题的日益严峻,智能交通系统成为解决当前交通问题的有效途径。车载自组织网络及车辆作为智能交通的重要组成部分也成为了当前的研究热点。首先介绍了车联网在国内外的发展历史和现状,然后分别从端系统、管系统、云系统3个方面对车联网进行了分析研究,着重介绍了管系统中的V2V(vehicle to vehicle)和V2R(vehicle to roadside)两种通信技术。此外,对于面向车联网的交通云和大数据技术进行了概括介绍。最后,探讨了车载自组织网络的应用场景和未来发展趋势。
文摘传统的基于专用短程通信(dedicated short range communication,DSRC)的车载网络(vehicular ad hoc network,VANET)通信架构难以满足车联网数据传输的服务质量(quality of service,QoS)需求,通过移动网关将数据上传至服务器,由服务器决策传输给目标车辆,可以扩大数据广播域,极大减少数据远程传输时延.结合移动云服务的思想,提出了一种新的车联网架构和数据传输方法.首先给出了网关服务者(gateway server,GWS)向云端注册服务信息的具体流程;其次提出了一种云端服务网关选取方法,该方法结合云端的历史数据和实时数据,动态决定参与服务的网关服务者及其服务范围,网关消费者(gateway consumer,GWC)在获取服务广播消息后,综合考虑通信负载、链路稳定度、信道质量等性能参数来选出最优的网关服务者,并将数据传输给网关服务者,再由其上传到云端;最后在OMNeT++实验环境下,针对不同的交通场景,对该方法传输性能进行了评估.结果表明该方法获得较低传输延迟的同时,能够保证较高的传输成功率,理论分析也证明了该方法的有效性.
